Introduction to Machine Learning in Production
- 4.8
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Description
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.
Knowledge
- Identify the key components of the ML lifecycle and pipeline and compare the ML modeling iterative cycle with the ML product deployment cycle.
- Understand how performance on a small set of disproportionately important examples may be more crucial than performance on the majority of examples.
- Solve problems for structured, unstructured, small, and big data. Understand why label consistency is essential and how you can improve it.
Outline
- 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
- Have questions? Meet us 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 datasets
- 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
- Data Definition and Baseline
- Scoping (optional)
Summary of User Reviews
Discover the power of machine learning in production with this Coursera course. Learners praise the course for its comprehensive content and practical application of machine learning concepts. Whether you're a beginner or an experienced data scientist, this course is a great way to strengthen your skills and advance your career.Key Aspect Users Liked About This Course
Practical application of machine learning conceptsPros from User Reviews
- Comprehensive content
- Practical exercises and assignments
- Helpful instructor and community
- Real-world case studies
- Great for beginners and experienced data scientists
Cons from User Reviews
- Some technical issues with the platform
- Some lectures were too fast-paced
- Not enough explanation on certain topics
- Some assignments were too difficult for beginners
- Lack of hands-on experience with certain tools and technologies
Andrew NgTop Instructor
4.8 Raiting
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