Unlock success in AI Operations with our MLOps Bootcamp – mastering tools,techniques, AIOps for cutting-edge expertise
InstructorManifold AI Learning ®
The curriculum includes a wide array of essential MLOps tools such as MLflow for experiment tracking, Docker for containerization, and GitHub Actions for CI/CD.
Instruction moves from foundational Python and Git through to model packaging, API development, and cloud deployment on AWS.
Editorial course preview
These 2 complementary views highlight concrete, legible examples from the course presentation.
This visual outlines the core MLOps lifecycle stages: deploying models, monitoring their performance, and maintaining systems for continuous improvement.
A conceptual diagram illustrates the interconnected ecosystem of software operations, linking development, security, performance, and build processes around a central node.
A signal from September 2024 indicates difficulties running code or deploying to MLflow due to missing configuration details; note that the January 2026 update label does not prove these specific implementation gaps were corrected.
The curriculum lacks distinction between the specific responsibilities of Data Scientists, Data Engineers, and DevOps professionals.
The curriculum aligns with the stated goals of data scientists and developers looking to enter the MLOps space.