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AI for Data Engineers Training

Live Online & Classroom Enterprise Training

AI for Data Engineers teaches how to build and manage data pipelines that support AI and machine learning workflows in scalable, production-ready environments.

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What is AI for Data Engineers Course about?

This course bridges the gap between data engineering and artificial intelligence, focusing on how data engineers can design, build, and maintain data systems that support AI and machine learning applications. It covers data pipelines, data preprocessing, model deployment, and tools/frameworks like Apache Spark, TensorFlow, and MLflow. 

What are the objectives of AI for Data Engineers Course ?

  • Understand the role of a data engineer in AI/ML workflows
  • Design data pipelines optimized for ML training and inference
  • Clean, transform, and manage datasets for supervised and unsupervised learning
  • Automate model deployment using ML pipelines and orchestration tools (e.g., Airflow, MLflow, Kubeflow)
  • Manage model versioning, retraining, and serving in production environments
  • Handle large-scale, real-time data processing for AI applications
  • Implement best practices in MLOps (Machine Learning Operations)

Who is AI for Data Engineers Course for?

  • Data Engineers
  • ML Engineers transitioning from data roles
  • Cloud Engineers and Solution Architects
  • Big Data Professionals
  • AI Engineers focusing on infrastructure
  • Technical Data Analysts and Developers 

What are the prerequisites for AI for Data Engineers Course?

  • Solid understanding of data engineering concepts (ETL/ELT, data warehousing, data lakes)
  • Proficiency in Python, SQL, and basic cloud platforms (AWS, GCP, or Azure)
  • Familiarity with distributed computing frameworks like Apache Spark

Available Training Modes

Live Online Training

4 Days

Self-Paced Training

40 Hours

Course Outline Expand All

Expand All

  • The AI lifecycle and the data engineer’s role
  • Differences between data science and data engineering in AI
  • Overview of data-to-AI architecture
  • Feature engineering at scale
  • Data labeling, transformation, and enrichment
  • Handling unstructured data (text, images, etc.)
  • Designing batch and real-time data flows
  • Tools: Apache Spark, Kafka, Beam
  • Data quality checks and monitoring
  • Creating end-to-end AI pipelines
  • Introduction to MLflow, Airflow, and orchestration
  • Data lineage and pipeline reproducibility
  • Serving ML models in real-time vs. batch
  • Using TensorFlow Serving, FastAPI, and Docker
  • Monitoring model performance (drift, accuracy)
  • Version control for models and datasets
  • CI/CD for ML (with GitHub Actions, Jenkins, etc.)
  • Collaboration between data engineers and data scientists
  • Deploying AI pipelines on AWS/GCP/Azure
  • Using managed services: SageMaker, Vertex AI, Azure ML
  • Auto-scaling and cost optimization strategies
  • AI for fraud detection (financial data pipelines)
  • Recommendation engine data flow
  • Real-time NLP pipeline in production

Who is the instructor for this training?

The trainer for this AI for Data Engineers Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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