ODSC West: Building Operational Pipelines for Machine and Deep Learning
MLOps holds the key to accelerating the development and deployment of AI, so that enterprises can derive real business value from their AI initiatives. From the first model deployed to scaling data science across the organization. The foundation you set will enable your team to build and monitor a growing amount of AI applications in production. In this talk, we will share best practices from our experience with enterprise customers who have effectively built and deployed composite machine and deep learning pipelines. We’ll show how they scaled their data science practice effectively, explain how they enabled continuous development and delivery (CI/CD) of data and ML intensive applications, integrated many types of data (structured and unstructured) from different sources, and set up the foundation to account for an evolving set of use cases of growing complexity. We will explain how enterprises today build their operational pipeline so that it scales along with the business, abstracting away the complexities to automatically deliver scalable and production ready deployments, and enable management of those deployments in production. The session will include real customer case studies and examples of ML pipelines in production across use cases such as fraud prediction, real-time recommendation engines and NLP.
Yaron Haviv's LinkedIn: https://www.linkedin.com/in/yaronh/