Operationalizing machine learning has become the number one challenge and the bottleneck today for enterprises even though they are hiring the best talent from data science perspective. >> But when you look at enterprises today, they're still struggling with that. We see so many projects die on the way to production. As engineers consider how to build and scale their AIM ML pipelines, they simply don't have time to wrestle with infrastructure decisions or to wait on the IT infrastructure team to procure and deploy infrastructure on their behalf. When enterprises first build their ML pipeline, they need to choose between specivity as well as flexibility. They also need to choose between speed and cost. While considering all of this, they also need to glue the different parts together. All this requires a lot of time and effort.>> When you build an AI application and you want to actually take the result of your data science work into [music] the real world and bring value with it, you're challenged with the fact that data is not accessible to you in the same way that it was in the lab. and harnessing that data whether it's the real time sources whether it's the historical data that sits in a slow data warehouse [music] and bringing it all together and applying themodel on the data is a big challenge. One thing that happened during the corona period that people realize that you cannot treat models as black boxes. You can't just really plug them and run data through them and hope that they'll be accurate and the prediction will be great. You have to actually monitor them in the production pipeline. You have to retrain the models in the production pipeline and update them and all of that while running application at 24/7. AWS Netup and Iguadio have partnered to provide a one-stop shop solution for enterprise AI. The combined solution addresses the enterprise requirements around scale and security [music] when it comes to the data and across MLOps needs from the beginning to an end of a project. They brought all these together in a fully integrated solution built upon FSX for NetApp on tap. It provides full MLOps capabilities and are consistent even at hyperscaler level and it allows data scientists, data engineers, MLOps engineers to consume it from one place end to end seamlessly. The cost optimization features on board FSX for NetAppet app on ONAP such as automated data taring and capacity efficiencies such as dduplication and compression ensure that enterprises pay only for what they need when they need it. An FSX foret app on tap file system is infinitely scalable, meaning that engineers can utilize it as needed with capacity being allocated on demand. And all this available for quick and easy purchase from your AWS account in just a few clicks.