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Databricks Machine Learning Training

Live Online & Classroom Enterprise Training

Databricks Machine Learning enables data scientists and engineers to build, train, and deploy machine learning models on the Databricks Lakehouse Platform. This course provides hands-on knowledge of scalable ML workflows using Apache Spark, MLflow, and Databricks tools for data preparation, model development, tracking, and deployment.

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What is Databricks Machine Learning Training about?

The Databricks Machine Learning course introduces learners to building end-to-end machine learning solutions using the Databricks platform. Participants will learn how to prepare large datasets, develop machine learning models using Spark ML and popular Python libraries, track experiments with MLflow, and deploy models for real-world applications. The course emphasizes scalable data processing, collaborative workflows, and production-ready ML pipelines in the Lakehouse environment.

What are the objectives of Databricks Machine Learning Training ?

  • Understand the fundamentals of machine learning on the Databricks Lakehouse platform. 
  • Learn to prepare and process large datasets using Apache Spark. 
  • Build and train machine learning models using Spark ML and Python libraries. 
  • Track experiments and manage models using MLflow. 
  • Deploy and monitor machine learning models in production environments. 

Who is Databricks Machine Learning Training for?

  • Data Scientists working with large-scale datasets. 
  • Data Engineers interested in machine learning workflows. 
  • AI/ML Engineers building scalable ML pipelines. 
  • Analytics professionals working with predictive models. 
  • Software developers transitioning into machine learning. 

What are the prerequisites for Databricks Machine Learning Training?

Prerequisites:  

  • Basic understanding of machine learning concepts. 
  • Knowledge of Python programming. 
  • Familiarity with data analysis and statistics. 
  • Basic understanding of Apache Spark or big data concepts. 
  • Experience working with datasets and data processing tools.  

  

Learning Path: 

  • Introduction to Databricks and the Lakehouse Platform. 
  • Data preparation and feature engineering with Spark. 
  • Building and training machine learning models. 
  • Experiment tracking and model management with MLflow. 
  • Model deployment and monitoring in Databricks. 


Related Courses: 

  • Apache Spark for Data Engineering 
  • Data Engineering on Databricks 
  • Machine Learning with Python 
  • Advanced Apache Spark

Available Training Modes

Live Online Training

2 Days

Course Outline Expand All

Expand All

  • Overview of Databricks Lakehouse Platform
  • Machine Learning Workflow in Databricks
  • Introduction to Databricks Workspace and Notebooks
  • Setting Up ML Environments
  • Data Ingestion and Exploration
  • Data Cleaning and Transformation with Spark
  • Feature Engineering Techniques
  • Handling Large-Scale Datasets
  • Introduction to Spark MLlib
  • Supervised and Unsupervised Learning Models
  • Model Training and Evaluation
  • Hyperparameter Tuning
  • Introduction to MLflow
  • Tracking Experiments and Metrics
  • Model Versioning and Registry
  • Collaboration and Reproducibility
  • Deploying Models using Databricks
  • Batch and Real-Time Inference
  • Monitoring Model Performance
  • Managing ML Pipelines and Lifecycle

Who is the instructor for this training?

The trainer for this Databricks Machine Learning Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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