Course Overview
Welcome to Machine Learning with Databricks!
This course is your gateway to mastering machine learning workflows on Databricks. Dive into data preparation, model development, deployment, and operations, guided by expert instructors. Learn essential skills for data exploration, model training, and deployment strategies tailored for Databricks. By course end, you’ll have the knowledge and confidence to navigate the entire machine learning lifecycle on the Databricks platform, empowering you to build and deploy robust machine learning solutions efficiently.
What are the skills covered
- Data Preparation for Machine Learning
- Machine Learning Model Development
- Machine Learning Model Deployment
- Machine Learning Operations
Who should attend this course
- Everyone who is interested
Course Curriculum
What are the Prerequisites
At a minimum, you should be familiar with the following before attempting to take this content:
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Knowledge of fundamental concepts of regression and classification methods
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Knowledge of fundamental machine learning models
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Knowledge of the model lifecycle, MLflow components, and MLflow tracking
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Familiarity with Databricks workspace and notebooks
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Familiarity with Delta Lake and Lakehouse
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Intermediate level knowledge of Python
Course Modules
Exam & Certification
Databricks Certified Machine Learning Associate exam.
The Databricks Certified Machine Learning Associate certification exam assesses an individual’s ability to use Databricks to perform basic machine learning tasks. This includes an ability to understand and use Databricks and its machine learning capabilities like AutoML, Unity Catalog and select features of MLflow. It also assesses the ability to explore data and perform feature engineering.
Additionally, the exam assesses model building through training, tuning and evaluation and selection. Finally, an ability to deploy machine learning models is assessed. Individuals who pass this certification exam can be expected to complete basic machine learning tasks using Databricks and its associated tools.
This exam covers:
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Databricks Machine Learning – 38%
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ML Workflows – 19%
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Model Development – 31%
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Model Deployment – 12%
