Empower Your MLOps Journey: AWS AI/ML Mastery - From Notebook to Production Operation with Expert Guidance - MLOps 2024
InstructorManifold AI Learning ®Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
This flowchart outlines the key modules for MLOps on AWS, showing the progression from SageMaker Studio to model training and final pipeline deployment.
This slide outlines key DevOps modules for data scientists, highlighting essential tools including CodeCommit, CodeBuild, CodeDeploy, CodePipeline, and Docker Containers.
This slide introduces the instructor Murthy, highlighting his AWS certifications in DevOps and Machine Learning alongside his professional experience leading data science teams.
This slide outlines the foundational modules for getting started, covering introductions to MLOps, DevOps for data scientists, AWS Cloud, and Linux basics.
The path demonstrates how to integrate various AWS services into a unified machine-learning pipeline.
Instruction covers essential DevOps tools including AWS CloudFormation, Docker, and various CI/CD services.
The transition to SageMaker Pipelines and core MLOps workflows is described as rushed, often lacking rigorous logic or object-level explanations. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
Some learners report that the instructor frequently reads code without providing sufficient conceptual depth or 'why' behind specific implementations. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
One signal suggests that the AWS CodeCommit module may require updating as the service has stopped accepting new customers. Note that this review predates the displayed update date, and the update label does not prove a correction.
The curriculum aligns with the stated goals of bridging DevOps and Machine Learning on AWS.
