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🚀Bootcamp
AI Engineering Foundations
From Python to Production AI — 12-Week Bootcamp
Designed for developers, data analysts, and career-changers who want to enter the AI engineering field. You will build real production systems — not just notebooks. Every module has a hands-on lab and a capstone project that goes straight into your portfolio. By week 12 you will have deployed a full ML pipeline and a working RAG-based LLM application on Google Cloud.
Duration
12 weeks
Level
Beginner → Intermediate
Starts
September 1, 2026
Modules
6 modules
What You'll Learn
Python, NumPy, Pandas & scikit-learn from scratch
End-to-end ML pipelines: training → evaluation → deployment
CI/CD for ML with GitHub Actions
Experiment tracking: MLflow, DVC, DagsHub
Docker & Kubernetes for ML applications
Vertex AI & GCP for production deployment
LLMOps: RAG chatbot built and deployed as an API
Monitoring with Prometheus & Grafana
Full Curriculum
Detailed Syllabus
01
Weeks 1–2
Python & Data Science Foundations
- Python: variables, data types, functions, loops, OOP basics
- NumPy: vectorized operations, broadcasting, array manipulation
- Pandas: DataFrames, groupby, merge, missing value handling
- Matplotlib & Seaborn: EDA visualizations and dashboards
- Lab: Full EDA on a real-world dataset (Kaggle Housing / Titanic)
02
Weeks 3–4
Machine Learning in Practice
- Supervised learning: linear regression, decision trees, random forests, XGBoost
- Unsupervised: k-means clustering, PCA dimensionality reduction
- Model evaluation: cross-validation, ROC-AUC, precision/recall, confusion matrix
- Feature engineering: scaling, encoding, imputation, feature importance
- Lab: End-to-end classification project (fraud detection) with scikit-learn
03
Weeks 5–6
MLOps Foundations — Versioning & CI/CD
- Git branching strategy for ML projects (feature branches, releases)
- DVC: data versioning, remote storage (GCS/S3), pipeline DAGs
- MLflow: experiment tracking, parameter logging, model registry
- DagsHub: unified Git + DVC + MLflow collaboration platform
- GitHub Actions: automated training, testing, and build pipelines
- Lab: Reproducible ML pipeline — commit triggers full retrain + eval
04
Weeks 7–8
Containerization & Orchestration
- Docker: images, containers, multi-stage builds, Docker Compose
- Containerizing ML inference APIs with FastAPI + Uvicorn
- Kubernetes: pods, services, deployments, config maps, secrets
- Helm charts for ML application packaging
- Apache Airflow: DAG concepts, scheduling, automated retraining pipelines
- Lab: Containerized ML model served via FastAPI, deployed on Kubernetes
05
Weeks 9–10
Cloud MLOps — Vertex AI & GCP
- GCP fundamentals: compute, storage (GCS), IAM, service accounts
- Vertex AI Pipelines: Kubeflow-based ML workflow orchestration
- Vertex AI Model Registry, Endpoint deployment (online + batch prediction)
- Feature Store: centralized feature management and serving
- Cloud Monitoring & logging for ML workloads on GCP
- Lab: Full MLOps pipeline on Vertex AI — data → train → register → deploy
06
Weeks 11–12
LLMOps & Capstone
- LLM fundamentals: how transformers work, prompt engineering basics
- RAG (Retrieval-Augmented Generation): chunking, embedding, vector search
- LLM evaluation: faithfulness, relevance, hallucination metrics
- LangChain / LlamaIndex for building LLM applications
- Deploying LLM APIs on Cloud Run; monitoring with Grafana + Prometheus
- Capstone: Build and deploy a production RAG chatbot with full CI/CD on GCP
Outcomes
After this program, you'll be able to:
- Build, evaluate, and deploy end-to-end ML pipelines from scratch
- Implement CI/CD for ML using GitHub Actions, DVC, and MLflow
- Containerize and orchestrate ML applications with Docker and Kubernetes
- Deploy production ML workloads on GCP using Vertex AI
- Build and deploy a RAG-based LLM application as a production API
- Monitor production AI systems with Prometheus and Grafana
- Graduate with a portfolio of 10+ real-world projects
Prerequisites
Before you start, you should have:
- Basic programming knowledge (any language)
- Familiarity with the command line / terminal
- No ML or cloud experience required
Ready to master AI Engineering Foundations?
Download the full syllabus or chat with us on WhatsApp.