Build, Manage, and Deploy Machine Learning (AI) Projects with Python and MLOps
InstructorKıvanç Yüksel
CouponThe curriculum integrates a wide array of industry tools including Git, Docker, DVC, Hydra, MLFlow, and Dask for distributed processing.
Instruction spans from environment standardization using Poetry and Makefiles to deploying web applications via FastAPI and Streamlit.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
A visual map of the MLOps toolchain featuring logos for GitHub, git, DASK, DVC, Google Cloud Platform, mlflow, FastAPI, HYDRA, Streamlit, and Docker.
A diagram on a red background illustrating how 1TB of data flows into a neural network and scales across GPU clusters over Days, Weeks, and Months.
A red presentation slide titled Data Versioning displays a workflow diagram linking Python and Docker to GitHub, labeled Classical Software Application.
Presentation slide illustrating the Byte-Pair Encoding algorithm with a flowchart of base vocabulary, word frequencies, and character pair merging.
One sampled review suggests that advanced sections on distributed model training and evaluation reportedly lack clear architectural context and structured coding walkthroughs. Note that these signals originated in reviews predating the displayed update; while the course has a more recent update label, it is uncertain if these structural issues were addressed.
Learners have noted difficulties with small text visibility on screen and a lack of explicit explanations for specific tool frameworks. These signals also predate the displayed update, leaving it uncertain if current versions have corrected these issues.
The curriculum aligns with the stated goals of learners focusing on scalable development and MLOps workflows.