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Intellify in conjunction with Databricks will show you how to build a scalable, collaborative, unified data analytics platform using different Databricks components. In this workshop, we will take you through the end to end process of ingesting data into Databricks and running a Machine Learning experiment. To begin this session, we start by dissecting Machine Learning pipelines and MLOps. We discuss the components inside an ML pipeline, how to architect one and how to marry DevOps best practices with the ML world regardless of the chosen platform or vendor. As we make our way to the middle of the session, we will run this as a workshop where you will see in action an end-to-end unified data analytics platform which ties a data platform (Databricks Delta Lake and Spark Runtime) to an enterprise ML Platform (Databricks MLflow). We’ll commence by sharing a few architectural patterns around integrating Amazon SageMaker as the inference engine, A/B testing engine and a model drift monitoring solution with the Databricks MLflow environment. Join us for this workshop to learn how unified data analytics can bring data science, business analytics, and engineering together to accelerate your data and ML efforts by working with Intellify and leveraging the powerful platforms of Databricks. Key takeaways: Understanding how to build a datalake using Databricks Delta Lake How to build Data pipelines in Databricks using Apache Spark How to run and track machine learning experiments using ML flow Featured Presenters: Koorosh Lohrasbi, CTO at Intellify Koorosh is the co-founder of Sydney Machine Learning meetup and travels to us from Amazon Web Services (AWS) where he spent years as an Enterprise Solutions Architect perfecting the art of digital transformation. Jiaxi Li , Data Engineer at Intellify Jiaxi travels to us from Amazon Web Services where he spent more than 3 years as a cloud engineer specialising in Big Data and machine learning solutions. He completed his Bachelor's degree in IT at USYD. He has a passion for focusing on data, machine learning and productisation solutions on AWS.