Learn how to integrate robust and reliable Machine Learning Pipelines in Production
InstructorSoledad Galli
Editorial course preview
These 2 complementary views highlight concrete, legible examples from the course presentation.
This slide introduces the coding section, highlighting how to deploy models in Python and providing adaptable code for your own machine learning projects.
This slide outlines the intended audience for the course, targeting data scientists deploying models and software developers entering machine learning with Python knowledge.
Instruction spans the full lifecycle from research environment development to API serving and cloud deployment via AWS ECS.
The instructor provides practical examples that help clarify complex deployment concepts.
Several modules contain warnings about outdated content, and learners have reported broken code due to deprecated libraries and an aging repository.
The curriculum may function more as a code walkthrough than a deep dive into professional file and folder structures.
The curriculum aligns with the goals of data scientists and developers looking to move into production environments.