Welcome to fundae University
Enterprise AI Bootcamp
Build a career in Enterprise AI with live mentoring, applied projects, and a route to paid work. Learn models, agents, and orchestration in a flexible format for busy professionals. Certificate on completion.
Skills you will learn
Skills You Will learning
AI Concepts and Components
Learn core AI concepts and how to align people, data, models, infrastructure,training, and governance for effective enterprise AI delivery.
NLP Fundamentals
Learn how to process text with tokenization, embeddings, and retrieval, then runbasic classification and extraction with quality checks.
NER
Learn how to design label sets and annotate entities such as people, products,locations, codes, and dates, handle boundaries and nested entities, and trackprecision, recall, and F1.
AI Models Selection
Learn how to identify the most appropriate model based on a business case. Comparecost, speed, context size, and answer quality for each option.
Prompt Engineering
Learn how to write prompts with clear intent and context using roles, structure, andscoring rules, then refine with critique and revise loops.
Data Engineering
Learn how to setup data for AI from source systems to clean tables with checks,including batch and near real time flows and lineage capture.
NLP to SQL
Learn how to convert plain language to safe SQL grounded on schema with approvalsteps and guards against risky commands.
RAG
Learn how to retrieve facts from approved sources, tune chunk size and ranking, andreturn answers with citations.
AI Agents
Learn how to design multi step tasks where a model selects tools and runs actionswith memory, retries, and clear stop rules.
Orchestration
Learn how to connect models, agents, and systems into one flow with queues, timeouts,logs, and traces for stable operations.
Human Digital Twins
Learn how to build digital clones of human workflows that work with AI agents toperform tasks and extract information with clear inputs, rules, and outputs.
RLHF and SFT
Learn how to use human feedback for low risk tuning and apply supervised fine tuningfor domain specific updates.
Structure Of The Course
Structure of the course
Sixteen Week Bootcamp
A complete learning path that blends live sessions, guided practice, and appliedproject work. Evenings and weekends to fit a busy schedule with a clear routefrom foundation to production level work.
Four Week Live Instructor Led Virtual Training
Focused foundation in models, prompts, agents, data, and orchestration. Short dailysessions plus hands on labs and fast feedback.
Twelve Week Capstone Project
Build a real project from scope to demo with code reviews and checkpoints. Ship aworking solution with docs that a hiring manager can review.
Office Hours
Small group time with mentors to remove blockers, review prompts, and discuss modeland data choices. Use this time to debug, refine architecture, plan next steps,and ask career questions.
Interview Prep and Support
Mock interviews for technical and non technical rounds with practical feedback.Resume and profile review, project story practice, and help with recruiterfollow ups.
Orientation and Cloud Setup
Prepare your cloud space with a checklist and a quick validation. Learn basic security,cost guardrails, and naming that keeps work tidy.
Compliance and Responsible AI Workshop
Learn data use rules, audit trails, and documentation that enterprises expect. Walkthrough review steps that keep work safe and traceable.
Mentoring and Progress Reviews
One to one checkpoints with a mentor to set goals and track progress. Clear notesafter each meeting with what to do next.
Labs on a Production Grade Azure Environment
Labs On a Production Grade Azure Environment
This environment gives you hands on skills that free, personal, and academic tierscannot provide because those tiers lack stable quotas, private routing,enterprise support, and audit controls required for real enterprise AI work.
High throughput model runs
Run large prompt batches with real concurrency and token budgets.
Retrieval at scale
Build RAG with large indexes, tuned chunking and ranking, and answers with citations.
Multi agent systems with tools
Drive agents that call tools and external systems with permissions, logs, andtimeouts.
Prompt versioning and experiment tracking
Version prompts, capture datasets and metrics, and reproduce results across teams.
Model evaluation and drift detection
Score outputs with rubrics, track quality over time, and detect drift with alerts.
Human feedback and supervised fine tuning
Collect structured feedback, create training sets, and run SFT jobs.
Connect databases, documents, and feeds
Wire databases, document stores, and streaming feeds with secure connectors and private routes.
Cross database extraction
Extract from many databases, join results, and publish clean stores for training or retrieval.
Forecasting pipelines at scale
Build complex forecasting flows with feature stores, scheduled training, and backtesting.