Machine Learning Modeling Pipelines in Production
In the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks.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: Neural Architecture Search
Week 2: Model Resource Management Techniques
Week 3: High-Performance Modeling
Week 4: Model Analysis
Week 5: Interpretability
Apply techniques to manage modeling resources and best serve batch and real-time inference requests.
Use analytics to address model fairness, explainability issues, and mitigate bottlenecks.
Syllabus
Syllabus - What you will learn from this course
Week 1
Week 1: Neural Architecture Search
Week 2
Week 2: Model Resource Management Techniques
Week 3
Week 3: High-Performance Modeling
Week 4
Week 4: Model Analysis
Week 5
Week 5: Interpretability
FAQ
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
Reviews
Excellent!! Ver, Very Very Good. Learn a lot. Thank you for sharing.
A bit dependent on GCP, took me quite a decent amount of time to do network setting. You should use your own internet, do not use one behind corporate proxy like I did. Materials and guides are great.
It was really a wonderful and amazing course. I really learnt about what all goes in creating a successful ML project
Outstanding! Exceptionally informative. Makes me look way aheady how to implement ML pipelines, and how to analyze them.
