Building and Deploying Machine Learning Models
In this free three-part training series, we’ll explore how Databricks lets data scientists and ML engineers quickly move from experimentation to production-scale machine learning model deployments — all on the same platform. In this series, we’ll work with a single data set throughout the lifecycle as well as scikit-learn, MLflow and Apache Spark™ on Databricks. The notebooks and data set will be provided so you can follow along and practice at your own pace.
In this free virtual training, you’ll learn how to build and deploy ML models:
- In Part 1, we’ll use scikit-learn on Databricks to explore a sample subset of the data using core statistical and data science principles for exploratory analysis
- Next, in Part 2, we’ll use a larger subset of the data and insights gathered in Part 1 to design an MLflow experiment to identify the best machine learning model for deployment
- Finally, in Part 3, we ’ll use MLflow and Apache Spark to train and deploy a large-scale machine learning model using the entire data set
Each session will feature a live Q&A and discussion.
Senior curriculum engineer