On February 19th of this year, Barron's dropped an article declaring Oracle a cloud giant. And the article explained why the stock was a buy. Investors took notice and the stock ran up 18% over the next nine trading days. And it peaked on March 9th, the day before Oracle announced its latest earnings. The company beat consensus earnings on both top line and EPS last quarter. But investors, they did not like Oracle's tepid guidance and the stock pulled back. But it's still, as you can see, well above its pre-Barron's article price. What does all this mean? Is Oracle a cloud giant? What are its growth prospects? Now, many parts of Oracle's business are growing, including Fusion ERP, Fusion HCM, NetSuite. We're talking deep into the double digits, 20 plus percent growth. It's on-prem legacy license business, however, continues to decline. That moderates the overall company growth because that on-prem business is so large. So overall, Oracle's growing in the low single digits. Now, what stands out about Oracle is its recurring revenue model. That figure the company says now represents 73% of its revenue. And that's going to continue to grow. Now, two other things stood out on the earnings call to us. First, Oracle plans on increasing its capex by 50% in the coming quarter. That's a lot. Now, it's still far less than AWS, Google or Microsoft spend on capital, but it's a meaningful data point. Second, Oracle's consumption revenue for autonomous database and cloud infrastructure, OCI, Oracle Cloud Infrastructure, grew at 64% and 139% respectively. And these two factors combined with the capex spend suggest that the company has real momentum. I mean, look, it's possible that the capex announcement, maybe just optics and they're front-loading some spend to show the street that it has a player in cloud, but I don't think so. Oracle, SaferCats, usually pretty disciplined when it comes to its spending. Now, today on March 17th, Oracle announced updates to its autonomous data warehouse. And with me is David Floyer, who has extensively researched Oracle over the years. And today we're going to unpack the Oracle Autonomous Data Warehouse ADW announcement, what it means to customers. But we also want to dig into Oracle's strategy. We want to compare it to some other prominent database vendors, specifically AWS and Snowflake. David Floyer, welcome back to theCUBE. Thanks for making some time for me. Thank you, great pleasure to be here. All right, I want to get into the news, but I want to start with this idea of the autonomous database, which Oracle's announcement today is building on. Oracle uses the analogy of a self-driving car. It's obviously powerful metaphor. It's a, they call it the self-driving database. And my takeaway is that this means that the system automatically provisions, it upgrades, it does all the patching for you, it tunes itself. Oracle claims that all reduces labor costs or admin costs by 90%. So I ask you, is this the right interpretation of what Oracle means by autonomous database? And is it real? Is it the right interpretation? It's a nice analogy. It's a test or analogy, isn't it? I would put it as the first stage of the autonomous data warehouse was to do the things that you talked about, which was the tuning, the provisioning, all of that sort of thing. The second stage is actually, I think more interesting in that what they're focusing on is making it easy to use for the end user, eliminating the requirement for IT staff to be there to help in the actual using of it. And that is a very big step for them, but an absolutely vital step because all of the competition are focusing on ease of use, ease of use, ease of use, and cheapness of being able to manage and deploy. But so I think that is the really important area that Oracle has focused on and it seemed to have done so very well. So in your view, is this, I mean, you don't really hear a lot of other companies talking about this analogy of the self-driving database. Is this unique? Is it differentiable for Oracle? If so, why? Or maybe you could help us understand that a little bit better. Well, the whole strategy is unique in its breadth. It is really brought together a whole number of things together and made it of its type the best. So it has a single, a whole number of data sources and database types. So it's got a very broad range of different ways that you can look at the data. And the second thing that is also excellent is it's a platform. It has, it is fully self-provisioned and its functionality is very, very broad indeed. The quality of the original SQL and the query languages, et cetera, is very, very good indeed. And its ability to do joins, for example, is excellent. So all of the building blocks are there and together with its sharing of the same data with OLTP and inference and in-memory databases as well, together the breadth of what they have is unique and very, very powerful. I want to come back to this, but let's get into the news a little bit in the announcement. I mean, it seems like what's new in the autonomous data warehouse piece for Oracle is new tooling around four areas that I, so Andy Mendelssohn, the head of this group and sort of the guy who releases his baby, he talked about four things. Maya takeaway, faster, simpler loads, simplified transforms, autonomous machine learning models which are facilitating what he called citizen data science and then faster time to insights. So tooling to make those four things happen. What's your take and takeaways on the news? I think those are all correct. I would add the ease of use in terms of being able to drag and drop the user interface has been dramatically improved. Again, I think those strategically are actually more important that the others are all useful and good components of it but strategically, I think it's more important to see ease of use, the use of Apex, for example, are more important and... Why? Why are they more important strategically? Because they focus on the end users capability. For example, one of the things that they've started to introduce is Python together with the spatial databases, for example. That is really important that you reach out to the developer as they are and what tools they want to use. So those type of ease of use things, those types of things are respecting what the end users use. For example, they haven't come out with anything like a click or a tableau. They've left that there for that marketplace for the end user to use what they like best. You mean they're not trying to compete with those tools. They had a laundry list of stuff that they supported, talent, tableau, looker, click, informatica, IBM. I had IBM there, so their claim was, hey, we're open, but so that's smart. That's just, hey, they realized that people use these tools. Don't try and exclude other people and be a platform and be an ecosystem for the end users. Okay, so Mendelssohn who made the announcement said that Oracle's the smartphone of databases. And I think, I actually think Ellison kind of used that or maybe that was us applying it to him. I thought he did, like the iPhone of when he announced Exadata way back when the integrated hardware and software, but is that how you see it? Is Oracle the smartphone of databases? It is. I mean, they are trying to own the complete stack, the hardware with Exadata all the way up to the databases or the data warehouses and the OLTP databases, the inference databases. They're trying to own the complete stack from top to bottom. And that's what makes autonomy possible. And you can make it autonomous when you control all of that, take away all of the requirements for IT in the business itself. So it's democratizing the use of data warehouses, it is pushing it out to the lines of business and it's simplifying it and making it possible to push out so that they can own their own data, they can manage their own data and they do not need an IT person from headquarters to help them. Let's stay on this a little bit more then I want to go into some of the competitive stuff because Mendelssohn mentioned AWS several times. One of the things that struck me, he said, hey, we got, we're basically one API because we're doing analytics in the cloud, we're doing data in the cloud, we're doing integration in the cloud and that's sort of a big part of the value proposition. He made some comparisons to Redshift. Of course, I would say if you can't find a workload where you beat your big competitor, then you shouldn't be in this business. So I take those things with a grain of salt but one of the other things that caught me is that migrating from on-prem to Oracle in Oracle Cloud was very simple and I think he might've made some comparisons to other platforms. And this to me is important because he also brought in that Gartner data. We looked at that Gartner data when they came up with it. In the operational database class, Oracle smoked everybody. They were like way ahead and the reason why I think that's important is because let's face it, the mission critical workloads, when you look at what's moving into AWS, the mission critical workloads, the high performance, high criticality OLTP stuff, that's not moving in droves. And you've made the point often that companies with their own cloud, particularly Oracle, you've mentioned this about IBM for certain DB2 for instance, customers are going to, there's going to be a lower risk environment moving from on-prem to their cloud because you could do, I don't think you could get Oracle Rack on AWS, for example, I don't think Exadata is running in AWS data centers. And so that like component is going to facilitate migration. What's your take on all that, Spiel? I think that's absolutely right. Your crown jewels, the most expensive and the most valuable applications are the mission critical applications, the ones that have got to take a beating, keep on ticking. So those types of applications are where Oracle really shines. They own a very large, high percentage of those mission critical workloads. And you have the choice, if you're going to AWS, for example, of either migrating to Oracle on AWS. And that is frankly, not a good fit at all. There are a lot of constraints to running large systems on AWS, large mission critical systems. So that's not an option. And then the option, of course, that AWS will push is move to Aurora, change your way of writing applications, make them tiny little pieces and stitch them all together with microservices. And that's okay if you're a small organization, but that has got a lot of problems in its own right because then you, the user, have to stitch all those pieces together. And you're responsible for testing it, and you're responsible for looking after it.  So AWS, in my opinion, needs to have a move towards a tier one database of its own. And it is not in that position at the moment. Interesting. So, you know, okay, let's talk about the competitive landscape and the choices that customers have. As I said, Mendelssohn mentioned AWS many times. Larry on the calls often takes, it's a compliment to me. When Larry Ellison calls you out, that means you've made it, you're doing well. We've seen it over the years, whether it's IBM or Workday or Salesforce, even though Salesforce, big Oracle customer, of course, AWS, as we know, Oracle customer as well. Even though AWS tells us they're off Oracle, when you peel the onion. Five years to migrate some of the work. Well, this, I believe they're still using Oracle and certain workloads, but anyway, we digress. So, AWS though, they take a different approach. And I want to push on this a little bit with database. It's got, it's got more than a dozen, I think purpose built databases. They take this kind of right tool for the right job approach was Oracle, they're converging all this function into a single database, SQL, JSON, graph databases, machine learning blockchain. I'd love to talk about more about blockchain if we have time, but it seems to me that the right tool for the right job, purpose built, very granular down to the primitives and APIs. That seems to me to be a pretty viable approach versus kind of a Swiss army approach. How do you compare the two? Yes, and it is to many initial programmers who are very interested, for example, in graph databases or in time series databases. They are looking for a cheap database that will do the job for a particular project. And that makes, for the programmer or for that individual piece of work is making a very sensible way of doing it. And they pay for it and it's, you know, it's clear cloud dynamics. The challenge as you have more and more data and as you are building up your data warehouse and your data lakes is that you do not want to have to move data from one place to another place. So for example, if you've got Aurora, you have to move the database. And it's a pretty complicated thing to do it, to move it to Redshift. It's a five or six steps to do that. And each of those cost money and each of those take time. More importantly, they take time. The Oracle approach is a single database and in terms of all the pieces there, obviously you have multiple databases, you have different OLTP databases to data warehouse databases. But it's a single architecture and a single design which means that all of the work in terms of moving stuff from one place to another place is within Oracle itself. Oracle is doing that work for you. And that is a, as you grow, that becomes a very, very important to me, very, very important cost saving. The overhead of all those different ones. And the databases themselves originated all as open source and they've done very well with it. And there's a large revenue stream behind those. AWS, you mean? Yeah, AWS, yes. The original databases in AWS and they've done a lot of work in terms of making it fit with the pads, et cetera. But if a larger organization is, especially very large ones, and certainly if they want to combine, for example, data warehouse with the OLTP and the inference, which is, in my opinion, a very good thing that they should be trying to do, that that is incredibly difficult to do with AWS. And in my opinion, AWS has to invest enormously in to make the whole ecosystem much more, much better. Okay, so innovation required there, maybe as part of the TAM expansion strategy, but just to sort of digress for a second. So it seems, and by the way, there are others that are taking this converged approach. It seems like that is a trend. I mean, you've certainly seen it with SingleStore. I mean, the name sort of implies that formally in MCQL. I think Monty's Weebin's place machine has probably had it in a similar direction with embedding AI in. Microsoft's kind of interesting. It seems like Microsoft is willing to build a subtraction layer that hides that complexity of the different tooling. AWS thus far has not taken that approach. And then I'm sort of looking at Snowflake. Snowflake's got a completely different, I think Snowflake's trying to do something completely different. I don't think they're necessarily trying to take Oracle head on. I mean, they're certainly trying to, I guess, let's talk about this. Snowflake simplifying EDW, that's clear. Zero to Snowflake in 90 minutes. It's got this data cloud vision. So you sign on to this Snowflake, speaking of layers, they're abstracting the complexity of the underlying cloud. That's what the data cloud vision is all about. They talk about this global mesh, but they've not done a good job of explaining what the heck it is. You know, we've been pushing them on that, but we got. Aspirational. Well, I guess, I mean, yeah, it seems that way. And so, but conceptually, it's, I think, very powerful. But this, in reality, what Snowflake's doing with data sharing, a lot of reading, it's probably mostly read-only. I say mostly read-only, there you go, you'll be there. But it's mostly read. And so you're able to share the data, it's governed. I mean, it's actually quite genius how they've implemented this with its simplicity. It is a caching architecture. We've talked about that. We can geek out about that. There's good, there's bad, there's ugly. But generally speaking, I guess my premise here, I would love your thoughts, is that Snowflake's trying to do something different. It's trying to be not just another data warehouse, it's not just trying to compete with data lakes. It's trying to create this data cloud to facilitate data sharing, put data in the hands of business owners in terms of product builder, data product builders. That's a different vision than anything I've seen thus far, your thoughts. I agree, and even more, going further, being a place where people can sell data, put it up and make it available to whoever, whoever needs it. So, and making it so simple that it can be shared across the country and across the world. I think it's a very powerful vision indeed. The challenge they have is that the pieces at the moment are very, very easy to use, but the quality in terms of the, for example, joins, I mentioned the joins were very powerful and horrible. They don't try and do joins. They would say- They being Snowflake. Snowflake, yeah, they don't even, right. They would say use another Postgres database to do that. So, they have a long way to go- Complex joins anyway, maybe simple joins, yeah. Complex joins. So, they have a long way to go in terms of the functionality of their product. And also, in my opinion, they should be going to have more types of databases inside it, including OLTP. And they can do that. They have obviously got a great market cap and they can do that by acquisition as well as they can- They've started. They've started. I think they support Jason, right? They support Jason definitely. And graph, I think there's a graph database that's either coming or is there, I don't know, I keep all the stuff in my head, but there's no reason they can't go in that direction. And speaking to the founders in Snowflake, they were like, look, we're kind of new. We would focus on simple. A lot of them came from Oracle. So, they know database and they know how hard it is to do things like facilitate complex joins and do complex workload management. And so, they said, let's just simplify. We'll put it in the cloud and we'll spin up a separate data warehouse. It's a virtual data warehouse every time you want one. So, that's how they handle those things. So, different philosophy, but again, coming back to some of the mission critical work and some of the larger Oracle customers, they said they have a thousand autonomous database customers. I think it was autonomous database, not ADW, but at any rate, a few stood out Aeon, Lyft, I think Deloitte stood out and as obviously hundreds more. So, people misunderstand Oracle, I think. They got a big install base. They invest in R&D and they talk about lock-in, sure. But the CIOs that I talked to and you talked to, David, they're looking for business value. They'll, I would say that 75 to 80% of them will gravitate toward business value over the fear of lock-in. And I think at the end of the day, they feel like, you know what? If our business is performing, it's a better business decision. It's a better business case. I fully agree. They've been very difficult to do business with in the past. You know, everybody's in dread of the... The audit. The knock on the door from the audit. And so, and that is, that from a purchasing point of view has been really bad experience for many, many customers. The users of the database itself are very happy indeed. I mean, you talk to them and they understand why, what they're paying for, they understand the value. And in terms of availability and all of the tools for complex, multi-dimensional types of applications, it's the only, pretty well, the only game in town. There's only DB2 and SQL that have any hope of doing that. Microsoft, Microsoft SQL, right? Yeah. Which, okay. Yeah, yeah, definitely competitive for sure. DB2, look, IBM lost its dominant position in database. They kind of ceded that. Oracle had to fight hard to win it. It wasn't obvious in the 80s who was going to be the database king. And Oracle had to fight it. To me, I always tell people, the difference is that the chairman of Oracle is also the CTO.  I want to say something about- I was just going to make one extra point. The simplicity of and the capability of their cloud versions of all of this is incredibly good. They are better in terms of spending what you need, what you use, much better than AWS, for example, anybody else. So they have really come full circle in terms of attractiveness in a cloud environment. You mean charging you for what you consume? Mendelssohn talked about that. He made a big point about the granularity. You pay for only what you need. If you need 33 CPUs or the other databases, you got to shape. If you need 33, you got to go to 64. I don't know if that's true for everyone. I'm not sure that's true for Snowflake. It may be. I got to dig into that a little bit. But- Well, Snowflake kind of has got a front end to hide it. Right, right, right. But I want to push it that a little bit because I want to go look at their pricing strategies because I still think they make you buy. I may be wrong. I thought they make you still do a one-year, two-year, or three-year term. I don't know if you could just turn it off at any time. They might, they might allow, I should hold off. I'll do some more research on that. But I wanted to make a point about the audit. You mentioned audits before. A big mistake that a lot of Oracle customers have made many times, and we've written about this, negotiating with Oracle. You got to bring your best and your brightest when you negotiate with Oracle. Some of the things that people didn't pay attention to, and I think they sort of caught onto this, is that Oracle's SOWs adjudicate over the MSA. A lot of legal departments and procurement departments, say, oh, do we have an MSA with Oracle? Yes, you do. OK, great. And because they think the MSA, then they can run. If they have an MSA, they can rubber stamp it. But the SOW really dictates, and Oracle's got you there. And they're really smart about that. So you've got to bring your best and the brightest. And you've got to really negotiate hard with Oracle. You get in trouble. So it is what it is. But coming back to Oracle, let's sort of wrap on this. Dominant position and mission critical, we saw that from the Gartner research, especially for operational, giant customer base, this cloud-first notion, this investing in R&D. Open, we'll put a question mark around that. But hey, they're doing some cool stuff with Microsoft. Ecosystem. I put an ecosystem up and they are promoting their ecosystem. Yeah, and look, I mean, for a lot of their customers, we've talked to many. They say, look, actually at the end of the day, this saves us money and we don't have to migrate. So interesting. So I'll give you the last word. We started focusing on the announcement. So what do you want to leave us with? My last word is that there are platforms for certain key applications or key parts of the infrastructure, which I think can differentiate themselves from the Azures or for the AWS. And Oracle owns one of those. SAP might be another one, but there are certain platforms which are big enough and important enough that they will, in my opinion, will succeed in their cloud strategy for this. Great. David, thanks so much. Appreciate your insights. Good to be here. All right, thank you for watching, everybody. This is Dave Vellante for theCUBE. We'll see you next time.


Hi guys, my name is Dan Crawford and I'm the Founder and Chief Strategy Officer here at Axial 3D. And today what I'm going to talk to you about is a little bit of background in the company. What we do in medical 3D printing and how it impacts the hospitals that we work with. Not only that, I'll talk a little bit about machine learning and the AWS cloud is helping us make this technology more readily available with all of the hospitals around the world. Ultimately as a company what we're trying to do is make 3D printing and medicine completely routine by easily transforming everything traditionally 2D in medicine like CT and MRI scans into physical objects which provide huge benefits over traditional medicine. Making surgeries faster for surgeons, cheaper for hospitals but more importantly actually safer for patients. And what this practically means is the process of taking these medical images and delineating the anatomical structures that you actually need from them and reconstructing these in the one-to-one scale physical models of actually what's in your body. When you boil Axial 3D down into one concept it's about making patient data real. We live in a 3D world and our bodies are made up of thousands of complex three-dimensional structures so if you think about it your surgeon or your acting clinician is there a huge disadvantage when they actually go to treat you because traditionally in medicine a lot of these technologies are two-dimensional images viewed on 2D screens and they have to create a conceptual understanding of actually what's going on with their patients which more often than not can be difficult to create or unsafe to interpret. And we've been 3D printing in the medical space now for around five years and each and every case that we've worked with has a huge impact on a patient's outcomes whether it be a change in diagnosis, an approved outcome or enabling a surgeon to actually learn something completely new about a patient that they hadn't seen on traditional two-dimensional images. And just as an example I wanted to introduce you to Jonah so Jonah's a case that we would see pretty much on a daily basis now in Axial 3D one that's hard to diagnose, hard to plan and so Jonah's a 14-year-old boy who suffered from scoliosis all his life and he's gone through a number of surgeries to recently went through was to have the ears that were anchoring rods that were keeping his spine straight to be replaced and unfortunately over time these metal rods that were anchoring on his pelvis had started to wear away and break down over time as Jonah had started to grow meaning that the rods that were actually keeping his spine straight were becoming compromised and a surgeon, Mr. O'Brien, had approached us I mean he was trying to make a picture in his head and attempt to make a plan with not only his own team but a team of neurosurgeons that were in charge of keeping Jonah's spinal cord intact and from these images he had zero surgery on the day around where the pins lay in Jonah's anatomy and actually the extent of the damage that the rods had had to Jonah's pelvis was effectively going in blind we were able to take these images and make them in the single model of Jonah's spine and using this Mr. O'Brien was actually able to see the exact extent of the damage to his pelvis and also able to find three new fractures on Jonah's rib cage that had occurred as a result of the metal work that they hadn't seen in 2D images and this allowed him to prepare all of the equipment that he needed to treat all of these pathologies well in advance of stepping foot into the operating theatre which represented a huge cost saving and time saving and if you think about it, a minute in surgery time can cost up to around $100 three additional rib fractures he was able to bring all the equipment that he needed to treat Jonah to do all the operations on one sitting as opposed to having to do additional operation when he found the rib fractures from the first surgery meaning that Jonah and his parents only had to go through one operation and all the ensuring ICU and recovery time that's associated with this type of operation so it's a huge benefit to Jonah and his family so some of you may not know this but 3D printing has been around since 1984 and in medicine it's been around since the early 90s with the recent desktop revolution, printers have dropped in price by around 99% which opens up this to a much wider market so you might be asking yourself with all the benefits why is 3D printing not routine so from taking a step back we can see three main bottlenecks for this technology not being adopted by every hospital around the world so getting access to the raw images from a hospital and getting an accurate prescription of exactly what is required from the 3D printed medical model is a clumsy and disjointed process which can take weeks to actually do so we fixed this by creating a web application that's hosted on the AWS cloud to allow surgeons anywhere in the world to upload their diagram images exactly how they want the model to be made what anatomy they want to see or be highlighted what material they want to be in and when they actually need it for the operation and this simple application allowed us to make medical models and as little as 48 hours for surgeons cutting the process that could traditionally take four to six weeks so we were able to create an easy and scalable way of collecting data from customers but still the biggest issue in processing the data was scaling and before I tell you a little bit about how we solved that issue I wanted to take another step back and give you another example of why we actually wanted to address this biggest bottleneck in the market which was to do with scalability of our processes and while we began to do up machine learning and SageMaker in the processes is actually because of this patient so Kiri, I wanted to introduce you to she's a 22 year old mother based in Balthast Kiri's gone through renal problems most of her life meaning that she'd previously received a kidney transplant and because of this she was on immunosuppressant drugs and unfortunately for Kiri her previous kidney transplant was beginning to fail and because she was on immunosuppressant drugs it meant that her donor pool was extremely limited to basically only family members but luckily for her when her family were tested her father William was a perfect match meaning that Kiri had one last chance of actually getting another kidney and having a long fruitful life so William went through the standard processes for evaluating the transplantation his vitals were tested and he was scanned to ensure that he had a good anatomical match for Kiri but from the work up William's surgical team found a previously undiagnosed problem William actually had a tumor on his left kidney which meant that Kiri's only kidney that she could get was deemed untransplantable initially given that it was too close given that the tumor was actually too close to all the vital structures within the kidney so Mr Brian the surgeon who was in charge of the transplantation process from William to Kiri approached us get a full picture of what was actually going on with the kidney and if anything could be done so we were able to take the images directly from the hospital through a web platform and create a model of William's kidney on the same day created using 302 dimensional images to create this a physical one-to-one scale model of William's kidney and with this Mr Brian was actually able to conceptualise exactly where the tumor lay which you see in black here against the other vital renal arteries, veins and ureter within the kidney and from having this Mr Brian was actually instantly able to plan an approach to harvesting enough of William's kidney to make it a viable transplant for his daughter Kiri and a procedure called an X-FIVO partial nephrectomy effectively cutting out a portion of the kidney but still leaving enough to be fully functional and not only that, he was able to actually bring the kidney into theatre with him to use as an operative guide to cut out the perfect margins of William's kidney to ensure that he was removing just the right amount of the entire tumor and not to hit any of the vital structures within the organ and after the procedure the pathologist was able to actually confirm with Mr Brian and in fact he'd carried out a perfect nephrectomy so he'd cut the exact margin to remove the tumor within William's kidney on the first try so something that had previously never been done before anywhere in the world making it a world first so this is something, just another one of those examples of hundreds of cases that are being done internally at Axial 3D that has an amazing story behind it made possible by giving surgeons access to this 3D printing technology so back to the biggest problem why is this technology not being used in every hospital around the world? For a hospital or a company to make patient specific data and patient specific 3D printed medical models from scans the biggest bottleneck to all of this is a process called segmentation and this process put simply is the when which you take medical images and annotate them to pull out the anatomy that you want to be printed it requires a knowledge of medical physics it requires a knowledge of anatomy and the engineering principles behind 3D printing to make sure that the images are segmented in the correct way that they can be printed and with each data set being made up of hundreds of two dimensional images this process can take hours and if we look at the levels of complex surgeries and injuries and pathologies that are traded across the world it would take thousands of years of manual processing by skilled personnel so we looked at this and it's just completely unfeasible for 3D printing to be part of everyday practice and critical care if this isn't fixed and this is the main reason medical 3D printing is not being used by every hospital around the planet so we're fixing this by creating machine learning models to automatically process this data based on a verified ground trust database of hundreds of thousands of medical images and these convolutional neural networks that we've built and trained on the AWS cloud and SageMaker are able to process thousands of cases in parallel enabling us to create tools to allow every hospital across the world to actually adopt this technology and what this looks like in practice is we're able to take these data sets like Jumas that would have traditionally taken four or five hours to process by hand and run these through an inference of ground trust data to automatically segment structures from the CT scans in minutes not hours to create 3D models that are able to be verified and sent for printing pretty much instantly and orthopedics represents our biggest use case right now but for this to be routine for every hospital around the world we've developed algorithms for a number of different specialties including cardiology, trauma oncology and maxillofacial surgery and an internal database of ground trust data that is constantly growing and being fine to speed up our processes and we've been creating these machine learning models for over two years now and seeing the huge benefits that they bring to our processes and our scalability but internally we focus on the patient benefits and the reason we do what we do and why we're developing the tools is the impact 3D printing actually has on patients we've helped diagnose and treat over thousands of difficult pathologies and helps save countless hours of surgeries and in some cases like the ones mentioned in this presentation we've actually saved lives by making this technology routine practice in medicine and these newly developed tools along with the scalability that the IWS cloud provides Axial 3D is on a mission now to make 3D printing readily available for every hospital around the world allowing every surgeon to gain access to the technology to improve the lives of their patients Thanks


Before we start before I'll wait for a couple of more minutes for others to come in I want like a show of hands. How many of you here have used react or something like that How many of you have used other forms of virtual DOM? Okay, sure, but that's not a good sign. Okay, so I was just asking how many of you have used react before React is okay, none of you all okay cool. How many of you have heard of this programming language called Julia? One person, okay So a lot of the things I'm doing here the server side part of it is in Julia Actually, all code is server side. That's the whole point of this talk So it's a very easy language language. It's like the syntax is like pseudo code I haven't started yet Yeah, I'm just yeah, so it's it's basically like pseudo code, so I'm sure you guys can figure it out so what Julia is is a it's a Programming language for technical computing which means it's really good at number crunching basically But it's also good for general purpose programming That's the kind of work I do in my job Well, so if you haven't heard of Julia yet look it up and there is a Nice article called why we created Julia which explains why They created Julia and it's like pretty ambitious and very nice and I think we're almost there So is that so yeah, I guess we have two more minutes or should we start? Um Oh, yeah, I know Okay, let's start. Okay, so Hi, everyone. I am Shashi Gouda From time to time I make UI packages for this for users of this programming language called Julia This talk is about some of the work I did on a package called a share.jl You can Google this to look it up So my the topic of my talk is what can you do with a virtual DOM on the server, right? So So what is a virtual DOM? What does it even mean and does it actually work on this?


How much money did you get paid for 6ix9ine? I never got paid a dime. So you market somebody for free? 6ix9ine was not the label favorite. Because of some controversy, he was actually about to be dropped. He has a song called Gummo. I like the song. His label owner was like, yo, whenever you're in LA, we should catch up with drinks. I show up to this place. I always call it the Illuminati Hub.  He said, I can see you're a very powerful influence in the culture. Whatever you say, you as a person, your platform influences a lot. He tells me a lot of problems that 6ix9ine has had. The guy wanted my advice. What do you think we should do? I said, first of all, you've got to put the song out. He needs to clarify what people think about him. Yo, we try to get him an interview with everybody for him to explain his side. Nobody wants to do it. Is it too much if we ask you to give him an interview? And I only did it because I've been the black sheep through my whole media career. I didn't come up on Hot 97. I didn't come up on these glamorous platforms. And I say, yo, fucking, I'm going to give him an interview.


Chapman's three balloon saves in his last eight opportunities. What do you think is going on with him in the mound right now? No, I thought he came in throwing the ball well, actually. Taylor Rose the ball up the middle off of him. And he gets Alberto there. And then Maryfield dumps the one down the line. And then we're in a unique situation where Perez is obviously out of the game. So I made a tough call to walk Santana.  And I just decided didn't want to have him beat us in that spot. So, you know, and then the inning got a little long on him. But I thought, you know, if you really take a look at how he threw the ball up to that point, you know, I think he's actually in a much better spot than obviously some of the results of late have been.