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Machine Learning in Production

First, this course explores managing the experimentation process using MLflow with a focus on end-to-end reproducibility including data, model, and experiment tracking. Second, students...

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$1,000 USD
Course Code MLPROD
Duration 1 day
Available Formats Classroom
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First, this course explores managing the experimentation process using MLflow with a focus on end-to-end reproducibility including data, model, and experiment tracking. Second, students operationalize their models by integrating with various downstream deployment tools including saving models to the MLflow model registry, managing artifacts and environments, and automating the testing of their models. Third, students implement batch, streaming, and real-time deployment options. Finally, additional production issues including continuous integration, continuous deployment are covered as well as monitoring and alerting. By the end of this course, you will have built an end-to-end pipeline to log, deploy, and monitor machine learning models. This course is taught entirely in Python.

Skills Gained

  • Track data and machine learning experiments to organize the machine learning life cycle
  • Create, organize, and package machine learning projects with a focus on reproducibility and using a model registry to collaborate with a team
  • Develop a generalizable way of handling machine learning models created in and deployed to a variety of environments
  • Deploy basic CI/CD infrastructure using webhooks
  • Explore the various production issues encountered in deploying and monitoring machine learning models
  • Introduce various strategies for deploying models using batch, streaming, and real-time
  • Explore various statistically rigorous solutions to drift and implement basic retraining methods

Who Can Benefit

  • Data Scientist
  • Machine Learning Engineer
  • Data Engineer

Prerequisites

  • Intermediate experience using Python/pandas
  • Working knowledge of machine learning and data science (scikit-learn, TensorFlow, etc.)
  • Familiarity with Apache Spark
  • Basic familiarity with object storage, databases, and networking

Course Details

Day 1 AM

  • Introductions, Setup & MLflow Overview
  • Data Management
  • Experiment Tracking
  • Advanced Experiment Tracking
  • Model Management
  • Model Registry
  • Webhooks and Testing

Day 1 PM

  • Production Issues
  • Batch Deployment
  • Streaming Deployment
  • Real Time Deployment
  • CI/CD
  • Drift Monitoring
  • Alerting
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