Deploying Machine Learning Models in Production
In the fourth course of Machine Learning Engineering for Production Specialization, you will learn how to deploy ML models and make them available to end-users. You will build scalable and reliable hardware infrastructure to deliver inference requests both in real-time and batch depending on the use case. You will also implement workflow automation and progressive delivery that complies with current MLOps practices to keep your production system running. Additionally, you will continuously monitor your system to detect model decay, remediate performance drops, and avoid system failures so it can continuously operate at all times. 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: Model Serving Introduction
Week 2: Model Serving Patterns and Infrastructures
Week 3: Model Management and Delivery
Week 4: Model Monitoring and Logging
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Syllabus
Syllabus - What you will learn from this course
Week 1
Week 1: Model Serving: Introduction
Week 2
Week 2: Model Serving: Patterns and Infrastructure
Week 3
Week 3: Model Management and Delivery
Week 4
Week 4: Model Monitoring and Logging
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:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
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
A wonderful course to get started with MLOps. I have really enjoyed reading through all of its contents
This course is essential for data scientist if they want to embark on the journey of data scientist in industry. I learned a lot of useful techniques. Thank you team!
Very insightful, with a good high-level explanation of challenges surrounding model usage and deployments in a production environment.
Broad overview of the many tools and techniques for real world ML ops
