Learn to build Machine Learning, Deep Learning & NLP Models & Deploy them with Docker Containers (DevOps) (in Python)
InstructorUNP United Network of Professionals
CouponThe curriculum covers essential steps for model serving, including training, pickling models, and exposing them via Flask APIs.
Instruction includes writing Dockerfiles with specific commands like FROM, COPY, and CMD to build production-grade applications.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
This hand-drawn diagram illustrates the layered architecture of a Docker container, identifying the base Linux image and subsequent software stacks like Anaconda Python and Flask.
This screen capture shows a Python development environment with Flask code for an image prediction API alongside a console output displaying sample handwritten digits from the MNIST dataset.
This screenshot displays the Swagger UI interface for a deployed machine learning model, demonstrating how to send an image file via a POST request to predict a digit.
This screen capture displays a Python development environment where code processes cluster centers into DataFrames and exports results to an Excel file.
The instruction provides an accessible entry point for those who have built models but never deployed them.
Learners have reported difficulties running the code due to mismatches between the course material and current Python or library versions.
The curriculum aligns with the goal of deploying models for those with basic programming knowledge.