 Hi, everyone. Hope you're doing good. My name is Abhinav Joshi. I am the Director for AI Strategy and Go to Market at Red Hat. Today, I'm going to be talking about how organizations are fast-tracking and operationalizing AI projects with a hybrid cloud platform. Based on my experience in the industry, these are the top benefits organizations are achieving with AI ML. Improve digital customer experience, gaining competitive advantage by offering differentiated digital services, increasing revenue and reducing operational cost, and finally, being able to automate the manual and time consuming business operations. Here are the examples of business outcomes achieved by organizations across various industries because of the power of AI ML. Healthcare organizations looking for faster diagnostics and improving clinical efficiency. Financial service organizations interested in use cases like fraud detection, risk analytics, and improving digital security. Insurance companies interested in automating the claims processing and offering usage-based discounts. Then finally, automotive companies wanting to speed up the autonomous driving initiatives, being able to achieve predictive maintenance and optimizing manufacturing operations. There are many more use cases of AI ML across different industries, namely telco, retail, manufacturing, and so on. The bottom line is AI ML is the real now, and it's helping organizations achieve the key business goals and objectives. Operationalizing AI is not trivial. It requires a complex workflow with collaboration across various teams to help gather and prepare data, develop and train machine learning, deep learning models, integrate these models into the software development process, and finally, being able to roll out the models into production repeatedly as needed. However, there are execution challenges that are resulting in stall projects. The big one that we hear all the time from different organizations is the lack of data, lack of quality data, and talent shortage. Both of them continue to be a challenge. Then in addition to that, being able to operationalize AI is a big challenge. If data scientists and software developers have to wait to access the infrastructure and software tools of their choice, it actually shows, it slows down the data scientists and developers from doing their job. Also, the slow and the manual siloed operations like result in the slower execution of the AI lifecycle. In order to execute on this workflow, what you need is a machine learning, deep learning software tool chain, and also associated data services in the form of data lakes, be it like a database, data warehouse, and so on. And all this has to be supported on a self-service hybrid cloud platform that empowers the data scientists, data engineers, and software developers to be agile and collaborative throughout the whole process without depending too much on the IT operations for individual tasks and activities. The hybrid cloud platform should be integrated with hardware accelerators such as the GPUs to help speed up the machine learning model development and inferencing tasks. Finally, the hybrid cloud platform should offer consistent experience across on-premises, public cloud, and edge locations and be able to efficiently be like all the architecture be efficiently managed by IT operations. Red Hat Hybrid Cloud Platform, powered by OpenShift Kubernetes solution, our broader portfolio, including self-storage, the application services, data services, and a very broad ISV and hardware ecosystem helps accelerate operationalizing the AIML projects at scale. So the value that this hybrid cloud platform has been providing to speed up the software application development can now be applied to the data science workflows as well. It simplifies the deployment, scaling, and lifecycle management of containerized AIML tools by automating the day one to two operations tasks associated with these tools, ensuring the high availability and the faster time to value. Integration with the hardware accelerators such as the NVIDIA GPUs ensures that the modeling and inferencing tasks can happen seamlessly and be able to consume the GPU resources as needed. It can also be consumed either as a self-managed or a cloud-hosted option, thereby providing a consistent way to perform day one to two operations across data center, edge, or public clouds, ensuring the portability of the machine learning, deep learning, and the application development workflows see across data gathering, preparation, machine learning modeling, deployment, and inferencing. It helps extend the value of DevOps to the entire machine learning lifecycle, thereby enabling the automation of machine learning lifecycle and the faster rollout of applications. This helps ensure that the machine learning, deep learning models can be easily deployed in the application development process and the rollout of the machine learning intelligent applications that are powered by AI-ML can also happen in a smooth way. It is a fully integrated hybrid cloud platform that includes key capabilities like monitoring, automation, DevOps, tool chain, and so on, built on 100% open source project to help drive the innovation forward. On that point, this helps drive the innovation without any vendor lock-in. These are some of the examples of organizations across different industry verticals that have successfully operationalized the AI-ML projects with Red Hat OpenShift-powered hybrid cloud platform to achieve the key business goals and objectives. Now let's dive into some of these to understand their specific goals, challenges, solution, and the benefits achieved. The first one I want to talk about is like Royal Bank of Canada or more commonly known as RBC. It's one of the top 10 global banks with over 86,000 employees. This is a story of how they have remained at the forefront of digital transformation and innovation, even going so far as to create a separate AI research group. Red Hat OpenShift-powered cloud platform and also the solution from NVIDIA, our partners, formed the underpinning of this sophisticated AI-ML deep learning platform at RBC. This solution has garnered a lot of media attention like recently a number of times, including most recently in the Wall Street Journal. Banks like RBC handle massive lots of data and easily 10 billion new client interactions a month. Being able to analyze the increasing amount of data was not a small task. In 2016, RBC launched its own AI research center called Borealis AI. And this was done to address some of the challenges and taking advantage of AI-ML for both the internal research as well as to build out the new customer service offerings across personal and commercial banking to wealth management and capital markets. It has a team of over 100 engineers and researchers dedicated to machine learning research and development. But teams sometimes had to wait for two months for the AI infrastructure platform just so they could get started on their work. The infrastructure had to also meet the compliance and security requirements. And even then the workload orchestration and the systems could be slow. So RBC set out to build an AI platform that could meet the demands of the business. They needed to be able to support a growing number of AI developers and engineers and help them get more projects going into production. As part of that, the team needed to undergo a cultural transformation as well, built on a DevOps approach to help them improve operational efficiency and also accelerate a release cycle of the AI-powered applications. RBC partnered with Red Hat and NVIDIA to begin building the Borealis AI infrastructure. The Red Hat Hybrid Cloud Platform gives the bank a consistent cloud platform for both the AI ML research as well as the production rollout of the AI-powered applications that result from this research. Now machine learning applications such as PyTorch also run in this cloud platform and the team is able to take the full advantage of the compute power of NVIDIA GPUs. The results are astounding. So the team has developed like over a thousand models on this cloud platform covering areas including fraud detection, risk analytics and marketing. The data scientists have been able to run 10 experiments in time. In the time it used to take to run one. It helps that it takes days now and not months to build test and run the new models. And the data scientists and the developers have to no longer wait on the infrastructure and the tools to be configured. This cloud platform is powerful too. As one example, the bank was able to analyze data from 13 million clients in just a matter of 20 minutes for a project. Now going forward, RBC is also looking to leverage the AI in customer facing apps as well. It already has a customer virtual assistant called Nomi that is helping customers like achieve better decision. By being able to enhance the app with the new AI powered capabilities like RBC will be able to provide a more tailored approach and an experience and make better recommendations to customer. The next example I want to share is for HCA Healthcare. They are one of the largest healthcare systems in the US and also have the presence in UK as well. For HCA, the Red Hat Hybrid Cloud Platform is actually helping them save lives at the hundreds of hospitals and associated healthcare facilities. Previously, being able to detect sepsis at HCA's hospitals was done manually by the nursing staff. That required a lot of time and also it meant that the patients were only getting diagnosed, say evaluated, once every 12 hours. But the mortality rate for the missed or delayed diagnosis is significant. So the team wanted to create an application that would support the nurses in diagnostic and treating sepsis very quickly and not take them out of the picture. So HCA wanted to meet a few primary goals with the project. First and foremost, be able to improve the sepsis detection and get the diagnosis done at a faster rate. Put the user-friendly software in the hands of the nursing staff that would help them make better and faster treatment decisions. The next thing was being able to build an application that could easily scale to hundreds of sites and be updated as and when needed in a very quick way. And finally, be able to collect in near real time the data from the hospitals to support the decision making throughout a machine learning model, through a machine learning model. They felt that they could do it by giving the data scientists and developers the ability to rapidly build, deploy and update the machine learning model and software to help detect sepsis. That's where the hybrid cloud platform powered by Red Hat comes into the picture. HCA team recognized this wasn't just about throwing technology at the problem. They worked in partnership with the hospitals, like engage the key stakeholders and the CDM management very early in the process and also evangelize the solution to the CDM leadership. The team engaged the sepsis coordinators, like who are the end users, very early on in the software development process. Once the application was ready, they implemented a training program that helped them to adopt the new processes. So this software app called SPOT is developed and runs on this hybrid cloud platform to be able to collect the real time data from the hospitals, which then recommends an action to the nursing staff based on the machine learning models. So the result is a five hour head start in the diagnosis and treatment of sepsis. For clinicians, that means saving lives. Now, based on all the work that was done to build out this solution for sepsis, this earlier in the year, HCA Healthcare was able to build an application that's actually helping them fast track the treatment and the fight against COVID-19. So another very recent example from the healthcare industry is the partnership between Boston Children's Hospital, Darwin AI and AI software company and Red Hat to help quickly operationalize COVID net, radiographic screening, the AI app across the different hospitals in the US using the hybrid cloud platform. The auto industry is changing. Today, cars are digital devices. Tomorrow, they'll drive themselves. This is the story of why Red Hat OpenShift, our hybrid cloud platform, has become the platform of choice for multiple projects at the BMW Group. Today, there are nearly 1.4 billion cars on the road and 250 million of those are connected cars. Manufacturers need to be much more focused on providing the new features for these digital devices that also generate a lot of data. On top of that, every car company has an autonomous driving project. These projects also generate massive volumes of data that needs to be analyzed very quickly by a sophisticated AI ML software. In short, data is now the fuel of the automotive industry. Modern cars are digital devices that double as transport. BMW Group faced several challenges when Red Hat first engaged with them in 2016. First and foremost on their mind was redesigning the connected drive platform to support the rapid growth and offer their customers new services. They sell roughly 2.5 million cars per year and all of them are now connected devices. The autonomous driving was also on the horizon and they knew that it would require a new platform for AI ML. BMW Group is not only about a single project and its goal, it's about the digital transformation of the company and the industry, including a redesign of its connected car system to support the anticipated growth in the connected vehicles. That's how Red Hat Hybrid Cloud platform entered the picture in 2016. In 2018, BMW wanted to accelerate its development of the autonomous driving to address competitive threats and invest in an emerging technology. Its corporate IT team also recognized a need to transform how BMW developed software and ran its IT infrastructure. BMW began to migrate the entire backend to the Red Hat Hybrid Cloud platform in 2016, which resulted in not only in an IT transformation, but a cultural transformation as well. By 2018, it became the platform of choice for what was to become the company's cloud-native ABDEV platform. The connected drive software from development test production was also migrated to the Red Hat Hybrid Cloud platform with AWS as the backend infrastructure. If you drive a late model BMW and use connected drive to make a reservation at a restaurant or to find a tire repair shop, the software running on the Red Hat Hybrid Cloud platform handles your requests. The BMW Group's high-performance D3 platform also supports the autonomous vehicle development program, gathering massive amounts of data from the test fleet on the road. So Red Hat partnered with DXC Technologies, a system integrator, to provide the open-ship-based digital solution that BMW manufacturing research and development teams now use to collect, store, and manage the vehicle sensor data in a manner of seconds rather than days or weeks. And the result is the faster completion and being able to speed up autonomous driving development program. And finally, the end result is the faster execution of the data analytics and AI projects, and also the faster and more frequent rollout of the AI-powered software application to the BMW test fleet globally. I hope you enjoyed the talk. As the next steps, I would recommend you to check out the several more use cases and the success stories that we have on our website. And all those are linked here in the presentation. All these organizations were able to speed up the operationalizing of the AI ML project at scale using the Hybrid Cloud platform, our portfolio, the ISV ecosystem, and open source technologies. Thanks for listening to me. I hope you enjoyed my talk today. Stay safe and happy holidays. Thanks, take care, bye-bye.