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Databricks and AI/ML

Summary:

 

This Databricks course covers core concepts, data engineering, machine learning, and advanced AI techniques. Learn to build data pipelines, manage ETL processes, and perform data transformations using PySpark. Explore deep learning with TensorFlow and PyTorch, along with real-world AI use cases. Gain expertise in model deployment, CI/CD, and monitoring with Delta Lake. Complete the course with a hands-on project showcasing a full data pipeline and ML solution.

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Course Specifics:

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Module 1: Introduction to Databricks

  1. Overview of Databricks

    • What is Databricks?

    • Features and benefits

  2. Databricks Ecosystem

    • Integration with cloud platforms (Azure, AWS, GCP)

    • Databricks and Apache Spark

  3. Hands-On:

    • Setting up a Databricks workspace

    • Navigating the Databricks user interface

 

Module 2: Data Engineering with Databricks

  1. Understanding Data Pipelines

    • ETL process overview

    • Data formats and ingestion

  2. Building Data Pipelines

    • Using Databricks for batch and real-time data processing

    • Data storage and optimization techniques

  3. Hands-On:

    • Creating and managing ETL pipelines in Databricks

    • Data transformation with PySpark

 

Module 3: Machine Learning with Databricks

  1. ML Fundamentals in Databricks

    • Databricks MLflow integration

    • Experiment tracking and model registry

  2. Training and Tuning Models

    • Using AutoML in Databricks

    • Hyperparameter optimization

  3. Hands-On:

    • Training a machine learning model

    • Logging experiments with MLflow

 

Module 4: Advanced AI and Deep Learning

  1. Deep Learning Capabilities

    • TensorFlow and PyTorch on Databricks

    • Distributed training

  2. Advanced AI Techniques

    • NLP and image processing

    • Using pre-trained models

  3. Hands-On:

    • Building and deploying a deep learning model

    • Leveraging GPUs for computation

 

Module 5: Databricks for Big Data and AI Integration

  1. Big Data in Databricks

    • Working with large datasets

    • Optimizing performance with Delta Lake

  2. AI and IoT Applications

    • Real-world use cases

    • Predictive analytics pipelines

  3. Hands-On:

    • Real-time analytics with streaming data

    • End-to-end pipeline for predictive maintenance

 

Module 6: Deployment and Monitoring

  1. Deploying AI/ML Models

    • Deployment strategies

    • CI/CD for Databricks projects

  2. Monitoring and Scaling

    • Model performance monitoring

    • Scaling Databricks clusters

  3. Hands-On:

    • Deploying a model to production

    • Setting up alerts and monitoring

 

Final Project:

  • Develop a complete data pipeline and machine learning solution using Databricks.

  • Present project results, challenges, and insights.

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