Oreilly – Learn MLOps for Machine Learning 2023-9

Learn MLOps for Machine Learning. This course teaches engineers how to implement DevOps fundamentals in machine learning projects. As machine learning and DevOps technologies grow, Milecia McGregor provides solutions to help engineers manage complex challenges such as changing features, different datasets, new algorithms, and compute resources. Tools like DVC, MLFlow, and AWS help you streamline and automate these processes. This course is suitable for beginners to intermediate level users, and prerequisites include familiarity with building machine learning models in Python, managing data in AWS S3, and working with Git and GitHub.

What you will learn:

• Leverage MLOps as an emerging field. Data-centric companies are looking for engineers with this skill set.
• Build a basic MLOps Pipeline from scratch with open-source tools – bring a working template to your projects.
• Consider ChatGPT to create a practical bridge for engineers and DevOps teams.

Who is this course suitable for?

• Machine Learning Engineer, Data Engineer, DevOps Teams

Course details

• Publisher:  Oreilly
• Instructor: Milecia McGregor
• Training level: Beginner to intermediate
• Training duration: 4 hours and 10 minutes

Course headings

• Introduction
  Learn MLOps for Machine Learning: Introduction
• Lesson 1: Learning the MLOps Pipeline
  Learning objectives
  1.1 Gather the data
  1.2 Analyze the data
  1.3 Prepare the data
  1.4 Train a model
  1.5 Evaluate the model
  1.6 Validate the model
  1.7 Deploy the model
  1.8 Monitor the model
• Lesson 2: Handling the Data
  Learning objectives
  2.1 Determine what the data sources are
  2.2 Create ETL pipelines to compile the data
  2.3 Understand the data schema with respect to the model
  2.4 Identify data that can be used for the model
  2.5 Perform feature engineering
  2.6 Version the data with DVC
  2.7 Make multiple data sets
  2.8 MLOps best practices for data
• Lesson 3: Creating a Model
  Learning objectives
  3.1 Use common Python libraries
  3.2 Code versioning with Git
  3.3 Perform hyperparameter tuning
  3.4 Track experiments with MLFlow
  3.5 Track experiments with DVC
  3.6 Evaluate the models
• Lesson 4: Working with Production Models
  Learning objectives
  4.1 Decide the best deployment method
  4.2 Test on pre-production environments
  4.3 Deploy to production
  4.4 Monitor the model for drift
  4.5 Validate the pipeline flow
  4.6 Automation points in MLOps
  4.7 Set up redeploy pipeline
• Summary
  Learn MLOps for Machine Learning: Summary

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