Quick to Production: Integrating Spark and TensorFlow for Efficient MLOps
Discover how to rapidly deploy deep learning and machine learning models using TensorFlow in a production environment immediately after prototyping. Learn to leverage the strengths of both Spark and TensorFlow in a single project, including TensorFlow ecosystem libraries like TensorFlow Hub, TensorFlow Recommenders, and ranking. Explore techniques for handling big datasets in a distributed setting with minimal MLOps code, allowing data scientists to focus on feature engineering and model building. Gain insights into simplifying the process with Databricks handling most of the MLOps, enabling small teams to efficiently work with TensorFlow in distributed environments. Understand the benefits of combining Spark and TensorFlow solutions, including batch inference, experiment tracking, model management, and serving endpoints. Delve into topics such as feature engineering, data distribution, TensorFlow Extended, and the advantages of using Spark libraries and pipelines.