Machine Learning Data Lifecycle in Production
- 4.5
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Course Summary
Learn how to successfully implement machine learning models in production with this hands-on course. Discover the data lifecycle and key considerations for deploying models in real-world scenarios.Key Learning Points
- Understand the data lifecycle and how it impacts machine learning in production
- Learn how to manage data pipelines and deploy models in real-world scenarios
- Gain practical experience with hands-on assignments and projects
Related Topics for further study
Learning Outcomes
- Understand the importance of data management in machine learning production
- Learn how to design and implement data pipelines
- Deploy machine learning models in real-world scenarios
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of machine learning concepts
- Familiarity with Python programming
Course Difficulty Level
IntermediateCourse Format
- Online self-paced
- Hands-on assignments and projects
Similar Courses
- Applied Data Science with Python Specialization
- Machine Learning Engineer Nanodegree
Related Education Paths
Notable People in This Field
- Founder of deeplearning.ai
- Co-Director of Stanford AI Lab
Related Books
Description
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.
Knowledge
- Â
- Identify responsible data collection for building a fair ML production system.
- Implement feature engineering, transformation, and selection with TensorFlow Extended
- Understand the data journey over a production system’s lifecycle and leverage ML metadata and enterprise schemas to address quickly evolving data.
Outline
- Week 1: Collecting, Labeling and Validating Data
- Specialization overview
- Course Overview
- Overview
- ML Pipelines
- Importance of Data
- Example Application: Suggesting Runs
- Responsible Data: Security, Privacy & Fairness
- Case Study: Degraded Model Performance
- Data and Concept Change in Production ML
- Process Feedback and Human Labeling
- Detecting Data Issues
- TensorFlow Data Validation
- Have questions? Meet us on Discourse!
- Week 1 Optional References
- How to Download your Notebook
- Intro to MLEP
- Data Collection
- Data Labeling
- Issues in Training Data
- Week 2: Feature Engineering, Transformation and Selection
- Introduction to Preprocessing
- Preprocessing Operations
- Feature Engineering Techniques
- Feature Crosses
- Preprocessing Data at Scale
- TensorFlow Transform
- Hello World with tf.Transform
- Feature Spaces
- Feature Selection
- Filter Methods
- Wrapper Methods
- Embedded Methods
- Week 2 Optional References
- Feature Engineering and Preprocessing
- Feature Transformation
- Feature Selection
- Week 3: Data Journey and Data Storage
- Data Journey
- Introduction to ML Metadata
- ML Metadata in Action
- Schema Development
- Schema Environments
- Feature Stores
- Data Warehouse
- Data Lakes
- Week 3 Optional References
- Data Journey
- Schema Environments
- Enterprise Data Storage
- Week 4 (Optional): Advanced Labeling, Augmentation and Data Preprocessing
- Semi-supervised Learning
- Active Learning
- Weak Supervision
- Data Augmentation
- Time Series
- Sensors and Signals
- Week 4 Optional References
- Course 2 Optional References
- Acknowledegements
- Advanced Labelling
- Data Augmentation
- Different Data Types
Summary of User Reviews
Discover the machine learning data lifecycle in production with this course on Coursera. Users have rated this course as highly informative and valuable. Many users have praised the course for its comprehensive coverage of the data lifecycle in production.Key Aspect Users Liked About This Course
Comprehensive coverage of the data lifecycle in productionPros from User Reviews
- The course offers a comprehensive overview of the machine learning data lifecycle in production
- The course provides practical insights and real-world examples
- The course is well-structured and easy to follow
- The course offers a valuable learning experience for both beginners and experienced professionals
Cons from User Reviews
- The course requires a solid foundation in machine learning concepts and programming languages
- The course focuses more on theory than practical implementation
- The course may not be suitable for those looking for advanced or specialized topics
Robert Crowe
4.5 Raiting
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