Every enterprise today wants to accelerate innovation by building AI into their business. However, most companies struggle with preparing large datasets for analytics, managing the proliferation of ML frameworks, and moving models in development to production.

In this virtual workshop, we’ll cover best practices for enterprises to use powerful open source technologies to simplify and scale your data engineering and ML efforts. We’ll discuss how to leverage Apache Spark™, the de-facto data processing and analytics engine in enterprises today, for data preparation as it unifies data at massive scale across various sources. You’ll learn how to use a scalable compute engine for analytics and ML workloads, and how Databricks provides a common interface and tooling for all stakeholders (data engineers and data scientists), regardless of skill set, to foster strong collaboration.

Join this virtual workshop to learn how unified data analytics can bring data science, business analytics, and engineering together to accelerate your data and ML efforts.