NEWIntermediate
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Data Engineer + AI Bootcamp

Build Smart Data Pipelines with AI & ML Integration

The Data Engineer + AI Bootcamp takes you from data pipeline fundamentals all the way to building AI/ML-integrated production systems. You'll master SQL, Apache Spark, Airflow, and cloud data tools on AWS and Azure — then layer in ML pipeline integration, feature engineering, and MLOps fundamentals. Graduate ready for top-tier Data Engineer and ML Platform Engineer roles at US companies.

6 Months duration
1,000+ enrolled
4.8 (980+ reviews)
Next batch: May 15, 2026

What You'll Learn

SQL for data engineering & analytics
Apache Spark for large-scale processing
Apache Airflow for pipeline orchestration
AWS & Azure data platform tools
Data warehousing (Snowflake, Redshift)
ML pipeline integration (data → model → deployment)
Feature engineering for ML systems
Real-time AI data pipelines with Kafka
MLOps fundamentals & model monitoring
End-to-end prediction system deployment
⚙️
Data Engineer + AI Bootcamp
Build Smart Data Pipelines with AI & ML Integration
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Curriculum6 modules
Projects4 capstone projects
CertificateIndustry recognized
Live sessions5 days/week
Mentorship1:1 industry experts

About This Program

The Data Engineer + AI Bootcamp takes you from data pipeline fundamentals all the way to building AI/ML-integrated production systems. You'll master SQL, Apache Spark, Airflow, and cloud data tools on AWS and Azure — then layer in ML pipeline integration, feature engineering, and MLOps fundamentals. Graduate ready for top-tier Data Engineer and ML Platform Engineer roles at US companies.

Common Questions

What background do I need for this bootcamp?
Basic SQL and Python familiarity is helpful but not required. We cover all foundational concepts before moving to advanced topics.
What cloud platforms will I learn?
You'll get hands-on experience with AWS (Glue, S3, Redshift) and Azure (Data Factory, Databricks). Projects are deployed on real cloud environments.
How is this different from a pure data engineering course?
We go beyond pipelines — you'll integrate ML models into your pipelines, implement feature stores, and learn MLOps so your pipelines support production AI systems.
What roles does this prepare me for?
Data Engineer, ML Platform Engineer, Analytics Engineer, and Data Infrastructure Engineer at US tech companies.
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