Go beyond data science. Build production AI systems that scale.
Equip learners to build production-grade data pipelines, integrate machine learning models into applications, and deploy AI-powered microservices with real-time analytics capabilities.
Master the modern AI data stack, data lakes vs. warehouses, and start exploring production-grade datasets.
Build batch and streaming ingestion scripts using REST APIs to feed your data warehouse.
Implement ETL/ELT pipelines with Pandas and SQL for cleaning and feature preparation.
Set up scheduled job orchestration using Airflow or Prefect. Manage complex dependency graphs.
Implement event-driven data processing using Kafka. Handle streaming data for real-time applications.
Design structured data storage in PostgreSQL. Optimize queries for large-scale analytical workloads.
Move from model development to production serving with FastAPI. Build inference endpoints.
Build real-time monitoring dashboards for project metrics and model performance analytics.
Implement data validation and quality checks. Ensure reliability and lineage in your data systems.
Implement vector storage using Pinecone. Master similarity search for AI-powered discovery.
Automate embedding updates for large datasets. Build pipelines for continuous vector indexing.
Build intelligent insight engines using LLMs to analyze structured and unstructured data.
Containerize your data systems and deploy to the cloud. Manage production-grade AI services.
Optimize your data architecture for scale. Implement caching and load balancing for AI services.
Build and showcase a production-grade Recommendation Engine. Graduation day.
Bridge the gap between raw data and intelligent applications.
Apply for Track D