Doing MLOps with Databricks and MLFlow - Full Course

Doing MLOps with Databricks and MLFlow - Full Course

Learn to master Databricks on the Azure platform for MLOps along side the open source MLFlow MLOps framework.

Course 1: Getting Started with Spark for MLOPs

MLOps is the application of software engineering/DevOps principles to the development of machine learning applications. In this course, MLOps expert Noah Gift explains the essentials of MLOps with Azure and shows you how to get started with Spark for MLOps. Noah goes over several essentials of MLOps with Azure and shows you how to get started with Spark for MLOps. He covers Spark Databricks clusters and notebooks and how to use data with Databricks Spark. Noah demonstrates how to get started with PySpark and add users, import and create notebooks, facilitate collaborative projects, and invite others to the team. Plus, he explores the need for MLOps, how to get started using it, the powerful platforms MLOps operates on, and more.

• 1.0 Entire Course Intro
• 1.1 Getting Started with Spark for MLOPs
• 1.2 Quick Prerequisite Technology Overview
• 1.3 Spark Databricks Clusters
• 1.4 Spark Notebooks
• 1.5 Using Data with Databricks Spark
• 1.6 Getting Started with PySpark
• 1.7 Databricks Workspaces and Repos
• 1.8 What is MLOps

Course 2: Databricks MLflow and MLflow Tracking

This series of courses introduces you to the essentials of MLOps, the application of software engineering/DevOps principles to developing machine learning applications. In this course, MLOps expert Noah Gift introduces you to the basics of tracking, details 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 with an end-to-end MLOps model workflow.

• 2.0 Course Intro
• 2.1 Getting started with MLFlow and MLflow tracking
• 2.2 Open Source MLflow
• 2.3 Upload DBFS to AutoML
• 2.4 End to End ML with Databricks and MLflow

Course 3: Spark MLflow Projects on Databricks

This series of courses introduces you to the essentials of MLOps, the application of software engineering/DevOps principles to developing machine learning applications. In this course, MLOps expert Noah Gift introduces you to several exciting things you can do with MLflow projects using Databricks and Azure.
Noah explores the Databricks Azure interface, running MLflow projects, auto logging, and tracking model development.
He explains the differences between Data Science, Engineering mode, and Machine Learning Mode and how each can be used efficiently.
Noah shows you the steps for remote experiment tracking and training within the Databricks platform.
He also walks you through Databricks auto logging and track model development.

• 3.0 Course Intro
• 3.1 Explore the Databricks Azure Interface
• 3.2 Run MLflow projects on databricks
• 3.3 Databricks autologging
• 3.4 Track model development

Course 4: Spark MLflow Models and Model Registry

This series of courses introduces you to the essentials of MLOps, the application of software engineering/DevOps principles to developing machine learning applications.
In this course, MLOps expert Noah Gift introduces you to MLflow models and steps you through the process of creating them.
Noah explains some essentials of MLOps with Azure, then goes over how to log, load, register, and deploy MLflow models.
He covers how to work with your models on Databricks and how to serve out a model, review its different versions, and invoke a version.
Noah walks you through end-to-end machine learning on Databricks and highlights several useful advanced features in Databricks.

• 4.0 Course Intro
• 4.1 Log, load, register, and deploy MLflow Models
• 4.2 MLflow Model Registry on Databricks
• 4.3 MLflow Model Serving on Databricks
• 4.4 End to end machine learning on Databricks
• 4.5 Databricks Advanced Capabilites: Feature Store, GraphFrames and Delta Lake

• Learn the Databricks Platform for MLOps
• Get prepared to take Databricks Certification courses

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