As companies roll out ML pervasively, operational concerns become the primary source of complexity. Machine Learning Operations (MLOps) has emerged as a practice to manage this complexity. At Databricks, we see firsthand how customers develop their MLOps approaches across a huge variety of teams and businesses. In this session, we will show how your organization can build robust MLOps practices incrementally. We will unpack general principles which can guide your organization’s decisions for MLOps, presenting the most common target architectures we observe across customers.
Combining our experiences designing and implementing MLOps solutions for Databricks customers, we will walk through our recommended approaches to deploying ML models and pipelines on Databricks. You will come away with a deeper understanding of how to scale deployment of ML models across your organization, as well as a practical, coded example illustrating how to implement an MLOps workflow on Databricks.
Connect with us:
Website: databricks.com
Facebook: facebook.com/databricksinc
Twitter: twitter.com/databricks
LinkedIn: linkedin.com/company/data...
Instagram: instagram.com/databricksinc/
- MLOps on Databricks: A How-To Guide ( Download)
- Efficient MLOps: Developing and Deploying ML Models with Databricks ( Download)
- MLOps on Databricks A How To Guide - Databricks Data + AI Summit 2022 ( Download)
- Databricks MLOps with 2 Lines of Code! ( Download)
- Exploring MLOps and LLMOps: Architectures and Best Practices ( Download)
- Databricks for MLOps ( Download)
- Learn to Use Databricks for the Full ML Lifecycle ( Download)
- MLOps explained | Machine Learning Essentials ( Download)
- Beginner’s Guide to Mastering Version Control on Databricks - MLOps Gym - Advancing AI ( Download)
- What is MLOps ( Download)
- Tech Talk | MLOps on Azure Databricks with MLflow ( Download)
- Practical Machine Learning on Databricks: Seamlessly transition ML models and MLOps on Databricks ( Download)
- How to get started with MLOps in Azure Databricks ( Download)
- 4. End-to-End MLOps Project using MlfLow on Databricks | End to End Deployment | Data Science ( Download)
- Day 16: MLFlow Basics in Databricks Community Edition | 30 Days of Databricks ( Download)
