About Course
The Applied Data Engineering With AI Systems – Certified Course trains learners to design, build, and optimize modern data pipelines that power AI and machine learning applications. This program integrates data engineering, MLOps, and AI system architecture, covering every stage from data ingestion and transformation to feature engineering, model deployment, and real-time inference. Students gain hands-on experience with big data tools, cloud platforms, vector databases, LLM workflows, and AI-driven data automation. Ideal for professionals who want to become AI-ready data engineers and work on scalable, production-level AI systems.
Skills You Will Gain:
- Data engineering fundamentals
- AI system architecture
- ETL/ELT & data pipeline development
- Feature engineering for ML/AI
- Big data processing (Spark, distributed systems)
- Data lake & data warehouse design
- Building AI-ready datasets
- Vector databases (FAISS, Pinecone, Milvus)
- Real-time data & streaming analytics
The Course Enables Students To:
- Build scalable data pipelines for AI workloads
- Create feature stores and AI-ready datasets
- Implement real-time data streaming for LLMs/AI
- Use big data engines (Spark/PySpark)
- Deploy vector databases for AI retrieval systems
- Implement MLOps: model versioning, CI/CD, monitoring
- Operationalize AI systems using APIs & cloud services
SYLLABUS:
Module 1: Fundamentals of Data Engineering & AI Systems
- Overview of modern AI architectures
- Data pipeline lifecycle
- Data & AI dependencies
Module 2: Data Collection & Ingestion
- Batch ingestion
- Real-time streaming (Kafka/Kinesis)
- Web/API ingestion for AI
Module 3: Data Storage Architectures
- Data lakes & medallion architecture
- Data warehouses
- Object storage for AI
Module 4: Big Data Processing
- Spark, PySpark, distributed compute
- Handling large datasets for ML
- Data cleaning & transformation
Module 5: Feature Engineering for AI
- Feature stores (Feast/Databricks)
- Feature pipelines
- Embeddings & vector transformations
Module 6: Vector Databases & Retrieval Systems
- FAISS / Pinecone / Milvus
- Embedding storage
- RAG (Retrieval-Augmented Generation) fundamentals
Module 7: MLOps & Model Pipeline Automation
- CI/CD for ML
- Model registry & version control
- Automated data & model testing
Module 8: Building AI Inference Systems
- API deployment
- Real-time inference pipelines
- Scaling AI models
Module 9: Data Governance, Monitoring & Quality
- Data validation (Great Expectations)
- Drift detection
- AI system monitoring tools
Module 10: Industry Capstone Project
- End-to-end AI data engineering solution
- Data ingestion → Feature pipelines → Vector DB → Deployment
Skills You Will Develop:
- Apache Spark / PySpark
- Kafka / Kinesis
- AWS / GCP / Azure AI tools
- Feast (Feature Store)
- Airflow / Step Functions
- Vector DBs: FAISS, Pinecone, Milvus
Live Projects:
- Real-time streaming pipeline for AI predictions
- Vector database implementation for retrieval systems
- AI-ready feature store design
- ETL pipeline for model training
- Distributed Spark data processing workflow
- Deployment of a full AI data pipeline end-to-end
Who Is This Program For?
- Data engineers
- AI/ML engineers
- Cloud & DevOps engineers
- Software developers transitioning to AI
- Students aiming for careers in AI infrastructure
- Anyone who wants to build scalable AI pipelines
How To Apply:
- Mobile: 9100348679
- Email: coursedivine@gmail.com
