Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Databricks Certified Machine Learning Associate Exam Guide
Rating: 3.8 out of 5(695 ratings)
6,837 students

Databricks Certified Machine Learning Associate Exam Guide

Databricks Certified Machine Learning Associate Certification with 10+ Hours of HD Quality Video & Lots of Hands-on
Last updated 11/2025
English

What you'll learn

  • Apply Databricks AutoML to different ML Problem like Regression, Classification
  • Use MLFlow to Track Complete ML Lifecycle inside Data bricks environment
  • Register model & Deploy to Production with MLFlow & Databricks
  • Store Model Features inside Feature Store

Course content

5 sections35 lectures4h 39m total length
  • Introduction to Databricks Machine Learning6:28
  • Lab: Databricks Workspace with Community Edition6:41
  • Lab: Databricks Workspace with Azure Cloud8:54
  • Databricks User Interface Overview8:56
  • Azure Databricks Architecture Overview3:07
  • Resources Created by Azure Databricks Workspace2:26

Requirements

  • Basic Machine Learning knowledge
  • Credit or Debit card for Azure Account

Description

Welcome to our comprehensive course on Databricks Certified Machine Learning Engineer Associate certification. This course is designed to help you master the skills required to become a certified Databricks ML engineer associate.

Databricks is a cloud-based data analytics platform that offers a unified approach to data processing, machine learning, and analytics. With the growing demand for data engineers, Databricks has become one of the most sought-after skills in the industry.

The minimally qualified candidate should be able to:

  • Use Databricks Machine Learning and its capabilities within machine learning workflows, including:

    • Databricks Machine Learning (clusters, Repos, Jobs)

    • Databricks Runtime for Machine Learning (basics, libraries)

    • AutoML (classification, regression, forecasting)

    • Feature Store (basics)

    • MLflow (Tracking, Models, Model Registry)

  • Implement correct decisions in machine learning workflows, including:

    • Exploratory data analysis (summary statistics, outlier removal)

    • Feature engineering (missing value imputation, one-hot-encoding)

    • Tuning (hyperparameter basics, hyperparameter parallelization)

    • Evaluation and selection (cross-validation, evaluation metrics)

  • Implement machine learning solutions at scale using Spark ML and other tools, including:

    • Distributed ML Concepts

    • Spark ML Modeling APIs (data splitting, training, evaluation, estimators vs. transformers, pipelines)

    • Hyperopt

    • Pandas API on Spark

    • Pandas UDFs and Pandas Function APIs

  • Understand advanced scaling characteristics of classical machine learning models, including:

    • Distributed Linear Regression

    • Distributed Decision Trees

    • Ensembling Methods (bagging, boosting)

Who this course is for:

  • Anyone wants to Pass Databricks Certified Machine Learning Associate Exam