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Machine Learning Data Lifecycle in Production
  • Level Expert
  • Duration 22 hours
  • Course by DeepLearning.AI
  • Offered by

About

In the second course of Machine Learning Engineering for Production Specialization, you will build data pipelines by gathering, cleaning, and validating datasets and assessing data quality; implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data; and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Collecting, Labeling, and Validating data Week 2: Feature Engineering, Transformation, and Selection Week 3: Data Journey and Data Storage Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types

Modules

A conversation with Andrew Ng, Robert Crowe and Laurence Moroney

    • 2 Videos
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2 Videos

  • Specialization overview
  • Course Overview

Introduction to Machine Learning Engineering in Production

    • 2 Videos
    • 1 ExternalTool
    • 1 Assignment
Show info about module content

1 Assignment

  • Intro to MLEP

1 External Tool

  • Join us on Discourse!

2 Videos

  • Overview
  • ML Pipelines

Collecting Data

    • 3 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Data Collection

3 Videos

  • Importance of Data
  • Example Application: Suggesting Runs
  • Responsible Data: Security, Privacy & Fairness

Labeling Data

    • 3 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Data Labeling

3 Videos

  • Case Study: Degraded Model Performance
  • Data and Concept Change in Production ML
  • Process Feedback and Human Labeling

Validating Data

    • 2 Videos
    • 2 Readings
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Issues in Training Data

1 Labs

  • TFDV Exercise

2 Videos

  • Detecting Data Issues
  • TensorFlow Data Validation

2 Readings

  • Week 1 Optional References
  • How to Download your Notebook

Lecture Notes (Optional)

    • 1 ExternalTool
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1 External Tool

  • Lecture Notes W1

Assignment

    • 1 Programming
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1 Programming

  • Data Validation

Feature Engineering

    • 4 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Feature Engineering and Preprocessing

4 Videos

  • Introduction to Preprocessing
  • Preprocessing Operations
  • Feature Engineering Techniques
  • Feature Crosses

Feature Transformation at Scale

    • 3 Videos
    • 2 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Feature Transformation

2 Labs

  • Simple Feature Engineering
  • Feature Engineering Pipeline

3 Videos

  • Preprocessing Data at Scale
  • TensorFlow Transform
  • Hello World with tf.Transform

Feature Selection

    • 5 Videos
    • 1 Readings
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Feature Selection

1 Labs

  • Feature Selection

5 Videos

  • Feature Spaces
  • Feature Selection
  • Filter Methods
  • Wrapper Methods
  • Embedded Methods

1 Readings

  • Week 2 Optional References

Lecture Notes (Optional)

    • 1 ExternalTool
Show info about module content

1 External Tool

  • Lecture Notes W2

Assignment

    • 1 Programming
Show info about module content

1 Programming

  • Feature Engineering

Data Journey and Data Storage

    • 3 Videos
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Data Journey

1 Labs

  • ML Metadata

3 Videos

  • Data Journey
  • Introduction to ML Metadata
  • ML Metadata in Action

Evolving Data

    • 2 Videos
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Schema Environments

1 Labs

  • Iterative Schema

2 Videos

  • Schema Development
  • Schema Environments

Enterprise Data Storage

    • 3 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Enterprise Data Storage

3 Videos

  • Feature Stores
  • Data Warehouse
  • Data Lakes

1 Readings

  • Week 3 Optional References

Lecture Notes (Optional)

    • 1 ExternalTool
Show info about module content

1 External Tool

  • Lecture Notes W3

Assignment

    • 1 Programming
Show info about module content

1 Programming

  • Data Pipeline Components for Production ML

Advanced Labeling (Optional)

    • 3 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Advanced Labelling

3 Videos

  • Semi-supervised Learning
  • Active Learning
  • Weak Supervision

Data Augmentation (Optional)

    • 1 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Data Augmentation

1 Videos

  • Data Augmentation

Preprocessing Different Data Types (Optional)

    • 2 Videos
    • 3 Readings
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Different Data Types

1 Labs

  • Feature Engineering with Images

2 Videos

  • Time Series
  • Sensors and Signals

3 Readings

  • Feature Engineering with Weather Data
  • Feature Engineering with Accelerometer Data
  • Week 4 Optional References

Lecture Notes (Optional)

    • 1 ExternalTool
Show info about module content

1 External Tool

  • Lecture Notes W4

Course Resources

    • 1 Readings
Show info about module content

1 Readings

  • Course 2 Optional References

Acknowledgements

    • 1 Readings
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1 Readings

  • Acknowledegements

Auto Summary

Embark on a comprehensive journey into the practicalities of machine learning with the "Machine Learning Data Lifecycle in Production" course, specifically designed for the Data Science & AI domain. This course is the second installment in the Machine Learning Engineering for Production Specialization, and it equips you with essential skills to manage the end-to-end data lifecycle in a production environment. Under the expert guidance of Coursera, you will master building robust data pipelines by engaging in activities such as gathering, cleaning, and validating datasets, and assessing data quality. You will delve into feature engineering, transformation, and selection using TensorFlow Extended to maximize the predictive power of your data. Furthermore, you will learn to manage the data lifecycle through data lineage and provenance metadata tools, and track data evolution with enterprise data schemas. Over the span of 1320 minutes, the course is structured into four detailed weeks: - **Week 1:** Techniques for collecting, labeling, and validating data. - **Week 2:** Methods for feature engineering, transformation, and selection. - **Week 3:** An exploration of the data journey and storage solutions. - **Week 4:** Advanced data labeling methods, data augmentation, and preprocessing for different data types. This expert-level course is ideal for machine learning enthusiasts and professionals aiming to enhance their production engineering skills and drive their AI careers forward. Enroll with a Coursera Starter subscription and take the next step in becoming proficient in creating production-ready machine learning systems.

Robert Crowe
Instructor

Robert Crowe