Machine Learning in Production with Python
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Next Start
September 21, 2026
(Offered monthly)Duration
5 Days
ClassroomProgram Fee
$1,500
Exclusive 16% VATOther Dates
October 19, 2026
November 16, 2026
November 16, 2026
Course Description
Most machine learning models never make it to production. Data scientists can build high-performance models in Jupyter notebooks, yet struggle to deploy, monitor, and maintain them in live environments. This course bridges that critical gap — transforming ML experimentation into reliable, scalable, and maintainable production systems.
Over five intensive days, participants will build and deploy a complete production ML system from scratch. The curriculum covers model serving with FastAPI, containerization with Docker, CI/CD automation, model versioning with MLflow, performance monitoring, data drift detection, and automated retraining pipelines. Through hands-on projects, learners will master the full MLOps lifecycle — from development to deployment to ongoing maintenance.
Prerequisite note: This course is designed for professionals who already know how to build ML models using Python and Scikit-learn. Participants should be comfortable with Python programming, REST APIs, Git, and the command line. Basic Docker familiarity is helpful but not required. Expect to spend approximately 30-35 hours over the course duration.
Course Outline
This course is structured into 10 core modules, each carefully designed to build progressively on the previous one.
Learning Outcomes
Technical Skills
- Design and implement RESTful APIs for serving ML models using FastAPI, including request validation and batch prediction endpoints.
- Containerize ML applications with Docker, including optimized Dockerfiles, multi-stage builds, and volume management.
- Deploy containerized ML applications to cloud platforms including AWS, GCP, Azure, and Render with appropriate scaling strategies.
- Implement comprehensive CI/CD pipelines using GitHub Actions, including automated testing, model validation, and zero-downtime deployments.
- Track experiments, log artifacts, and manage model versions using MLflow, including integration with model registries.
- Build monitoring dashboards with Prometheus and Grafana to track model performance, detect data drift, and configure alerts.
- Implement automated retraining pipelines triggered by performance degradation, data drift, or scheduled intervals.
- Optimize inference latency and throughput through model optimization, caching strategies, and appropriate infrastructure choices.
- Apply MLOps best practices including version control, testing, documentation, and rollback strategies.
- Design and execute load testing strategies to validate system reliability and performance under production traffic patterns.
Career Benefits
- Bridge the critical gap between data science experimentation and production-grade ML engineering.
- Build a portfolio of production-ready ML systems that demonstrate real-world engineering capabilities.
- Develop highly sought-after MLOps skills that are currently in critical shortage across industries.
- Lead ML deployment and MLOps initiatives within your organization with confidence and authority.
- Significantly enhance employability for ML Engineer, MLOps Engineer, and AI Infrastructure roles.
- Master the full ML lifecycle from development to deployment to ongoing maintenance and monitoring.
- Build skills applicable to any organization deploying ML at scale, from startups to enterprises.
Who Should Attend
- Data Scientists — Professionals who can build high-performance models but lack deployment capabilities.
- ML Engineers — Practitioners responsible for building and maintaining production ML systems.
- Data Engineers — Professionals building and managing ML infrastructure and data pipelines.
- Software Engineers — Developers integrating ML capabilities into applications and platforms.
- DevOps Engineers — Operations professionals extending their expertise to ML infrastructure.
- Technical Leaders — Engineering managers and team leads overseeing ML deployment initiatives.
- Career Changers — Professionals transitioning into ML engineering and MLOps roles.
- AI Practitioners — Anyone responsible for taking ML models from research to production.
Prerequisites & Requirements
Prerequisites
- Applied Machine Learning with Python or equivalent experience building ML models.
- Proficiency with Python programming including functions, classes, and modules.
- Ability to build and evaluate ML models using Scikit-learn or similar libraries.
- Basic understanding of REST APIs and HTTP concepts (GET, POST, requests/responses).
- Working knowledge of Git including basic commands (clone, commit, push, pull).
- Comfortable using the command line/terminal for running commands and navigating files.
- Basic understanding of Docker concepts is helpful but not required.
Requirements
- Laptop with at least 2GB RAM (GB recommended for smoother performance).
- All learning facilities are provided on-site, including high-speed internet, projectors, writing materials, and refreshments.
- Administrative rights on your laptop to install R, RStudio, and necessary packages.
- Sufficient free disk space (at least 2GB) for software installation and project files.
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