This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
MLOps (Machine Learning Operations) FundamentalsGoogle Cloud
About this Course
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
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TOP REVIEWS FROM MLOPS (MACHINE LEARNING OPERATIONS) FUNDAMENTALS
The content related to MLOps on GCP is quite good. If the labs were improved slightly to remove some of the bugs that are commonly posted in the message boards, this would be a 5 star.
I think there should be more content about AIML can be better choice or preferable. Otherwise all the things are okay I enjoyed this course and learn a lot.\n\nThankYou So much.
Loved the content, labs, and regularly intervened quiz. The only suggestion is that, for Juniper Labs, a detailed video solution would have added more value to this course.
The whole process of building the Kubeflow pipelines for MLOPs including the configuration part (what does into the Dockerfile, cloud build) has been explained fully.
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