Are you seeing this strength slide. Yes, there now thank you so much. We had a really, really good set of discussions around infrastructure. First thing I want to thank the bro that broke that the project folks who did a great overview of the infrastructure because we know that can take a really long time. There's been great progress and additional comments on that is also the data growth is impressive. And the way anvil enhances usage by reducing time to get access to data. I think Vivian is one of made the point that if you know if if a researcher wanted to go out and get access to a whole bunch of these data sets, individually, you know on on original studies it would take them a really long time so there's a lot of enhancement from that. The reduce reduction of any costs for downloading data from anvil. Essentially it says egress is free. It's a great benefit to the research community. Duos the pilot the duo's pilot could be a potential game changer for dealing with access controls over who should have access to what for what use the use of API's and the GA for DH standards. Is really good as they help with interoperability. Also that this project the system support both bioinformatics and non informatics users. So it would appeal to a large user community. Taking advantage of the infrastructure of the existing cloud vendors is a really positive thing. And we talked quite a lot about security at different levels, and there's clearly been a lot of attention paid to security security of the access security the system security appropriate security the data. Participation in NC PI to be part of you know larger interoperability focus is very good discussion about fire allows usability ability to work with other projects activities. And the some built in machine learning models including the ability to have models available be reused and have outputs from those models. We can decide we didn't, we didn't have quite as long a list. There was a lot of discussion about clinical research needs incorporate those needs and expertise across the infrastructure data exchange, you know understanding the use cases and security and trust by clinical and user. The portal, while it looks, it looks good as we heard it was redesigned, it could get really busy, you know the number of as you add components, maybe the possibility of doing some user interface user experience analysis and then related improvements to make it easier to use. There was questions brought up about search what kinds of search needed to be fast needed to be accurate to keep up with the expansion of the resource. And more clarity on users outside users and the provenance of data workflows components pieces, you know, in terms of trusting the system trusting what you would what kind of results you might get from it. More resources again toward end user support comments about people end users with less experience with using cloud or needing support to onboard to cloud. And then, you know, any concerns about when the when you have data egress. What does that mean for security of that data and security privacy, especially for any data that allows for identification. Onto the next one. All right, this is the opportunities. Really there was a lot of discussion about opportunities which just demonstrate that many people see great potential here. And that's just a credit to all the work the team has done. Duos again came up as an opportunity, improve it so it goes beyond the DAX there was questions also about duos and silos, is it NIH wide that do us is an interest in do us or not. To advance the opportunity to work with groups like Vulcan to stay in touch with the fire, the evolution of the fire standards, and to work on the clinical use cases. There was a comment it was really good comment about trusting of a resource and the perception of a resource being trusted, whether or not. What about whether you have the security it's a perception of can you trust it to cover all the things you need so that you can trust that you're using the data and the results you get. You can count on those so this was a interesting comment about a perception, you know, like perception of hospital systems and whether or not those are trusted. We're leveraging strides and specifically the professional services portion of it, as well as cloud credits to enable access to a broader research community plan to use GP GP use or other cost controls, especially as the data grows and the analysis is more complex, you know, be be prepared that you could use a lot of resources without realizing it, and therefore incur a lot of costs. Align with GCP and other sources to, again ease use for participants with or without certain experience in the environment they may be joining provide more transparency and analysis and linkage with publications. Conversation was was really interesting about ways to also support reproducibility and other important items. Oh yeah connection with participants and ability to incorporate the preferences that they state when they make when they do consent for their data to be used. Enhanced interoperability look at impact of federated data systems as opposed to the I mean there's been a lot of data pulled in but what you're not going to necessarily be able to pull in every single very large data set. Oops. Oh, I'm sorry. I'm so sorry. My notes there. No, that was my problem. My fault. And then there was also a really interesting discussion about, you know, hospital systems connectivity, maybe having some form of anvil that hospital could link up with their system so you get that clinical interface without that community being concerned that their data is leaving their boundaries. I called it anvil box this was really I think it was Sandy Aronson I'm not sure that's exactly what Sandy meant. But anyway it's on site. One more. So go ahead Sandy if you want to make a call I just said I think it's a good description. Thank you. One of the interesting things is potential bias and models. We do, you know, we've, and we've heard about the need for diversity, we do know that whatever data used to train a model if it's not diverse it is going to like we have bias in it. There's a reliance on commercial cloud providers here. And I know that for anybody who's interested Anthony put in the notes that there's a plan to move to Azure in 2022 potential siloing if duos is not adopted. You know, NIH wide security threats, not because security hasn't been dealt with well but because security is an ever evolving area, and it is always a threat to any kind of an online resource reliance on rass and other identity servers services. So we probably could have put some of the rest stuff in a strengths area because it's come along, you know, in the past two three years potential cost issues again how do we control costs and people can still do the work they need to do and there's no surprises. And then finally, something that comes up on any kind of funded project. If it is something that has value to go beyond any type of funding cycle, how does one sustain it. Would we outgrow this the model we're using now. I don't know if there's time, but if, if there's anybody who is in the infrastructure group and like to add a note or two. Maybe we have time for that. I do have time. One or two comments. I'm not seeing anybody I'm not hearing anybody. I don't know. You did a great job. I had a great group so all of you really appreciate all the participation. So it's time to move on to see. See that you're online. Yeah, that's my microphone. Okay. Good. Great. Thanks for putting up the slides Chris. So our group was the outreach and training group. It was really good to see that we had a very large diversity of school sizes going from very small to very large. It was really good to see. I made everybody give me their favorite musical groups as their interest from the discussion and so much really good musical diversity and good taste in this group so I'm excited. So for our strengths. I think that there is a low activation energy for for this I think we're in a time now where this discussion could not have been had even 10 years ago, even at a Hopkins of, you know, well, cloud versus, you know, hardware on site infrastructure. So at that point in, in, in just technology, where you know that can become a strength, especially for school that doesn't have this and doesn't, you know, have a huge IT department to build infrastructure so that activation energy was was a definite strength and a lower barrier for, again, getting this geographic diversity so that these resources are not focused on the coasts and the major, you know, a large institutions, but, you know, everywhere in between, possibly deep pilots and other forms of direct user engagement that was seen as a strength for what was discussed before on the research projects coming up and notably the credits for the new users we spent a lot of time talking about the, the finance as a huge barrier but also some some opportunities and some strengths here. The group noted that the documentation for Anvil is excellent and thorough. It was discussed that the online forums have also been very helpful in getting new users and current and current users to be addressed from entry level to, to mid level on that and just in general like the quality of engagement who are going to the meetings, the outreach events, just like like personally from my experience with Anvil outside of the ECC. The creation of that GDS CN, the community network is is is doing a lot of very useful things for for a lot of folks, some of whom are on the call. So for weaknesses. It is the activation energy or the learning curve of getting on to this platform versus getting on your familiar existing platforms or infrastructure. One example was, you know, why, why to get all the training on Anvil when Galaxy, you can just use the free version of Galaxy and that should solve a decent amount of your needs if you are teaching an undergrad bioinformatics intro course. That was a use case for that. Another weakness is that the breadth of the tools and the capabilities are fragmented for some of the for some of the support needs and just, there's just a lot of stuff in there. And that that could be another weakness, where a focusing could be beneficial, or at least a discussion on the focus and could be beneficial. And the major one, I think that we discussed quite a bit was the costs, whether they were actual, or more so the perceived unknown costs, getting into that. So, I think we would discuss we did discuss how there is the financial barrier at, you know, whatever school you're at. But the administrative barrier of just getting a way to pay for an account. Some people don't have, you know, school credit cards, and, and, you know, might even have to use some other, you know, way of getting an actual account. And that points to like the lack of the free tier for users because they have to enter the credit card to get an account and, and so forth. So so that was that was a major bear, I think, of this and then it has been discussed this in many different iterations. Another suggestion about a weakness that is solvable is more video documentation versus print. So, the example that that sort of gave was how good ingenuity pathways analysis IPA, which is a kaijin bioinformatics product is very well documented, although that's a premium paid product. But you know short five minute videos like used to be on Galaxy, like would be probably helpful and beneficial. So the difficulty in platform integration was noted as another weakness. And more so like the need to have an easier technical entry point for brand new users, notably the ones who are coming from the from a new school from a smaller school, who this is the first exposure to genomic data science platforms. This is advertising and getting awareness out. We again talked about the gbsn where it's a fairly large group, I believe there's 30 or so schools involved in that. But that could be something of a weakness of you know, reaching out a little bit more and an opportunity. And, and lastly for weaknesses, the fear of investing in a dead end utility that was the word.  What happens the next semester when you know that student has graduated or moved on or, or, or so on so discontinuity of the platform being ported to other institutions or other places. Within the weakness comes our opportunities. So the ongoing efforts to retrain faculty in data science where an book could be extremely beneficial and useful for that. Specifically in unique perspectives needs and concerns and constraints of new faculty of old faculty who are being retrained. So, there are high institutional barriers to using the cloud. And those position to provide materials and awareness for researchers to navigate this that was seen as an opportunity. The robust research on how and those being discovered and used. And to get that information out there could be an opportunity to normalize cloud computing for the next generation of trainees. As always, the opportunity. Research faculty and providing resources to integrate and go into the undergraduate coursework which I know is a major focus of the training section. So that continues to to be implemented well. And the training, a sign up in billing at various scales was discussed as an opportunity can to overcome that hesitation of the of the credit card sign up continuing engagement of student users. And that leading into how data science workforce development and in particular minority data science training can happen with with the initiatives that that you've announced on this meeting. Another opportunity sharing course documentation for teaching and finally opportunities to retrain the current workforce. And finally threats. Great. So, there are psychological barriers to paying for compute in addition to the administrator barriers. And just the technical barriers. And that's that is a threat. These would be institutional barriers to cloud computing. And that's the general where I think a lot of us are still fighting that fight of, you know, cloud versus hardware. And you know just how to navigate that lack of an easy and cheap sign up for cloud computing could prevent and from becoming widespread and academic settings. And lastly, lack of skill transferability between platforms is a barrier to to the investments that are needed. So, that's where we will, we will finish up our squats. And thank you to the group this was just an eczema discussion we ran out of time. See, this is excellent. We do have time for anyone who wants to add anything or ask questions. Great job. See it really has been excellent reports. One more national, I get one more national reference here usually like you know when you see a concert. There's more talent in the audience and there's up on stage so it's just going to say that right now. All right. I think this is, we all heard very, you know, excellent reports from excellent discussions that took place during the breakout rooms sessions. I want to thank you all for really spending time with us and I think we're reaching the last stretch of this meeting and I'm going to hand this off to to Ken. And thank you to our repertoires and our discusses and attendees for the very thoughtful and informative feedbacks from the SWOT analysis. So can everybody hear me. Yes, we can. Okay, so I'm going to share my screen and go over what we're going to do for the closing and summary and closing. The logistics for the summary is while you've put together your SWOT analysis, I've been consolidating those listings and I've put them into four separate documents that we're going to review together. Because we didn't, I didn't think the strength was something that needed further evaluation. I really pulled off the elements, I mean the listings from the weaknesses, opportunities and threats. And so, and put them together so that we can actually take a look at them collectively and decide if these are elements that these are listed that could be addressed by the ample team alone. What should it be done as part of a collaboration and if so, who, what, who should be potential collaborators. In addition, this is the opportunity to collectively look at each of the listings under that different SWOT elements to see are they in the right place. Should a listing that's in strengths be actually put in maybe a weakness or I mean, if it listens and weakness should be put more in the opportunity or a threat. So before I say does anyone have any questions about what we're going to do. Okay, well then I will get started, and I will first show the start with the weaknesses and then show my screen here. Okay, can people see my screen. Yes, no, maybe. Yes. Okay, so then what we have here the first one the lack of phenotypic harmonization across programs as a weakness. Is this something that should be done by the ample team to be addressed or should there be a collaboration that should be done for this. Okay, so we'll put it as a collaboration. Okay, and I'm assuming this is in the right right element as a weakness. Okay, moving on. Is an animal is perceived as a challenge because it's cloud based make it easier to use and test drive the platform to get an idea of cost. This is something that should be done as an animal alone team effort or should be done as a collaboration. Again, yes. Sorry. Yeah, one of the things that came up in the outreach section is that some several the annual platforms don't really support free tier or use use of the account without a cloud without a credit card that kind of thing. So to me that elite that part at least falls within the end built team bucket. Okay. Anyone disagree. No, I agree. I agree completely with that. I mean, does anyone this think that this should not be under a weakness or should be classified under another element in the SWAT. No, I think it's weakness. Okay. Next one lack of understanding and how the cloud would have how the cloud use will cause that putting this is what may put the third column as far as in the right so it looks like this was coming out of the, out of the third column is like is this an actual threat. So let me first let me first reverse this and say, is this in the right element should this be classified as a threat. I would vote for the reason being that even, you know, most of us may not understand the real costs. Deeper sense right there is superficial sense about user not knowing what the cost will be but actually, you know the deeper level cloud provider make hide actual costs. One example is the illusion that bandwidth in a data transfer doesn't occur but it does so the cloud provider hides that from us and charges egress costs and storage cost to recoup their cost of bandwidth. And as the size of data grows and this space actually grows bandwidth will not keep up bandwidth will be more and more expensive so I consider that trend to be a threat at the deeper level as well as more superficial level of the user not understanding the cost. Maybe just in alignment with the slot processes, at least on the discussion space, you know, we some of these threats we framed as opportunities for conversion into opportunities. And so even if they begin as a threat. There's not there's the process to convert them into opportunities. So the opportunity here, you know, building on Adam's comment is to look at, you know, ways of managing the data that brings the data closer to the consumer, you know, a radical, you know departure would be, you know distributed system like so called interplanetary file system, you know, and file coin that actually brings the data to the consumer and really addresses the underlying cost which is bandwidth, which is going to be the limiting cost in the long run but I'm I mean, you move to, you know, interplanetary file system protocol or file con or anything like that I mean work with Google for now but it is going to be an increasing threat going forward. Is this something that the animal I'm this is something that the animal team should take on themselves are should this be done as a collaboration. I don't see this kind of like a collaboration but I don't want to be the one swaying this one. This is really a deep infrastructure right so I think it's an annual issue right because it's very deep infrastructure issue. I think it's both right there's things we can do inside of annual and then there's things we need the support of the community and the infrastructure and everything. So this question of, you know, anvil being a disadvantage, you know, disadvantage compared to who because it's the same problem that all the cloud platforms are facing is not just unique to anvil so collaboration makes sense among the cloud platforms, but you know disadvantage relative to on prem versus you know, other kind of formats. Okay, so I will put this as a collaboration. So the next one. The one person uses data gap and finding data is very difficult she does pharmacogenomic studies show how to use anvil and very concrete manner for example how identified data sets and specific areas. I see several of them that probably fit under this mode under this heading this looks like more of how to query and search data sets and refine those data sets that we have in here with the would would people see this as an anvil only effort or this should be a collaboration. I mean you can break this up in two different ways right there's a specific case of finding data is a challenge and we should definitely have resources for that. So everybody that comes up with a use case like I want to figure out pharmacogenomic data that's has to be collaboration, because it has to be someone coming over the use case that we'd want to specifically address their concerns if they're not currently done. So yeah I think there probably is a gap that the ample team doesn't need to address because we know you some use cases and we can do that but some of this we've got to have the help of the community. Okay. Is anyone distributed this is in the that this does I think this is not in the right element. Okay. This one. And this is a weakness are in some cases a threat dependent on which breakout room that you were in this is related to cost so managing costs for not just the data courting center also for all sites. Is this something that we consider as a as a as a anvil only effort to resolve or is this really a collaboration. I think cost reporting and cost controls within anvil workspaces that, you know, have been evolving and are getting better over time. But if we're talking about broad costs across the entire sort of ecosystem of submitters some of which may not be using anvil directly to process their data that's a broader sort of thing. Let's look at the second one after this and this looks is also another cost one related to so I'm everybody's comfortable I will actually skip so they want to talk about this one in more detail unless I was often interpreting that this is similar to cost. This is in general about cost at cost control for anvil. And can you might be able to tag this one maybe this is more central to using anvil workspaces and therefore is kind of anvil team focused and the other one is a broader sort of collaboration focused one. And I'll put that in a note separately. So this is what the next one be what will be the role of anvil how will it interact with consortium in the future concerns about the role of the data courting center going away this may change the dynamic of competition. Is this considered as a this is under the weakness and do is this something that anvil the anvil team themselves can themselves can address or is this something that we really need consortium to help us sort out. I would say this is something collaboration sorry. Yeah, this is something for I would say NHGRI and NIH to consider. As in terms of because they are setting up. You know these consortia the rules for forming consortia, etc. So I would say, at least NIH and anvil. But with advice, maybe an input from existing consortia right in terms of checking these policies or guidelines with the existing participants in this ecosystem or stakeholders who may be affected by these changes. Okay, something in this is as a collaboration since it's more than just the direct anvil team to try to find the resolution to. Right, right. Fair enough. And does this feel like this is still belongs on a weakness or it should be classified as something else. I wouldn't say it's weakness I mean it's adjusting to change. In a kind of positive way, I would say this is capturing really opportunity. Even that anvil can now provide the economy of scale robust infrastructure that used to be duplicated, etc. So I would say it's as much as a challenge because of change as an opportunity because it'll lead to improvements overall. So would others consider this as an opportunity. I would almost think about it from the threat mindset of if we don't properly handle these relationships there will be issues but I think we're, I don't know that we're on the wrong track per se. Okay. I think we'll put this as a potential opportunity if it can be to be for if it could be addressed a threat if it's not. Yeah, I think it actually ties into the one below it. And I think it's a, I think right now, both of these are sort of an identifying a threat to anvil if it's not in, if it, if we're not clear on how it's positioned relative to the rest of the community you know the first is sort of talking specifically about consortia and working with them closely and what how does that competition have and then the one you're on now is talking about sort of outside you know again people coming in and if they're not directly collaborators what is their role. And so I think the opportunity is to really think about how does anvil get positioned in the larger genomic ecosystem. In a way that really helps that is clear to everybody and is is not hindering groups or you know in a way that has a level playing field I think that covers both of those two together. Okay. Thank you Caroline. Any other comments about this about the this item or this listing. Okay. So, the next and do we need to be collaborators of anvil to get funding if we're not collaborators do we have a chance to get funding define defining clearly the parameters of competition this may impact buying from the community this number team but it's also a threat of done correctly. I like to comment on that again. I think it's a very good observation that both lines share the same feature. I would say this also points to even bigger issue which is positioning and bill, much more clearly, you know, especially saying what is it that then will is not doing right right now it looks like it's doing everything. So, I would say decisions need to this needs to be articulated, and then communicated consistently, you know what is the interface between anvil and the rest of the ecosystem. I think that's what we called it. Right now, it looks like analyst everything, and we are part of anvil, and that's all that there is right because of the lack of clarity about where the boundaries the interfaces are. So this sound like I mean going back what Carolyn you've mentioned you mentioned and also know this is really something that the NHN, and the community needs to work collaboratively on to address. All right, so the next one we have developing tools for analysis on open access data sets. This this this more something that the animal team can address or this really should be done in collaborations. And this will probably go the same for the single cell analysis and also so these two could probably go into the same category actually. So author, I think this could be done as a collaboration, but I want to know if anybody disagrees with that. I think it's a collaboration as well. Okay. And improvement documentation related to doc store. Yeah, this this also this to people I think this belongs under the animal team alone to address, or doesn't think this belongs in animal team alone to address. Well, yeah. And I believe the hard to find tools and workflows people develop I think this is an angle only and will team effort to to address. I would disagree with that push back I mean their efforts now to develop ontologies of like how what tools do what and what data types they do what I mean there's some community effort so it can be a part of the implementation but it's collaboration. Well, I mean, I think actually just add to that like our tools and workflows being developed by people that aren't part of the animal core team and if they are then it's a much bigger problem than and bill properly labeling things it's a community community challenge. Yeah, and along those lines, I mean, I think that that's exactly the point is you want the community developing more tools. Okay. And when they have a different opinion, I want to add to that. Okay. So I'm going to put this also for the second one to tools for interpretation because we kind of and tools for analysis and data sets. So I kind of see like these are all under about tools developments and integration. This would be something that we do feel a collaborations would be useful when anyone disagree with that. I look like that's what we were trying to convey before by maybe mistaken. I agree. Okay. Yeah. All right, mechanism to announce the log in. Yeah, that's clear anvil. Yeah, that's clearly yes. The only thing is that one's a little problematic. I didn't mention it before. I mean, there are good security reasons why you don't want that feature. Yeah. That's a real weakness. I think it's a strength and I don't think we should change it. Okay. So I just wanted to be clear. So, because these are things that these are just things that the in this case this was an individual break room session highlighted as a weakness is not this we understand that some of these things may not even be possible to do. It's really just a gauge on what we what we think if it, if it could be done, where should the bulk of that effort reside in as far as how to address it. Which room did this come from 10. This came out of this would be. Let me go back and see I actually don't remember each an engagement or something else. No, it wasn't outreach. It was. Actually, I don't remember this anyone remember that where this came from. Oh, it's actually announcing tools. I should know that it was analysis and tools. So can I think we should move on from this point, just in the interest of time. Yeah, and I think maybe looking at a lot of these on the screen I just want to I'm just looking at the clock and then looking at where we are. Um, you know, So, yeah. No, go ahead. I didn't mean to apologize. I think that you can group a lot of these together is what it was. Yeah. So yeah, if it's all right, I then we can do that and then we won't go through every single one of them. So, I'm going to skip down to the cost. How about this one here we get down lack of free to need to create accounts before getting hooked on science and capabilities. Um, do this is something that we feel belongs under a collaboration that needs to be done. Or is it something that's within the animal team to try to address. I think we should try to explore it under anvil. Okay. Yeah, I would agree with that. And video documentation. I don't think that this doesn't belong under the anvil. Yeah, that's something we can panel. Yeah. And I would imagine platform integration is a collaboration. Yeah, absolutely. And I kind of look at the need for either entry points for new users to be similar to helping to set up accounts for capability. So I'll skip that. I want to actually go to this fear invested in a dead end utility. Could somebody elaborate on that and discuss like, how does this something that animal could address or as a collaboration can address. And I think this came up in the context of, you know, what happens if, you know, angle ends in two years or in five years. You know, try to get people encourage them to come on. We want to make sure that they're going to be supported for many, many, many years in the future. So that's similar to the sustainability question. Yeah. So this is really something that a collaboration would have to help try to address. Yes. Okay. And so now, and I look at the, the needs to better incorporate clinical research needs and expertise that look at this is some of us, you know, working with collaborators and identifying, kind of like adding tools to get in the community to bring tools and resources and to But I will also go down to the one that says portals look like it might get busy quickly. You actually I need. Yeah. Celebrate on that. I think that's annual and we're constantly evolving the portal as new capabilities come on. Okay. And let's kind of look at the need for more resources tours and use for particularly those with less experience know that's, I think that's something we've already addressed in the following. Any comment about the previous item, which is portals, you know, customized portals specific audiences are sometimes built by data coordination centers. And it's not clear, you know, whether or not, you know, and will would support extension. I would say that would be a collaborative effort between annual and data coordination centers for consortia. For sure. Alexander and we do support it and we would love to see a lot more of it. And not just by data coordination centers but you can imagine lots of visualization portals and things like that. It's a great activity. Okay, so then we go in here. All right. And as far as the last three I think in some shape or fashion we've already covered these in previous ones that no one, anyone disagrees that please speak up now. Okay, then I'm going to say this and move on to the next one, which is going to be opportunities. And I'll quickly run through these also. Like I said, there's a lot of repetition in here also for this one. And so, overall, a lot of these were really about forming collaborations to address bringing more data sets in focusing on reducing the barriers for access to the annual platform and to find ways to continue to democratize data sets and developing new models. A lot of these opportunities that I saw here were really more focused on consortium and collaborations that were occurring but there's a few I wanted to point out just to highlight to get some feedback from people to see if I was mistaken in that and just see if we have a general understanding. So, when we look at the idea of scrolling down through here, query and insert developing search queries for anvil, which is one is how I to hear the ability to do this was one of the things that was how I as an opportunity. In general, do people feel that efforts to improve the querying and search for tools resources is something that is anvil. This should be something to be anvil led or is it something that we should be doing as a collaboration if so what kind of collaborators should we look for to help and to improve efforts to querying do searches on the anvil platform. I think it'll be both and it's kind of that earlier discussion there's work we can do in the anvil team but there's also, you know, groups that are proposing standards in the community. This is where we haven't had discussions about building in the same way that we have a data dashboard build like a tool dashboard that would kind of summarize and support search and other characteristics across the whole platform that's not available today but it's something we've been planning towards. And I also agree that it's a, it's kind of a collaboration, collaboration type of activity with end users this is, this is one of the ways you get your end users to engage in helping to improve the resources make, you know, getting their feedback on the current capabilities and what would make it better so definitely collaboration. Okay. So, like I said, I agree. Okay. And so, one thing I had here that I was interested in normalizing cloud computing for the next generation. I was wondering if we could elaborate a little on that to identify how best, like, how, what, describe more in detail what that means and how anvil and could do this either by themselves are in collaboration with others. I can speak to some of it I think which is just that in the in the breakout outreach and engagement. There was a general sense that there was this this real big activation barrier just doing anything on the cloud in the cloud, and that the current generation of bioinformatics researchers where we're mostly very content with how they were working, and didn't see the need. And when they did see the need it was very challenging to turn to switch over the what I found really impressive about what anvil outreach and engagement was doing was getting the undergraduates to start using anvil through some of their outreach outreach efforts. And that that means that sort of the next generation of graduate students will have a significant number of cloud natives so to speak, ready to go. I'm wondering why they can't gain access to large data sets without downloading them because I could do that on anvil three years ago, sort of, sort of comments. So that's the opportunity that I think we saw here. And if I could just add to that. Can I think any activity that will expand or extend the user footprint will have the impact of normalizing cloud computing for the next generation, anything you do connect that expand the footprint of anvil use will have that impact. So to me this sounds like this is something we need to do with collaborations with our colleagues with our colleagues in extra mail community to help us figure out how to make sure we accomplish this whether you want to disagree with that. I think you're right. And the last one I wanted to go with sharing course documentation. Anvil does have documentation they make available on their portal page but this was considered as an opportunity I was wondering if we could allow discuss this a little more is there other ways that we that thing that we can develop collaborations to share documentation or is this really something that the Anvil team could address. And so what, how could we address that. I think this is definitely collaborative where, you know, yes we can have materials from anvil but there's more and more people start using it and better in their courses and whatnot let's, you know, let's provide a centralized resource for people can add to that. Okay. Anyone disagree with that. Okay, so like I said the majority of the opportunities were collaborate where things that anvil team should be doing collaborations on does anyone feel after no, I don't know kind of skimmed all the way through this. So that there's something that was specific and opportunities that really is something that's anvil specific that they want to address now. Okay, then I will move over to the last one. This thing will let me exit out. And that will be say that go to threats. Well, so I was putting listening copy of these over and looking at these the different threats that were out there. A lot of these were somewhere related towards adjusting to the cultural shifts to help people adapt to using cloud resources, understanding the risk associated with working in the cloud in the cloud environment. So understanding where there are these areas of opportunities to reduce those barriers for transferring between platforms. A lot of these came out to me as opportunities that would require us to actually develop collaborations. But there was a few of these that I thought maybe something we could discuss. I really like to get an understanding on, for example, that like the reliance on commercial cloud providers. Clearly, I think this is an example of a kind of a collaboration. I've had consortium should be collaboration. Sorry about that. But I was wanting to get an understanding more about what people meant by this reliance on cloud price as a threat.  Right.  So reliance on a and anyone vendor could be a threat. And yeah, Karen can speak to this more in the you know this came out of our word. And I'm just laughing Dave. David just mentioned thread is such a strong word and someone we talked about this a little coming into it but it is the T of slot but Karen can speak to this but in our group it came up as a strength right we talked about the it was a working with the cloud providers as a strength and the ability to sort of build on what they're using. But then as you were you sort of hit got to like if you have that if you build on that and they're outside of your control and like intrinsically becomes a threat right because you're if they change things radically or things happen with with Google, then, you know, Anvil is is there. I don't think I don't know that it's something that can fully be mitigated. I think we were looking at it more as something that should just be called out. If we're calling it a strength to recognize when you have a strength that's reliable relying on something external, it sometimes just automatically become a threat. I think there are some technical ways to mitigate the risk. I do think there is a risk there and number one at the top of the list is, you know, we are moving into a multi cloud environment you know Anthony mentions the Microsoft Azure platform. We've also, I mean, it's, it's a loose connection today but you know, we do have an opportunity as well to connect to institutional resources and that's how we provide GTACs available egress free today. That's something I think that could be expanding the future as another way to mitigate this potential threat. Thank you. And so, like I said, no, this is very helpful. So, like I said, most of these were things that look like they were really focused on developing collaborations. And so anyone on this list that people want to just single out that they would like to talk more about that are they feel that they really are things that the animal team should just address on their own. Okay, and you also realize there's there's there's common things that are actually not in all three of them like the cloud cost it is both a weakness. In some cases it's an opportunity and in some cases it's also considered a threat so there's some things that truly cross different types of SWAT elements. So I'm going to stop here. And then I'm going to turn this I'm going to give the this this is go back to my slides. I can pull it up. Sorry my computer just froze for a second but I think I can do this without my slides. So I wanted to this concludes our actual workshop, and I want to thank all of you who's come for attending. I also want to specifically thank the members of the ECC. The members of the animal team that for the hard work that they've done along with our program analysts, as well as in our the AV support. I'd like to thank, thank, thank them for the hard work and helping make this possible to put this show on for our virtual virtual format. I also want to thank the communication and HR communications branch for helping us develop the slides for the presentation. And most of all I'd like to thank all the attendees. This was this workshop was very useful and very beneficial for us and it helps us understand what are the critical things that we need to focus on going forward. And I want to thank our speaker Dr. Mr Vince bottom for giving a wonderful presentation and Valentina is there something you want to add one more thing. We also want to say that both the video, the slides and the that were associated with this presentation of this workshop will actually be on our genome website shown here. And seeing my visa acknowledges that thank everybody who helped participate in making this zoom meeting possible. And thank you for your attendance. Yes, anything that I missed. Yeah, I just want to thank everybody. Once again, it's been great and we have a lot of materials to think about and try to prioritize but it's all good. So thank you very much. Thank you. And we got you out 15 minutes early.


So basically, what I'm talking about here is... Give me a minute, please. Yeah, I'll just set the tone. You can just put it in the background. So, yes, what I'm talking about here is why we need artificial intelligence to start with. So, you know, the scarcity of talent and human resources, you can start from slide two. So, if I take a typical use case, you know, case of India, over 100,000 schools have just one teacher. There's a very poor, you know, patient-to-doctor ratio. So, you know, the human talent is scarce, and AI can actually help, you know, help it reach the next 6 billion people who are not, you know, having the access to it. Next slide, please. So, across the sector, you know, AI can help scale the scarce human resources. Next slide, please. So, in a typical AI development cycle, right, we do realize now that, you know, it is important that we use artificial intelligence in a very, you know, good manner, wherever required, for giving access to many more people. But in a typical AI development life cycle, we have these four stages. It starts with that data acquisition, writing your architecture file, training it to get your weights and biases, and then deploying it. So, this is the deployment phase, which I am talking about here. And one thing we need to realize is that, you know, the training environment where you train and actually build your model with your GPUs is not the same as your deployment environment. Your end users will not have the same GPUs there. So, it's very important to treat both of them differently, and the deployment phase is what I am going to talk about today. Next slide, please.  There's a lot of latency. If we keep working in the way that we've used today, the cloud way, there's a lot of network latency. Next slide, please. So, okay. So, what is edge AI? So, what I am talking about here is, for all those, for the AI work, we started with GPUs, and then we realized that, you know, GPUs are expensive. There's a lot of application for deployment where we don't actually need a GPU, but when we add it in the end products, it's very difficult to position in the market because that one component is extremely expensive. So, people move to different cloud, you know, solutions because it's pay-as-you-use, subscription-based, and then what happened is now it comes with its own set of problems and limitation that is, you know, privacy security because it's going out of the network. There's network latency. There's bandwidth issues. Next slide, please. So, why edge AI? Quick response. You have everything central on the node. So, like, latency is less. You have better security and privacy. Everything in the network. You know, you have lesser bandwidth requirement because you have less data to send. You don't send just everything. Only important stuff you send to the cloud or the central node. Less communication cost because of that reason, reduced dependency on the network, and reduced power consumption. Next slide, please. So, why should you care? So, you know, right now from 6% in 2017, the number of devices with edge AI functionality will increase 7% to 43% by year 2023. Next slide, please. So, the next slide, please. So, I'll be telling you about two things which can help in deploying your applications on the network edge. So, first thing here is the neural compute stick. So, this is a tiny, very low form factor device. What happens here is you go train your model anywhere. Like, training environment you're not restricted. You go, you know, use a discrete GPU, have a cloud instance, and train your model. Now you have, and use whatever framework you want to. A lot of people now have a problem with, you know, being comfortable with different framework. If you are an organization, someone will be comfortable in, you know, Pytod, someone else in Cafe, someone TensorFlow, maybe. So, what happens now is... So, again, one thing I'll be showing you in the next slide will be the open Vino. So, what it does is you give your model in any of these frameworks. At the end of a desired, intermediate representation, that is the XML and bin. So, I have your model architecture from that whatever model you want, and I have your weights and biases as a binary blob. And then, once I have the XML and the binary blob, it becomes framework independent, then and there. Now, again, I can just deploy it to any framework that I want, be it a CPU, a GPU, a myriad, which this is a myriad by the way, or FPGAs.  For this device, this is a neural compute stick. So, it is just $99. And what happens is, you know, you just offload your model here, and all the processing happens here. So, even with the Atom, Celeron, or a Raspberry Pi for that matter, you can just infer your deep learning models. So, just imagine this, you know, something like a Celeron processor and a Raspberry Pi is now able to infer your deep learning model, and you don't actually need to go through the limitations that you have with the cloud. So, to end this, there is no one right platform, but, you know, be intelligent and smart enough for choosing the right platforms for each component of a product. So, you just don't need to just have a GPU and continue with it, or a cloud and continue with it. So, there's not one right platform. Just think that, you know, at each phase, which one is required, and make use of it. So, next slide, please. This is the picture for the OpenVINO. This is Open Source Tool and free to use. So, you have your trained model of these, any of these frameworks. We'll have a model optimizer. We'll convert it to IR. So, you have those XML and VIN. Again, a lot of layers that is there in training is not actually used in inferencing. So, we automatically take care of it in a black box, and, you know, those layers are removed and optimized. So, you have an optimized graph now, which is, again, to repeat both framework and platform independent. And then, using the API, you can just put it on any of the platforms. Thank you so much. This is Mashen from Intel. Thank you.


Live from Santa Clara, in the heart of Silicon Valley, it's theCUBE, covering Juniper Next Work 2016. Brought to you by Juniper. Now, here are your hosts, John Furrier and Stu Miniman. Hey, welcome back, everyone. We're live here in Silicon Valley in Santa Clara, California. This is theCUBE's Silicon Angels flagship program. We go out to the events and extract the signal from the noise. I'm John Furrier with my co, Stu Miniman, and also at wikibond.org. And our next guest is Chris McGurdy, VP of Global Security Services at IBM. Welcome to theCUBE. Thank you. So, obviously, Stu and I have been talking about security all morning. Juniper, a networking company, talking about security, obviously it's the hottest area where the action is. And where the threats and the detection and the enforcement's all happening. You know, policy-based networking. We've seen this before, but more than ever, security is the number one conversation. Top of mind. It's in top of mind, boardroom. It's all about risk. Now, it's all the hacking from foreign countries. Big enterprises are being affected by this. So, obviously, IBM is a lot of big enterprises. I mean, how do you guys talk to customers? What is IBM's conversations with customers when they say, hey, help us? So, all of our customers are now coming to us. And one of the most important reasons is because our chairman talks about security all the time. And one of those pieces that we like to talk about is secure by design. So, our customers, as they're developing solutions and asking us to write new applications or build new infrastructures or look at going to the cloud, their expectation is it's secure. And we can't provide a solution to our customer that doesn't include security because their expectations are we are building that. As the largest enterprise security company in the world, our customers expect that to happen. And of course, IBM's got the magic of Watson, a cognitive brain of the network, if you will. Yeah, but all kidding aside, cognitive is AI, promise of AI, machine learning. It's using data, but it's still a human problem.  It's a big data problem meets networking. Absolutely. So, very interesting story as Watson was first being created for security. So Watson for security, you're taking structured and unstructured data and trying to make intelligent decisions with that. The security language is very different. So ransomware, what does Watson think about ransomware? The first time Watson heard it was, that's a location, geographic location, where is it supposed to be? So it's a challenge to educate Watson on the different language of security. So currently today, we have eight universities that are participating in helping educate Watson to become smart in security. In our managed security services, we are already using that for our customers. So we're ingesting about 35 billion events per day. So that way we can teach Watson how to help us make better decisions at level one, level two, and level three. So as threats happen, we're able to provide remediation and take action much more quickly. So up and down this stack pretty much, you guys will look for any events and feed that into Watson, almost like a black box, if you will, but ultimately for some sort of predictive, prescriptive solution? Absolutely, so think about the number of white papers that are written in security. How do you make sense of that in a very quick amount of time, along with real events that you can look at, the telemetry data with source data, event data, and then be able to say this is what type of event this is. So you can have different variants as hackers create and utilize old code. We continue to see the same types of events. SQL Slammer, for example, is still prevalent in the wild. Different variants of that are used to create new attack types. We must be smart about that and use old intelligence structured data to be able to write new signatures or look at those different types of variants and understand how do we protect our infrastructure against those types of attacks. Chris, Juniper's been talking about the transformation of moving from hardware-defined to software and cloud-defined. How does that fit in with the services that your group offers? Absolutely, so as a security service provider, we're both doing network design, deployment, as well as managed services. IBM is the only company that is able to help from a strategy to deployment, as well as being able to do the run state for our customers. As we're looking at cloud, hybrid cloud, and continue private data center, it's important that you're able to provide a risk framework that is measured the same way across all those types of infrastructures. So we're able to look at and utilize the Juniper platform as what we call a zero trust infrastructure. So whether you're going to a cloud or you're a traveling sales person or in a different role where you're connecting to the network infrastructure, we want that person to be inspected the same way, whether they're on-premise or off-premise, they must be treated as zero trust and that's what we're bringing to the market in what we call our security hub along with Juniper. So as an emerging offering, whether it be hybrid cloud or cloud, we need to be able to do that same type of inspection wherever that infrastructure lies and looking at the network is the most important piece. So being able to capture network traffic from all endpoints is important to get all that telemetry data. What about SaaS and security because this is a cloud problem too and people move into the cloud at a rapid delay. How does the SaaS software market impact your engagement with customers? Because in one level it's like, we got to move to the cloud, all this operating benefits are going to be great, good leverage, a blue mix, there's some cool stuff going on with IBM, soft layer hosting and blue mix, but yet there's now more problem areas potentially or is it better in the cloud? I mean, how do you guys have those? Take us through that conversation. So the conversation is very much, you should adopt cloud, right? We want to see people move to the cloud. It's very important as they're able to get more elastic, right, as a service offerings where customers can consume in an OPEX type model. So we're actually able to provide infrastructure as a service as well. When you move to the security platform, it is a great opportunity to transform your security environment. You don't have the same legacy problems as you're moving to cloud infrastructure. The cloud providers have already started thinking about what type of security services can we enable in the cloud? I was recently in the Philippines and some of the reasons around compliance and making changes to their security policies haven't really hit, but as they start making leap changes in the type of infrastructure they're able to provide, they're looking at how do we do secure by design? So the first thing that they talk about and in most of these organizations the same way SaaS as they move to the SaaS environment, they're looking at how do I put security in that? The first thing they think about is we can't deploy this without security and that's what we're emphasizing with our customers is you must be secure by design. And this brings up another point about your customers ultimately in the SaaS market, the API economy, it's a lot of different software that could be really good. So let's just say I'm a customer and I'm an IBM customer. I'm like, hey, I use ISVs a lot for instance and they have apps. What do I do just about securing the binary? Is it a binary in the app?  So I don't know idea, it's all about zero trust but I have a trusted relationship with ISVs. They're going to run on my network. I mean, it's a nightmare for them. So how do you advise that situation? So what I tell people is you have to look at security end to end. You must look at the risk of the applications that you're running. What is the data that is residing in the cloud? So are you putting your crown jewels there? How do you put protection around those types of assets that you're moving to the cloud? Are you testing those applications? The cloud is simply another place to do offload computing. So you must have the same risk framework. You must employ the same type of strategies for protecting your infrastructure in the cloud as you would at any type of private data center. You must be able to have visibility of the events that are happening in the cloud and correlate those to the things that are happening in your environment back in your own private data center. Chris, can you bring us inside some of those conversations you're having with your customers? Security's such a tough issue for most people. It's a board level discussion. What are you hearing from your customers? So our customers are telling us exactly that. This is a board level discussion. It's what keeps us up at night. They have to protect sensitive data. My conversation with them is, how do you make money? Where are you making money? And in the event that revenue-producing take a power or utility, for example, and someone stopped them from creating power, what would happen to your business? How long can you run without your business? An online retailer.  So I look at it as one brand image, right? So there's certain industries like financial where we see industry turn rates of seven to 10% in our recent Ponymon study. In other industries, it's about revenue-producing. So energy utility, retail. If you can't make money, then you're going to have a problem running your business. So one of the things that's come up on Twitter is concerns regarding cloud threats around malware. We've seen the zero-day attacks. Threat Actors are constantly changing malware to evade signature-based detection. Questions kind of come up to us on Twitter here. What does IBM say to that? Because we heard a little bit of that here about the malware and the FireEye CEO saying, hey, signature-based attacks are static. How do you guys talk about that? Because that's one dimension of how malware is getting smart and dynamic. And so the signature-based detection isn't really working. That's right. And I would tell you that that's been for a very long time. And there are many malware providers dating back to 2006 and 2007 that, you know, as public disclosures reached seven, 8,000 vulnerabilities. And malware providers can't keep up with that. So it is, it used to be- And the new technique is what? Network stuff or what's the- Absolutely. Ransomware. Denial of service on network infrastructure. So being able to look at different protection strategies and to layer those protection strategies in, looking at the endpoint, if you only have visibility to your perimeter, you're only seeing about 10 to 15% of your network traffic. If you can see it down to the endpoint, you can get 100% of that traffic. You have to be able to do incident forensics so you can trace back and find out where the problem is. Endpoint data protection is critical to understand where's the source, where's the destination.  So obviously IBM and Juniper have had a long partnership there. They've been talking a lot for the last few months about SDSN, the software defined secure networks. How does that resonate with you? What's your take on what you've been hearing at the show so far? So software defined is really the next wave and where we are taking our customers together. So one of the emerging offerings that IBM and Juniper are going to market with is what we call our security hub. We've deployed this in several of our customers and the ability to have multifunction capabilities to be able to take our customers to look at their network traffic, to reduce overall cost in an OPEX type model. In one case, we were able to help with network latency, save 33X of what they were paying on their network infrastructure. At the same time, passing packets very fast, reducing cost to provide a secure solution for that customer. So they are capturing the benefits of having security hubs where all traffic is going through the security hub, providing the same risk posture, whether the client is on the inside of their network or from the outside of their network going to any type of cloud provider that they have, they're looking at their entire infrastructure in the same way. At the same time, they have that visibility that comes back into their security operation center. They have Qradar as their SIM, market leader provided by IBM. What we're able to do now is to take with machine identity and now identify users. So identity access management is a huge problem in security. What hackers are doing are capturing user credentials, creating additional user credentials. If you can grab the credentials that are performing the bad behavior, you're able to block it at the user level as opposed to the machine, and that's the capability that we're enabling for our clients. What do you say to those customers that have just had software-defined fatigue as they've heard so much promise from the industry, but software-defined networking hasn't necessarily hit for a lot of customers? That they need to be looking at the TCO. They aren't ready for a transformation, but once they start looking at a total cost of ownership or a ROI around what it means to be able to increase their bandwidth, to provide more security, it's a no-brainer once they start to get that information. And really, it's a couple-page document that is all fact-based. It says, I can save money, I've got a better and more secure solution, I've got more throughput that I can bring to my clients and provide a better user experience. So you're saying SDN, ultimately, is the promise of SDN kind of resides in security? As a security practitioner? From an ROI, from a TCO, I mean TCO, total cost of ownership. Yes. The impact there is security. Low-hanging fruit for TCO. Absolutely, absolutely. To be able to make those changes in the infrastructure, security is no longer a byproduct, but it is the answer, really, and of getting to that solution. So one of the things about the stage today, I want to get your thoughts on this as a parting comment, was automation is inevitable and good? Is automation trustworthy and how and why? And why is automation a good thing relative to the security conversation we're having? So automation has been a long time coming, especially in the security space, so people trusting things to automatically happen, especially when you're looking at a network person that wants to ensure packets pass, availability and capacity. When you're making changes on the fly, you have to be able to trust. So the more data and information that you can get to make sure that those changes and the automation around those types of changes, taking out human error, as I look at security, most mistakes happen because of human intervention. And I call those sometimes the oops mistakes, even down to users clicking on URLs. They make mistakes, very intelligent. Clearly a trust gone bad situation. Absolutely, and even the best make those same mistakes, but the testing and the tricking. It's a big data problem at the end of the day, all of us is coming down to big data. Absolutely. And the cognitive stuff that you guys have resonates with customers I can imagine. Absolutely. But then you get down and dirty, it's the packets at the end of the day. The malware needs to travel on the network before it gets to its host. That's exactly right. You got to get it where it's moving, right? You attack the move, moving threat. Absolutely. And the more data you can get, and that's a big challenge for us, right? So we like to capture as much threat data, as much vulnerability data. So the event data is critical, having all those events. Absolutely. Coming into the security hub, if you will, to look at everything, zero trust means every signal matters. That's right. And you use those signals to extract patterns, map that against other data sets. That's right. That's going to be the flywheel, right? As well as ingesting threat intelligence. What do we know about other events that are happening, potentially at other customers, or as we're doing threat research, or collaboration from our clients. Collaboration in security is one of the biggest pieces that we can do as the good guys in the marketplace. That's actually the biggest story I think of the year is that the security community is actually going to, bringing that old extra net mentality to the market where they're actually sharing the jewels of their network with other customers unprecedented. So there's like a social network developing amongst security experts. That's right. So you see many of the ISACs and the most prominent is in the financial services, FSISAC, has been doing this for many, many years. IBM has a product called X-Force Exchange that is free to customers. We have over 2,000 customers that participate and provide event data to us and to each other so that they can share in the community and understand what is happening, what are the bad guys doing, or understanding the events that happen in the environment. And the more we can do that, the better we are in combating threats. Chris McCrady, VP of Global Security Service at IBM, talking about sharing the data, but also making sure it works well, gets locked down, gets the data and network all combined working together. Thanks for sharing. I guess that's the cohesions do we've been hearing this, the theme of the show, digital cohesions all about sharing the data. Chris, thanks for sharing your insight and the data here on theCUBE. We're all secure here on theCUBE. There's more coverage after this short break. Thanks, John.


Business change is happening faster than ever before. New business models, new competition, new ways of working. But when you have a business system that delivers continuous innovation and a partner who's committed to your success, you can stay one step ahead. Meet Workday. Workday provides a cloud-native system with finance, HR, planning and analysis all in one. Allowing you to surface insights, respond to change and work more efficiently than ever. With a single finance, HR and planning system from Workday, you can change as your needs change and shape your future. Workday uses OpenStack for three primary business reasons. First, to offer an agile and consistent way to deploy applications across data centers. Second, to migrate applications to a cost-effective virtualized platform. And finally, provide an infrastructure for running new Kubernetes apps. Workday's journey with OpenStack began in 2013 when the cloud engineering team was formed. Since then, its OpenStack footprint rapidly grew. Work the private cloud or OpenStack cloud now runs on five data centers. There are 45 clusters, 39,000 running VMs, 422,000 cores and 7600 hypervisors. Workday contributes back to the open source community by presenting and participating in OpenStack summits, organizing meetups and contributing code. And finally, the team that made it all possible.