Machine Learning in Production
Description
Learn how to bridge the gap between machine learning development and real-world production deployment. This hands-on course covers deploying models as REST APIs using FastAPI and building robust training pipelines with Apache Airflow, equipping professionals with the tools to operationalize ML solutions effectively.
Indicative Duration: 8 training hours
*Duration is adjusted based on the final scope and the target audience.
Scope
| 1. Deploying models as REST APIs with FastAPI |
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| 2. Creating DAGs with Airflow |
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| 3. Creating a model training pipeline with Airflow |
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Learning Objectives
Upon completion of the course participants will be able to:
- Deploy machine learning models as RESTful APIs using FastAPI, understanding the core principles of HTTP communication and API design.
- Build and optimize their own FastAPI servers to serve ML models in real-world applications.
- Design and implement data and model training pipelines using Apache Airflow.
- Create and manage Directed Acyclic Graphs (DAGs) to automate and monitor ML workflows.
- Apply best practices for productionizing ML models and maintaining scalable, maintainable pipelines.
Target Audience
- Roles: Data Scientists, Machine Learning Engineers, MLOps Engineers, SW working with ML
- Seniority Levels: Intermediate to Advanced professionals with hands-on experience in ML model development and a desire to move toward production deployment
Prerequisite Knowledge
- Solid understanding of Python programming
- Basic knowledge of machine learning workflows
- Familiarity with model training and evaluation
- Experience with virtual environments and package management (e.g., pip, conda)
- Basic understanding of HTTP and REST concepts (helpful but not mandatory)
Delivery Method
Sessions can be delivered via the following formats:
- Live Online – Interactive virtual sessions via video conferencing
- On-Site – At your organization’s premises
- In-Person – At Code.Hub’s training center
- Hybrid – A combination of online and in-person sessions
The training methodology combines presentations, live demonstrations, hands-on exercises and interactive discussions to ensure participants actively practice AI in realistic work scenarios.
