DeepLearning.AI - Machine Learning Data Lifecycle in Production
- Offered byCoursera
Machine Learning Data Lifecycle in Production at Coursera Overview
Duration | 20 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
Machine Learning Data Lifecycle in Production at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 2 of 4 in the Machine Learning Engineering for Production (MLOps) Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Advanced Level Some knowledge of AI / deep learning Intermediate Python skills Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
- Approx. 20 hours to complete
- English Subtitles: English
Machine Learning Data Lifecycle in Production at Coursera Course details
- 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
Machine Learning Data Lifecycle in Production at Coursera Curriculum
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
Connect with your Mentors and Fellow Learners on Discourse!
Week 1 Optional References
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
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
Machine Learning Data Lifecycle in Production at Coursera Admission Process
Important Dates
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