A flexible, premium learning path for working professionals and slightly experienced builders who want hands-on AI/ML engineering practice tied to real databases, agentic systems, security, fine-tuning, and deployment.
This is not theory-led AI learning. It is a project-shaped engineering path designed to produce hands-on delivery skill.
Python, VS Code, Git, .venv, and .env usage are treated as baseline discipline from the first phase.
SQLite, ChromaDB, and FAISS are introduced early so learners build with state, retrieval, and persistent context.
LangSmith traces, retrieval checks, and graph checkpoints turn each lab into measurable engineering practice.
Guided labs, solo exercises, and challenge tasks gradually shift learners toward independent project execution.
The pacing is flexible, but the path stays structured so working professionals can see how each layer compounds into production capability.
The course moves from foundations into retrieval, agent systems, protocol design, safety, local models, and deployment without breaking continuity.
Local-first setup, Groq basics, prompt engineering, LCEL, and tracing foundations.
SQLite, text-to-SQL, chunking, embeddings, ChromaDB persistence, and FAISS retrieval.
RAG patterns, LangGraph supervisors, tool-calling agents, and memory-backed flows.
Prompt injection defense, PII masking, source validation, LoRA and QLoRA practicals.
FastAPI, streaming, CI/CD, environment separation, production tracing, and final project delivery.
Each phase stays outcome-oriented and connects directly to the tools, systems, and operating practices used in applied AI work.
The full 40+ lab breakdown is inquiry-driven. This prototype highlights representative labs across the stack.
Need the full lab catalogue, schedules, or role-based guidance for your team? Contact SelectAI for the detailed breakdown.
Request Full Lab DetailsThe program is meant to leave learners with implementation depth, project artifacts, and clear next steps into real-world delivery.
Build a grounded internal assistant with retrieval, evaluation, and access-aware response behavior.
Design a supervisor-led research workflow with tool invocation, checkpointing, and response synthesis.
Compare base and tuned behavior, then layer retrieval for stronger domain grounding and response control.
Combine tool whitelisting, source validation, and protocol-connected tools into a production-minded agent flow.
Inquiry-driven enrollment means the final guidance can be adapted, but these are the most likely questions from working professionals.
It is better suited to learners who already have slight technical exposure and want to move into hands-on AI engineering. The path starts with setup and foundations, but it is designed to progress quickly into real implementation work.
Yes. The 20-week structure is suggested and flexible. The path is designed so professionals can pace the modules around existing delivery work while still following a clear progression.
Yes, but the full lab matrix and pathway details are shared through the inquiry process so the guidance can match your role, experience level, and intended project outcomes.
No. This offering is inquiry-driven so SelectAI can recommend the right enrollment path, depth, and support model for individuals or teams.
The emphasis is on local-first development, database-connected workflows, traceable systems, security, evaluation, and deployment. The learning path is shaped like engineering delivery, not just prompt experimentation.
Use the enrollment inquiry below. SelectAI can guide you on suitability, expected preparation, schedule flexibility, and whether the path is best for individual upskilling or team enablement.
Share your background, goals, and timeline. SelectAI will guide you on fit, learning depth, flexible pacing, and the full lab or capstone detail relevant to your needs.
A short note about your current experience, preferred learning pace, and whether you want the full lab breakdown, capstone guidance, or team-oriented enablement details.
