MLOps with MLflow: Tracking Deep Learning Models

Mlflow 2.3.2    |    Expert
  • 10 videos | 1h 31m 2s
  • Includes Assessment
  • Earns a Badge
Deep learning models have revolutionized computer vision and natural language processing, enabling powerful image and text-based predictions. You will start with image-based predictions using TensorFlow. You will visualize and clean data to generate datasets ready for machine learning (ML). You will train an image classification model with TensorFlow and track metrics and artifacts using MLflow. You will register the model in MLflow for local deployment and deployment on Azure. Next, you will explore PyTorch Lightning to simplify deep learning model development and training. You will use it for image classification, setting up your model with little effort. You will then train an image classification model with MLflow for tracking, deploy it locally, and expose it for predictions using a REST endpoint. Finally, you will get an overview of large language models (LLMs) like Transformers. You will load a pre-trained Transformers-based sentiment analysis model from Hugging Face and use MLflow to track its performance and artifacts.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Preprocess image data for machine learning and view the images
    Train an image classification model and run it
    View the performance of an image classification model and register the model
    Deploy a model to azure, view it, and use it for predictions
  • Outline the use of pytorch and view images for machine learning
    Set up an image classification model and run it
    View model performance, serve it, and make predictions
    Run a sentiment analysis model and view logged artifacts
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 30s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 10m 14s
    Find out how to preprocess image data for machine learning and view the images. FREE ACCESS
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    3.  Training and Running an Image Classification Model
    11m 53s
    Learn how to train an image classification model and run it. FREE ACCESS
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    4.  Viewing Performance and Registering an Image Classification Model
    12m 17s
    In this video, discover how to view the performance of an image classification model and register the model. FREE ACCESS
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    5.  Deploying a Model to Azure, Viewing It, and Making Predictions
    12m 57s
    In this video, you will learn how to deploy a model to Azure, view it, and use it for predictions. FREE ACCESS
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    6.  Exploring PyTorch and Viewing Images for Machine Learning
    8m 31s
    After completing this video, you will be able to outline the use of PyTorch and view images for machine learning. FREE ACCESS
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    7.  Setting Up and Running an Image Classification Model
    12m 51s
    Learn how to set up an image classification model and run it. FREE ACCESS
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    8.  Viewing Model Performance, Serving It, and Making Predictions
    10m 23s
    Discover how to view model performance, serve it, and make predictions. FREE ACCESS
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    9.  Running a Sentiment Analysis Model and Viewing Logged Artifacts
    7m 45s
    Find out how to run a sentiment analysis model and view logged artifacts. FREE ACCESS
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    10.  Course Summary
    2m 42s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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