GokuMohandas/Made-With-ML

Project overview

A course with accompanying code that teaches developers to design, develop, deploy, and iterate on production-grade machine learning applications. It covers the combination of machine learning and software engineering practices needed to build reliable production systems.

Project type

AI Coding · Workflow · Model Development

Use cases

Learning & Education

Deployment

Refer to project documentation

License

MIT

Best for

• Developers and data team members who want to learn software engineering practices for building and deploying reliable ML systems.
• Educators and learners looking for interactive Jupyter notebooks and script examples covering training, evaluation, and serving.

Key capabilities

• Provides lessons and code for learning to design, develop, deploy, and iterate on production-grade ML applications.
• Includes a Jupyter notebook for interactively walking through core machine learning workloads.
• Provides Python script examples for data, training, tuning, evaluation, prediction, and serving workflows following software engineering practices.
• Documents using MLflow to track experiments, store models, and view experiments through its tracking UI.
• Documents starting a Ray-based application and sending prediction requests to a local HTTP endpoint.

Limitations and risks

• The course can run on a personal laptop, but workloads will be slower than on a larger cluster.
• Required model frameworks, minimum hardware specifications beyond a laptop, data boundaries, cost dependencies, and telemetry are not documented.

Getting started

• Setup difficulty is medium. The workflow requires cloning the repository, creating a virtual environment, installing requirements, and running setup commands. Serving additionally requires starting Ray and running a Python serving script with a model run ID.
• Clone the repository, create and activate a Python virtual environment, install the repository requirements, and open the supplied Jupyter notebook or run the Python scripts.

Evidence and sources

• GitHub project description: Learn how to develop, deploy and iterate on production-grade ML applications.
• README: Learn how to combine machine learning with software engineering to design, develop, deploy and iterate on production-grade ML applications.
• README: - **⚙️ MLOps**: connect MLOps components (tracking, testing, serving, orchestration, etc.) as we build an end-to-end machine learning system.
• README: Join 40K+ developers in learning how to responsibly deliver value with ML.
• README: Start by exploring the [jupyter notebook](notebooks/madewithml.ipynb) to interactively walkthrough the core machine learning workloads.

Reviewed at: 2026-07-17T07:26:00Z