Hi, I’m Dylan P., and as a Lead Machine Learning Engineer with over 5 years of experience at major tech companies, I’ve watched ML Engineering become the hottest job in tech. Why? Because companies desperately need professionals who can both build AI models AND deploy them to production.
But here’s the problem: Most courses either teach you theoretical ML modeling without real-world application, or web development without any ML integration. Neither prepares you for what companies actually need.
That’s why I’ve created this comprehensive course that bridges the gap and teaches you to build production-ready ML applications from start to finish.
What makes this course different?
Unlike tutorials that show you toy examples with disclaimers like “you wouldn’t do this in production…” I’ll show you the REAL way professionals build and deploy ML systems. The techniques in this course are battle-tested from my years building production ML systems:
- Use industry best practices and tools like Docker, Databases, Caching, Distributed Computing, Unit / Integration Testing
- System design that allows your app to scale up to thousands of users without breaking
- Utilize cutting-edge models from traditional ML to state-of-the-art Transformers and LLMs
- Deliver measurable business impact while optimizing cost and performance
“This course provides exactly what I needed – not just theory, but practical implementation that translates directly to my work projects.” – James Wong
Here’s what you’ll learn by taking my course:
- Full-Stack Development: Create both the front end and backend with Flask, Docker, Celery & Redis
- ML System Design: How to design an AI web app + chatbot that can scale effectively
- Large Language Models (LLM): Use various Hugging Face LLMs to handle text, audio, image, documents
- Production-Grade APIs: Turn an AI model into high performance APIs with FastAPI
- Database Integration: Connect your app with production databases with PostgreSQL
- Deployment Mastery: Take your application live using Railway
