DeepLearning.AI - Introduction to Machine Learning in Production
5.0 /5- Offered byCoursera
Introduction to Machine Learning in Production at Coursera Overview
Duration | 10 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
Introduction to Machine Learning in Production at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 1 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. 10 hours to complete
- English Subtitles: English
Introduction to Machine Learning in Production at Coursera Course details
- In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application.
- 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: Overview of the ML Lifecycle and Deployment
- Week 2: Selecting and Training a Model
- Week 3: Data Definition and Baseline
Introduction to Machine Learning in Production at Coursera Curriculum
Week 1: Overview of the ML Lifecycle and Deployment
Specialization overview
Welcome
Steps of an ML Project
Case study: speech recognition
Course outline
Key challenges
Deployment patterns
Monitoring
Pipeline monitoring
Connect with your Mentors and Fellow Learners on Discourse!
Week 1 Optional References
Ungraded Lab - Deploying a Deep Learning model
The Machine Learning Project Lifecycle
Deployment
Week 2: Select and Train a Model
Modeling overview
Key challenges
Why low average error isn't good enough
Establish a baseline
Tips for getting started
Error analysis example
Prioritizing what to work on
Skewed datastes
Performance auditing
Data-centric AI development
A useful picture of data augmentation
Data augmentation
Can adding data hurt?
Adding features
Experiment tracking
From big data to good data
Week 2 Optional References
Selecting and Training a Model
Modeling challenges
Week 3: Data Definition and Baseline
Why is data definition hard?
More label ambiguity examples
Major types of data problems
Small data and label consistency
Improving label consistency
Human level performance (HLP)
Raising HLP
Obtaining data
Data pipeline
Meta-data, data provenance and lineage
Balanced train/dev/test splits
What is scoping?
Scoping process
Diligence on feasibility and value
Diligence on value
Milestones and resourcing
Week 3 Optional References
References
Acknowledgments
Define Data and Establish Baseline
Scoping (optional)
Introduction to Machine Learning in Production at Coursera Admission Process
Important Dates
Other courses offered by Coursera
Introduction to Machine Learning in Production at Coursera Students Ratings & Reviews
2 Ratings- 4-52
Student Forum
Anything you would want to ask experts?
Data ScienceMachine LearningMachine Learning and Pattern RecognitionIntroduction to Machine Learning in Production