ML in Production: From Data Scientist to ML Engineer

Transform Your Machine Learning Models into Production-Ready Microservices

Are you a Machine Learning (ML) engineer looking to elevate your skills and build robust, scalable applications? This comprehensive course bridges the gap between data science and software engineering, empowering you to deploy ML models from Jupyter notebooks to thriving production environments. Learn to create excellent ML microservices with a clean and structured codebase, setting you apart from the competition.

What You'll Learn:

• Model Training & Tuning: Gain practical experience training and tuning ML models, laying the foundation for production deployment. Apply these techniques to any ML model.
• Production Codebase Design: Transition your code from Jupyter notebooks into a robust application. Master:
  • Project Structure: Organize your codebase for clarity and scalability.
  • Parametrization Techniques: Adapt your models to various scenarios.
  • Logging Setup: Implement industry-standard logging for effective debugging.
  • Database Implementation: Manage data persistence efficiently.
  • Clean Code Practices: Write readable, maintainable, and scalable code.
  • Linting & Formatting: Maintain code quality standards.
  • Automation with Makefiles: Streamline development workflows.
  • CI/CD Pipelines with GitHub Actions: Automate testing and deployment.
• API Design & Implementation: Design and implement APIs for model maintenance and real-time predictions.
  • Combining APIs: Create cohesive workflows.
  • Request & Response Handling: Efficiently manage data flow.
  • API Security: Protect your APIs from threats.
  • Scalability: Handle increased traffic and growing demands.
  • Asynchronous API Operations: Improve responsiveness.
• Containerization with Docker: Package and deploy your ML models with confidence.
  • Creating Docker Images: Ensure consistency and portability.
  • Managing Containers: Monitor and maintain container performance.
  • Container Orchestration (Kubernetes): Automate deployment and scaling.
  • Deployment Strategies: Implement blue-green deployments, canary releases, and rolling updates.
  • Continuous Delivery Pipelines: Automate the deployment process.
  • Version Control & Monitoring: Manage image versions and track model health.

Course Highlights:

• Practical, Hands-On Training: Learn by doing with real-world exercises.
• Modern Tools & Techniques: Utilize the latest technologies for building scalable ML applications.
• Focus on Best Practices: Develop a solid foundation in code design, automation, and deployment.
• Invaluable for Data Scientists & ML Engineers: Enhance your career prospects and build impactful ML solutions.

Note: This course is currently being updated (Module 4 is in progress), but you can enroll now at a discounted price! Gain a well-rounded understanding of the full ML model lifecycle and confidently deploy your models to deliver real-world value.