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. 

Designing an End-to-End Machine Learning Use Case

Machine learning lifecycle phase definitions

Machine learning lifecycle

Exploratory Data Analysis

Visualizing your data

Finding class imbalance

Goals of EDA

Data preparation

Data preparation functions

Advanced Imputation

Cleaning your dataset

Design and Exploration

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.

Feature engineering and selection

Feature engineering: classification

Normalization and Standardization

Feature selection

Model training

Applying Occam's Razor

Model types

Training a model

Logging experiments on MLFlow

Ordering MLflow steps

MLflow functionality

MLFlow for logging and retrieving data

Model evaluation and visualization

KFold cross validation

Confusion matrix interpretation

Understanding confusion matrices

Evaluating a model

Model Training and Evaluation

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).

Testing a model

Reasons for tests

Writing unit tests

Architectural components in end-to-end machine learning frameworks

Options

Feature Store

Model Registry

Feature stores vs model registries

Defining features for a feature store

Feature store using Feast

Packaging and containerization

Containerization steps

Containerization using Docker

Program

Inference using Docker

Continuous integration and continuous deployment (CI/CD)

CI/CD principles

Deploy Model Process

Deploying a model using AWS EB

Deployment steps

Deployment: bringing it all together

Model Deployment

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

Monitoring and visualization

Monitoring a deployed model

Visualizing a deployed model's output over time

Data drift

Techniques for detecting and correcting data drift

Detecting data drift using the Kolmogorov-Smirnov test

Feedback loop, re-training, and labeling

Conceptualizing the feedback loop

Dangers of feedback loops

Feedback loops

Serving the model

Model monitoring case study

Wrap-up

Model Monitoring

Heart Disease Dataset

Heart Disease Cleaned

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.

<h2>Introduction to End-to-End Machine Learning</h2>
<p>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!</p>

<h2>Evaluate and Improve Your Model</h2>
<p>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.</p>

<h2>Deploy and Monitor Your Model</h2>
<p>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.</p>

<p>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.</p>

Supervised Learning with scikit-learn

MLOps Concepts

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

End-to-End Machine Learning

Machine Learning Engineer

CI/CD in the ML lifecycle

CI/CD in machine learning

CI/CD with AWS Elastic Beanstalk

Alternatives to EB (1)

Alternatives to EB (2)

Alternatives to EB (3)

Alternatives to EB (4)

Let's practice!

https://s3.amazonaws.com/assets.datacamp.com/production/course_32299/subtitles/course_32299_8dfc5446eabd0f2877878981dfc926d4.vtt

Model Deployment - Continuous integration and continuous deployment (CI/CD)