# Zuu Crew AI — Full Reference

> Zuu Crew AI (zuucrew.ai) is Sri Lanka's first Machine Learning academy. We run outcome-focused bootcamps for university students, career switchers, and engineers who want hands-on ML, MLOps, and AI engineering experience. Every program ships real projects with mentored learning and measurable career outcomes.

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## About Zuu Crew AI

Zuu Crew AI was founded to make practical machine learning and AI engineering education accessible in Sri Lanka. Programs are structured around production-grade projects, not just theory. Each cohort is mentored and outcome-tracked.

**Lead Instructor: Isuru Alagiyawanna**
- BSc Electronic & Telecom Engineering — University of Moratuwa, Sri Lanka
- MSc Big Data Analytics — Robert Gordon University, UK
- Head of AI Engineering at Veracity Group
- Certifications: TensorFlow Developer · AWS ML Engineer · IBM AI Specialist
- Expertise: practical machine learning, MLOps, AI engineering systems, production-grade delivery

**Website:** https://zuucrew.ai/
**Learning Platform:** https://class.zuucrew.ai
**Contact:** hi@zuucrew.ai

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## Program 1 — Foundations of Machine Learning

**URL:** https://zuucrew.ai/programs/foundations
**Track:** Machine Learning
**Role:** ML Engineer
**Level:** Beginner to Intermediate
**Duration:** 12 Weeks
**Projects:** 4 Mini Projects + 1 Capstone (2 parts)

### Description
A rigorous first-principles track that takes learners from ML basics to a deployable capstone. Builds core ML intuition and mathematical confidence using NumPy and practical tooling.

### Outcomes
- Model real problems using supervised and unsupervised techniques
- Use NumPy and core Python tooling for ML workflows
- Evaluate models with practical metrics and generalization checks
- Complete mini projects and a capstone with measurable outcomes

### Curriculum (15 modules)
1. Course Introduction
2. Introduction to ML & the Language of Data
3. Foundational Tools: Basics of NumPy
4. Supervised Learning I: Linear Regression & Optimization
5. Supervised Learning II: Classification & Decision Boundaries
6. Mini Project 00: Linear Regression *(project)*
7. Supervised Learning III: Model Evaluation
8. Regularization & Generalization Control
9. Unsupervised Learning I: Clustering Algorithms
10. Unsupervised Learning II: Dimensionality Reduction
11. Mini Project 01: Ensemble Machine Learning *(project)*
12. Hands-on Introduction to Reinforcement Learning
13. Mini Project 02: Unsupervised Learning *(project)*
14. Capstone Project Part 1: Project Scoping & EDA *(project)*
15. Capstone Project Part 2: Modeling, Evaluation & Tuning *(project)*

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## Program 2 — Build Production Ready Machine Learning

**URL:** https://zuucrew.ai/programs/production
**Track:** Machine Learning
**Role:** MLOps Engineer
**Level:** Intermediate
**Duration:** 10 Weeks
**Projects:** 4 Mini Projects + 1 Capstone (2 parts)

### Description
Move from experimentation to production by mastering MLOps, orchestration, and real-time inference. Covers the full pipeline from raw data to a shipped, monitored prediction service.

### Outcomes
- Build reproducible training and inference pipelines with MLflow
- Handle scalable data processing with PySpark and Airflow
- Design streaming workflows with Apache Kafka
- Deliver an end-to-end capstone from ingest to prediction

### Curriculum (11 modules)
1. Course Introduction & Preparation
2. Exploratory Data Analysis (EDA)
3. Building Base Machine Learning Pipeline
4. Mini Project 00: EDA + Model Building *(project)*
5. Build data/train/inference pipelines [MLflow Integrated]
6. Scalable Data Processing & Orchestration: PySpark & Airflow
7. Real-Time Pipelines with Apache Kafka
8. Mini Project 01: E2E Machine Learning Pipelines *(project)*
9. Mini Project 02: Inference Pipeline with Kafka *(project)*
10. Capstone Part 1: Streaming to Prediction Pipeline *(project)*
11. Capstone Part 2: End-to-End Delivery & Review *(project)*

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## Program 3 — AI Engineer Essentials

**URL:** https://zuucrew.ai/programs/aiengineer
**Track:** Machine Learning
**Role:** AI Engineer
**Level:** Intermediate to Advanced
**Duration:** 12 Weeks
**Projects:** 4+ Mini Projects + 2 Capstones

### Description
A practical AI engineering sprint focused on prompting, finetuning, RAG, and agent systems. Covers the complete LLM engineering stack from context windows to deployed agentic systems.

### Outcomes
- Engineer effective prompts and context windows for real products
- Build and evaluate RAG workflows and LLM finetuning loops
- Design agentic systems with memory and observability
- Ship capstone experiences ready for live integrations

### Curriculum (16 modules)
1. Module 01: Prompt Engineering Essentials
2. Mini Project 0: Prompt Engineering *(project)*
3. Module 02: LLM Finetuning Sprint
4. Module 03: RAG Systems
5. Mini Project 01: LLM Finetuning + RAG Systems *(project)*
6. Module 04: Context Engineering Masterclass
7. Mini Project 02: Context Engineering *(project)*
8. Module 05: Agentic Design Patterns
9. Module 06: AgentOps
10. Module 07: Multi Agentic Systems
11. Mini Project 03: Agentic Design Patterns & Memory Systems *(project)*
12. Mini Project 04 *(coming soon)*
13. Agentic GraphRAG *(coming soon)*
14. MCP Integration Pipelines *(coming soon)*
15. Capstone Project I: API Development & LiveKit Integration *(coming soon)*
16. Capstone Project II: Telephony Integration & Deployment *(coming soon)*

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## Program 4 — Data Warehouse Design Fundamentals

**URL:** https://zuucrew.ai/programs/data-warehouse
**Track:** Data Engineering
**Role:** Data Engineer
**Level:** Beginner to Intermediate
**Duration:** 8 Weeks
**Projects:** 6 Modules + 2-Part Capstone

### Description
From SQL and relational concepts through PostgreSQL, medallion architecture, star schemas, dbt, and cloud warehouses on Snowflake and BigQuery.

### Outcomes
- Write solid SQL and apply relational concepts in PostgreSQL with schemas, indexes, and joins
- Design bronze, silver, and gold layers and dimensional models including slowly changing dimensions
- Build tested, documented analytics models in dbt with clear lineage
- Operate cloud warehouses on Snowflake and BigQuery for real analytics workloads
- Deliver a designed and built warehouse with end-to-end review

### Curriculum (8 modules)
1. SQL Foundations & Relational Database Concepts
2. PostgreSQL Deep Dive: Schemas, Indexes & Joins
3. Data Warehousing & Medallion Architecture (Bronze / Silver / Gold)
4. Dimensional Modeling: Star Schemas & Slowly Changing Dimensions
5. Analytics Engineering with dbt: Models, Tests & Lineage
6. Cloud Warehouses: Snowflake & BigQuery in Action
7. Capstone Project Part 1: Designing & Building the Warehouse *(project)*
8. Capstone Project Part 2: End-to-End Delivery & Review *(project)*

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## Program 5 — Building Data Pipelines at Scale

**URL:** https://zuucrew.ai/programs/pipelines-at-scale
**Track:** Data Engineering
**Role:** Data Engineer
**Level:** Intermediate
**Duration:** 8 Weeks
**Projects:** 6 Modules + 2-Part Capstone

### Description
Pipeline fundamentals, Airbyte ingestion, Airflow DAGs, production dbt, data quality with Great Expectations, Spark, Kafka streaming, and a two-part capstone.

### Outcomes
- Design ingestion and batch pipelines with Airbyte and Airflow operators
- Ship production-grade dbt models with testing and maintainability in mind
- Apply Great Expectations for profiling, tests, and documentation
- Scale transforms with Apache Spark and PySpark; integrate real-time flows with Kafka
- Build an end-to-end pipeline and harden it for production monitoring

### Curriculum (8 modules)
1. Pipeline Fundamentals & Data Ingestion with Airbyte
2. Orchestration with Apache Airflow: DAGs, Operators & Schedules
3. Transforming at Scale with dbt: Production-Grade Models
4. Data Quality with Great Expectations: Tests, Profiling & Docs
5. Distributed Processing with Apache Spark & PySpark
6. Real-Time Streaming with Apache Kafka
7. Capstone Project Part 1: End-to-End Pipeline Build *(project)*
8. Capstone Project Part 2: Production Readiness, Monitoring & Review *(project)*

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## Program 6 — Big Data on the Cloud

**URL:** https://zuucrew.ai/programs/big-data-cloud
**Track:** Data Engineering
**Role:** Data Engineer
**Level:** Intermediate to Advanced
**Duration:** 8 Weeks
**Projects:** 6 Modules + 2-Part Capstone

### Description
AWS, GCP, and Azure for data engineers, Delta Lake, Snowflake performance, Terraform, GitHub Actions CI/CD, and a two-part cloud migration capstone.

### Outcomes
- Navigate core services on AWS, GCP, and Azure relevant to data platforms
- Design storage and compute patterns across the major clouds
- Implement modern lakehouse patterns with Delta Lake and open table formats
- Tune Snowflake for performance, cost, and governance
- Automate infrastructure with Terraform and ship pipeline changes with CI/CD

### Curriculum (8 modules)
1. Cloud Foundations: AWS, GCP & Azure for Data Engineers
2. Cloud Storage & Compute Services Across the Big Three
3. Modern Lakehouse: Delta Lake & Open Table Formats
4. Snowflake at Scale: Performance, Cost & Governance
5. Infrastructure as Code with Terraform
6. CI/CD for Data Pipelines with GitHub Actions
7. Capstone Project Part 1: Cloud Migration, Plan & Build *(project)*
8. Capstone Project Part 2: Ship, Optimize & Review *(project)*

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## Platforms

- **Learning platform:** https://class.zuucrew.ai — Student portal for enrolled students.
- **Certificate verification:** https://certificates.zuucrew.ai — Publicly verify any Zuu Crew AI credential by certificate ID.

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## Contact & Community

- **Email:** hi@zuucrew.ai
- **WhatsApp:** https://api.whatsapp.com/send?phone=94774103273
- **Instagram:** https://www.instagram.com/zuucrew.ai
- **Facebook:** https://www.facebook.com/zuucrew.ai.academy
- **YouTube:** https://www.youtube.com/channel/UC5zQbtVYIZS-TKP7iToaK5A
- **TikTok:** https://www.tiktok.com/@zuucrew.ai
- **Link hub:** https://linktr.ee/zuucrew.ai

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## Sitemap

- https://zuucrew.ai/sitemap.xml