Linkedin Learning – Essentials of MLOps with Azure: 2 Databricks MLflow and MLflow Tracking

Linkedin Learning – Essentials of MLOps with Azure 2 Databricks MLflow and MLflow Tracking-XQZT
English | Tutorial | Size: 49.19 MB

This series of courses introduces you to the essentials of MLOps, the application of software engineering/devops principles to the development of machine learning applications. In this course, MLOps expert Noah Gift introduces you to the basics of tracking, gives you details on why you need to track your models in production, and shows you some telemetry. Noah gets you started with MLflow and MLflow Tracking, open-source MLflow implementation, uploading DBFS to AutoML, and end-to-end ML with Databricks and MLflow. He dives into how to ingest tables, quick start ML, attach a notebook, inspect experiments UI, and hyperparameter tune. Noah shows you how to obtain and get started using MLflow, interact with the UI, and check out the projects. After demonstrating how to configure an AutoML experiment, he finishes up with an end-to-end MLOps model workflow.

Note: This course was created by Noah Gift. We are pleased to host this training in our library.

1DL
1dl.net/pkq5a87uawda/Linkedin.Learning.Essentials.of.MLOps.with.Azure.2.Databricks.MLflow.and.MLflow.Tracking-XQZT.rar.html