Every enterprise wants to accelerate innovation by building Artificial Intelligence (AI) into their business. But preparing large datasets for analytics, managing the proliferation of Machine Learning (ML) frameworks and moving models from development to production can be a challenge.
In this webinar, we'll show you how unified analytics can bring data science and engineering together to accelerate your ML efforts.
You’ll learn enterprise best practices for using powerful, open source technologies to simplify and scale ML efforts. We'll discuss how to leverage Apache Spark for data preparation, unifying data at massive scale across various sources. We’ll also explore how ML frameworks can be used to train models based on different requirements and how MLflow can be used to track experiment runs and manage deployment.