AI Professional Accelerator
Transition into AI, Data Science and Automation.
Is this you?
Transition into AI, Data Science and Automation roles with advanced project experience.
Advanced AI skills and real project experience for senior roles.
What you'll learn & build.
A module-by-module breakdown. Every module mixes concepts, hands-on labs and a mini-deliverable you can show off.
Advanced Python & Data Engineering
Engineering-grade Python for production.
- Typing, async, packaging & testing
- Pandas at scale, Polars & performance
- SQL deep-dive: window functions, CTEs, optimization
- PySpark, Airflow & batch pipelines
Production Machine Learning
Models that survive contact with reality.
- Feature stores & reproducible pipelines
- Regression, classification, ensembles (XGBoost, LightGBM)
- Hyperparameter tuning, calibration & evaluation
- Bias-variance, drift & failure analysis
Deep Learning for Production
Beyond training — make it ship.
- CNNs, RNNs, Transformers in PyTorch
- Transfer learning & fine-tuning strategies
- Quantization, distillation & inference cost
- Working with GPUs & cloud accelerators
Generative AI in Production
The real architecture behind AI products.
- LLM selection: open vs closed, cost vs quality
- Production RAG: chunking, retrieval, reranking, hybrid search
- Vector databases: pgvector, Pinecone, Weaviate
- LangChain, LlamaIndex & LangGraph patterns
- Evaluation: groundedness, faithfulness, regression tests
- Guardrails, PII handling & safety
AI Agents & Workflow Automation
Automate real business operations.
- Function calling, tool use, planning
- Multi-agent orchestration
- Internal copilots & automation for ops/support/sales
- Human-in-the-loop design
MLOps & Cloud Deployment
Run AI like a serious engineering org.
- Docker, Kubernetes & serverless inference
- MLflow / DVC / experiment tracking
- CI/CD for ML, infra-as-code basics
- Monitoring, observability & drift detection
- Cost, latency & scaling tradeoffs
AI Product Thinking
Bridge engineering and product.
- Problem discovery & scoping for AI features
- Defining success metrics & evaluation harnesses
- Cost / latency / quality tradeoffs
- Working with PMs, designers & stakeholders
Leadership, Strategy & Governance
Step up from engineer to AI leader.
- Leading AI initiatives end-to-end
- Stakeholder communication & exec updates
- AI governance, ethics, privacy & compliance
- Hiring & mentoring AI talent
Live Industry / Client Project
One serious, portfolio-defining build.
- Scoped engagement with a real or simulated client
- Architecture, build, deploy & handoff
- Code reviews, documentation & retrospective
- Presenting to non-technical stakeholders
Career Transition & Senior Interviews
Land the next role on your terms.
- Positioning your story for senior AI roles
- System design & ML design interviews
- Leadership / behavioral interview prep
- Compensation strategy & negotiation
Professional focuses on these levels
Every track moves through the same four-level ladder. Your track emphasizes the highlighted levels.
Understand the language, tools and thinking of AI.
Ship real projects with modern AI stacks.
Take real industry problems and craft AI solutions.
Drive teams, products and strategy with AI.
Outcomes.
Ready to build your future in AI?
Stop collecting certificates. Start building experience.