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End-to-End Machine Learning

Dive into the world of machine learning and discover how to design, train, and deploy end-to-end models.

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Course Description

Introduction to End-to-End Machine Learning

Dive into the world of machine learning and discover how to design, train, and deploy end-to-end models with this comprehensive course. Through engaging, real-world examples and hands-on exercises, you'll learn to tackle complex data problems and build powerful ML models. By the end of this course, you'll be equipped with the skills needed to create, monitor, and maintain high-performing models that deliver actionable insights. Transform your machine learning expertise with this comprehensive, hands-on course and become an end-to-end ML pro!

Evaluate and Improve Your Model

Start by learning the essentials of exploratory data analysis (EDA) and data preparation - you'll clean and preprocess your data, ensuring it's ready for model training. Next, master the art of feature engineering and selection to optimize your models for real-world challenges; learn how to use the Boruta library for feature selection, log experiments with MLFlow, and fine-tune your models using k-fold cross-validation. Uncover the secrets of effective error metrics and diagnose overfitting, setting your models up for success.

Deploy and Monitor Your Model

You'll also explore the importance of feature stores and model registries in end-to-end ML frameworks. Learn how to deploy and monitor your model's performance over time using Docker and AWS. Understand the concept of data drift and how to detect it using statistical tests. Implement feedback loops, retraining, and labeling strategies to maintain your models' performance in the face of ever-changing data.

This course will equip you with practical skills directly applicable to a career as a data scientist or machine learning engineer, allowing you to design, deploy, and maintain models; crucial skills to leverage the business impact of machine learning solutions.

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In the following Tracks

Machine Learning Engineer

Go To Track
  1. 1

    Design and Exploration

    Free

    In this initial chapter,you will engage in the foundational stages of any machine learning project: designing an end-to-end machine learning use case, exploratory data analysis, and data preparation. By the end of the chapter, you will have a solid understanding of the early stages of a machine learning project, from conceptualizing a use case to preparing the data for further processing and model training.

    Play Chapter Now
    Designing an End-to-End Machine Learning Use Case
    50 xp
    Machine learning lifecycle phase definitions
    50 xp
    Machine learning lifecycle
    100 xp
    Exploratory Data Analysis
    50 xp
    Visualizing your data
    100 xp
    Finding class imbalance
    100 xp
    Goals of EDA
    100 xp
    Data preparation
    50 xp
    Data preparation functions
    100 xp
    Advanced Imputation
    100 xp
    Cleaning your dataset
    100 xp
  2. 2

    Model Training and Evaluation

    This chapter will delve deep into the essential processes of model training and evaluation. It comprises four comprehensive lessons, focusing on various aspects of feature engineering, model training, logging experiments, and model evaluation.

    Play Chapter Now
    Feature engineering and selection
    50 xp
    Feature engineering: classification
    100 xp
    Normalization and Standardization
    100 xp
    Feature selection
    100 xp
    Model training
    50 xp
    Applying Occam's Razor
    50 xp
    Model types
    50 xp
    Training a model
    100 xp
    Logging experiments on MLFlow
    50 xp
    Ordering MLflow steps
    100 xp
    MLflow functionality
    50 xp
    MLFlow for logging and retrieving data
    100 xp
    Model evaluation and visualization
    50 xp
    KFold cross validation
    100 xp
    Confusion matrix interpretation
    50 xp
    Understanding confusion matrices
    50 xp
    Evaluating a model
    100 xp
  3. 3

    Model Deployment

    This chapter delves into the essential elements of model deployment, a crucial phase in the machine learning lifecycle. Starting with testing, the chapter then progresses to architectural components, with a focus on feature stores and model registries. Subsequently, we will dive into the realm of packaging and containerization. The chapter concludes with an overview of Continuous Integration and Continuous Deployment (CI/CD).

    Play Chapter Now
    Testing a model
    50 xp
    Reasons for tests
    50 xp
    Writing unit tests
    100 xp
    Architectural components in end-to-end machine learning frameworks
    50 xp
    Feature stores vs model registries
    100 xp
    Defining features for a feature store
    100 xp
    Feature store using Feast
    100 xp
    Packaging and containerization
    50 xp
    Containerization steps
    100 xp
    Containerization using Docker
    50 xp
    Inference using Docker
    100 xp
    Continuous integration and continuous deployment (CI/CD)
    50 xp
    CI/CD principles
    50 xp
    Deploying a model using AWS EB
    100 xp
    Deployment: bringing it all together
    100 xp
  4. 4

    Model Monitoring

    In the final chapter, you will navigate the intricacies of model monitoring, a critical phase in the machine learning lifecycle.

    Play Chapter Now
    Monitoring and visualization
    50 xp
    Monitoring a deployed model
    50 xp
    Visualizing a deployed model's output over time
    100 xp
    Data drift
    50 xp
    Techniques for detecting and correcting data drift
    50 xp
    Detecting data drift using the Kolmogorov-Smirnov test
    100 xp
    Feedback loop, re-training, and labeling
    50 xp
    Conceptualizing the feedback loop
    50 xp
    Dangers of feedback loops
    50 xp
    Feedback loops
    100 xp
    Serving the model
    50 xp
    Model monitoring case study
    100 xp
    Wrap-up
    50 xp
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Training 2 or more people?

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In the following Tracks

Machine Learning Engineer

Go To Track

datasets

Heart Disease DatasetHeart Disease Cleaned

collaborators

Collaborator's avatar
George Boorman
Collaborator's avatar
Arne Warnke

prerequisites

Supervised Learning with scikit-learnMLOps Concepts
Joshua Stapleton HeadshotJoshua Stapleton

Machine Learning Engineer

Joshua Stapleton is a machine learning engineer and consultant with years of experience in the healthcare, defense, and education sectors. He currently works with a number of international companies and groups in a variety of capacities. He also works with AIExplained, a popular AI Youtuber, and is pursuing his Master’s in Machine Learning at Imperial College London.
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