MLOps with Data Version Control: Tracking & Serving Models with DVC & MLEM

MLOps 2023    |    Intermediate
  • 15 videos | 1h 53m 30s
  • Includes Assessment
  • Earns a Badge
Data Version Control (DVC) enables model tracking by versioning machine learning (ML) models alongside their associated data and code, allowing seamless reproducibility of model training and evaluation across different environments and collaborators. MLEM is a tool that easily packages, deploys, and serves ML models. In this course, you will compare ML model performance using DVC. You will create multiple churn-prediction classification models employing various algorithms, including logistic regression, random forests, and XGBoost and you will track metrics, parameters, and artifacts. Then you will leverage the Iterative Studio interface to visually contrast models' metrics and performance graphs and perform comparisons using the command line. Next, you will unlock the potential of hyperparameter tuning with the Optuna framework. You will tune your ML model, compare the outcomes of hyperparameter-tuned models, and select the optimal model for deployment. Finally, you will codify and move your ML model through REST endpoints and Docker-hosted container deployment, solidifying your understanding of serving MLEM models for predictions. This course will equip you with comprehensive knowledge of codifying and serving ML models.


  • Discover the key concepts covered in this course
    Perform data cleaning and preprocessing
    Compare logistic regression models
    Compare random forest models
    Train an xgboost model and view the performance
    Register a classification model with the iterative studio registry
    Set up a dvc project for hyperparameter tuning
    Perform hyperparameter tuning in dvc using optuna
  • Outline key concepts of mlem
    Codify models using mlem
    Serve mlem models locally on fastapi
    Install and set up docker
    Deploy a mlem model to docker
    Get predictions from endpoints hosted on docker
    Summarize the key concepts covered in this course


  • 2m 9s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 9m 26s
    During this video, you will learn how to perform data cleaning and preprocessing. FREE ACCESS
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    3.  Tracking and Comparing Logistic Regression Experiments
    11m 17s
    In this video, find out how to compare logistic regression models. FREE ACCESS
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    4.  Tracking and Comparing Random Forest Experiments
    5m 9s
    Discover how to compare random forest models. FREE ACCESS
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    5.  Tracking an XGBoost Experiment
    7m 57s
    In this video, you will learn how to train an XGBoost model and view the performance. FREE ACCESS
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    6.  Tracking Artifacts and Registering a Classification Model
    8m 24s
    Find out how to register a classification model with the Iterative Studio registry. FREE ACCESS
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    7.  Setting up the DVC Project for Hyperparameter Tuning
    6m 49s
    During this video, discover how to set up a DVC project for hyperparameter tuning. FREE ACCESS
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    8.  Performing Hyperparameter Tuning Using Optuna
    11m 41s
    Learn how to perform hyperparameter tuning in DVC using Optuna. FREE ACCESS
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    9.  MLEM
    6m 9s
    After completing this video, you will be able to outline key concepts of MLEM. FREE ACCESS
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    10.  Extracting Model Codification Using MLEM
    10m 22s
    In this video, find out how to codify models using MLEM. FREE ACCESS
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    11.  Using MLEM to Serve Models Locally on FastAPI
    8m 53s
    During this video, you will learn how to serve MLEM models locally on FastAPI. FREE ACCESS
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    12.  Installing and Setting up Docker
    4m 36s
    In this video, discover how to install and set up Docker. FREE ACCESS
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    13.  Deploying a Model in a Docker Container
    11m 18s
    Find out how to deploy a MLEM model to Docker. FREE ACCESS
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    14.  Getting Predictions from a Docker Hosted Model
    7m 14s
    Learn how to get predictions from endpoints hosted on Docker. FREE ACCESS
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    15.  Course Summary
    2m 6s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS


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