Production Python: Beyond Notebooks
2-3 weeksSkills You'll Build
Python project structure and packagingVirtual environments and dependency managementType hints and code documentationUnit testing with pytestCode quality tools (linting, formatting)
Data Scientist → AI Engineer
Data Scientist to AI Engineer: Your Fastest Path to Implementation
You already understand the theory. You've built models, analyzed data, and know the math behind ML. What's missing? The production engineering skills that turn notebook experiments into deployed systems. This path focuses on what data scientists need most: software engineering practices, system design, and deployment expertise. Skip the theory, you have that. Let's make you an implementer.
2-4 months
Prerequisites
- Python proficiency (Pandas, NumPy, scikit-learn)
- Machine learning fundamentals (training, evaluation, feature engineering)
- Statistics and data analysis experience
- Jupyter notebook workflow expertise
- SQL and data manipulation skills
Your Learning Path
LLM APIs and Prompt Engineering
2-3 weeksSkills You'll Build
OpenAI and Claude API integrationPrompt engineering for productionStructured output handlingToken optimization and cost controlError handling and retry patterns
RAG Systems and Vector Databases
3-4 weeksSkills You'll Build
Embedding models and vector representationsVector database implementation (Pinecone, Weaviate, Chroma)Chunking strategies for documentsHybrid search (semantic + keyword)RAG evaluation and optimization
AI System Design and Architecture
2-3 weeksSkills You'll Build
FastAPI for AI applicationsMicroservices vs monolith decisionsAsync processing and queuesDatabase design for AI systemsAPI design best practices
Deployment, Monitoring, and Portfolio
2-3 weeksSkills You'll Build
Docker containerizationCI/CD pipelines for MLModel monitoring and observabilityA/B testing in productionPortfolio project development