The data science practice is moving faster than ever, but its evolution together with the maturity of the wide range of Machine Learning tools available are becoming increasingly complex to manage. Currently, organizations of all sizes are looking for ways to increase efficiency while reducing the time to market through fully automated ML pipelines, most of them embracing the advantages that the public cloud provides today. Bringing those ML pipelines to the cloud with modern CI/CD operational models involves a series of cross-teams challenges and its best practices associated known as MLOps, which sometimes also involves full transformations in the structures of the teams in the organizations. In this session, we will explore the current state of the art for industrializing Machine Learning workflows in the cloud through MLOps pipelines, and how some of the most innovative companies in the world have solved the new challenges with innovation. We will cover the best practices in modern solutions towards important technical principles like consistency, flexibility, reproducibility, reusability, scalability, and auditability, and consider several technical options for orchestrating the pipelines with native cloud services in Amazon Web Services (AWS), like Amazon SageMaker or AWS Step Functions, as well as its possible integrations with some modern open source alternatives like Kubernetes and Kubeflow pipelines. Each alternative will be analyzed with its advantages and disadvantages, and the way to increase the efficiencies following the MLOps principles commented. We will also comment some real examples to cover the most common concerns we see in some companies like: Are you familiar with common design anti-patterns for MLOps like the “Superhero data-scientist dependency”, the “ML black-box”, or the “Deep embedded failure”?; are your teams prepared for operating and supporting machine learning workloads in production?; are you properly documenting and tracking model creation and its lineage and changes?; have you fully automated the end to end development and deployment pipeline of your machine learning workloads?; are you properly monitoring and logging the model hosting?; are you considering the automated re-training workflows when new data is available?; and after all… how can you accelerate the whole process again?. These examples incorporate new interesting technical and architectural concepts that are arising in many companies around the world like: “ML-lakes”, the “ML factories”, or the “universal ML pipelines”. Finally, we will explore how to combine the establishment of solid cloud IT governance mechanisms in the ML workloads for: security, compliance, and spend management; while staying agile and innovating with the speed of the cloud in the development of ML projects for self-service access, fast experimentation, and quick response to changes. This is particularly important for companies that are using machine learning in areas like financial or insurance, but still applicable to any type of company that is embracing ML at scale in the cloud. By the end of the session you should have a clear view on the motivation behind MLOps, the technical alternatives currently available in the cloud for implementing modern machine learning workflows with fully automated pipelines, and how to accelerate all the journey for you and your organization.
