Build end-to-end machine learning workflows and deploy models at scale using Databricks
This course teaches how to prepare and explore data, develop and tune models, and manage deployment using tools such as MLflow, AutoML and Databricks Model Serving within the Lakehouse platform.
- Why get trained: Learn how to build, track and deploy machine learning models using MLflow, AutoML, Feature Store and Databricks Model Serving.
- Why it matters: Machine learning capabilities enable organizations to operationalize AI, automate decision-making and scale predictive models across business use cases.
- Who should attend: Data scientists, machine learning engineers and data professionals responsible for developing and deploying machine learning solutions on Databricks.
Build the capability to design, deploy and manage machine learning solutions at scale using Databricks with Trainocate. HRD Corp Claimable.
Overview
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.
Skills Covered
- Data Preparation for Machine Learning
- Machine Learning Model Development
- Machine Learning Model Deployment
- Machine Learning Operations
Prerequisites
At a minimum, you should be familiar with the following before attempting to take this content:
- Knowledge of fundamental concepts of regression and classification methods
- Knowledge of fundamental machine learning models
- Knowledge of the model lifecycle, MLflow components, and MLflow tracking
- Familiarity with Databricks workspace and notebooks
- Familiarity with Delta Lake and Lakehouse
- Intermediate level knowledge of Python
Target Audience
- Everyone who is interested
Module 1: Data Preparation for Machine Learning
- Managing and Exploring Data
- Managing and Exploring Data in the Lakehouse
- Data Preparation and Feature Engineering
- Fundamentals of Data Preparation and Feature Engineering
- Data Imputation
- Data Encoding
- Data Standardization
- Feature Store
- Introduction to Feature Store
Module 2: Machine Learning Model Development
- Model Development Workflow
- Model Development and MLflow
- Evaluating Model Performance
- Hyperparameter Tuning
- Hyperparameter Tuning Fundamentals
- Hyperparameter Tuning with Hyperopt
- AutoML
- Automated Model Development with AutoML
Module 3: Machine Learning Model Deployment
- Model Deployment Fundamentals
- Model Deployment Strategies
- Model Deployment with MLflow
- Batch Deployment
- Introduction to Batch Deployment
- Pipeline Deployment
- Introduction to Pipeline Deployment
- Real-time Deployment and Online Stores
- Introduction to Real-time Deployment
- Databricks Model Serving
Module 4: Machine Learning Operations
- Modern MLOps
- Defining MLOps
- MLOps on Databricks
- Architecting MLOps Solutions
- Opinionated MLOps Principles
- Recommended MLOps Architectures
- Implementation and Monitoring MLOps Solution
- MLOps Stacks Overview
- Type of Model Monitoring
- Monitoring in Machine Learning
Dates & Locations
July 15, 2026 - July 16, 2026
September 15, 2026 - September 16, 2026
Exam & Certification
Databricks Certified Machine Learning Associate.
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:
- Databricks Machine Learning – 38%
- ML Workflows – 19%
- Model Development – 31%
- Model Deployment – 12%
Training & Certification Guide
Frequently Asked Questions
Speak to a Training Consultant
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