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Catalog / All levels / Databricks Certified Machine Learning Professional Exam Preparation with 10 Practice Exams
Mammoth Club All levels 16 sections 117 lectures

Databricks Certified Machine Learning Professional Exam Preparation with 10 Practice Exams

Move your machine learning models from the notebook to production. It's time to build and manage solutions that deliver real-world impact at scale.

MC
Created by Team Mammoth
01
Skill level
All levels
02
Sections
16
03
Lectures
117
04
Instructor
Team Mammoth
What's inside

This course includes.

✓
16
Sections
✓
117
Lectures
✓
69
Quizzes
✓
Certificate of completion
Included
✓
Mobile and desktop access
Included
✓
AI learning assistance
Included
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Course content

Curriculum & lectures.

6 sections · 60 lectures
+ Welcome 2 lectures
What You'll Learn Locked
What You'll Need Locked
+ Experimentation 9 lectures
Read and write a Delta table Locked
View Delta table history and load a previous version of a Delta table Locked
Create, overwrite, merge, and read Feature Store tables in machine learning workflows Locked
Manually log parameters, models, and evaluation metrics using MLflow Locked
Programmatically access and use data, metadata, and models from MLflow experiments Locked
Perform MLflow experiment tracking workflows using model signatures and input examples Locked
Identify the requirements for tracking nested runs Locked
Enable autologging, including with the use of Hyperopt Locked
Log and view artifacts like SHAP plots, custom visualizations, feature data, images, and metadata Locked
+ Model Lifecycle Management 16 lectures
Describe an MLflow flavor and the benefits of using MLflow flavors Locked
Advantages of using the pyfunc MLflow flavor Locked
Process and benefits of including preprocessing logic and context in custom model classes and object Locked
Basic purpose and user interactions with Model Registry Locked
Programmatically register a new model or new model version Locked
Add metadata to a registered model and a registered model version Locked
Identify, compare, and contrast the available model stages Locked
Transition, archive, and delete model versions Locked
Automated testing in ML Continuous Integration and Continuous Delivery pipelines Locked
Automate the model lifecycle using Model Registry Webhooks and Databricks Jobs Locked
Advantages of using Job clusters over all-purpose clusters Locked
Create a Job that triggers when a model transitions between stages, given a scenario Locked
Connect a Webhook with a Job Locked
Identify which code block will trigger a shown webhook Locked
Identify a use case for HTTP webhooks and where the Webhook URL needs to come Locked
How to list all webhooks and how to delete a webhook Locked
+ Model Deployment 20 lectures
Batch deployment as the appropriate use case for the vast majority of deployment use cases Locked
How batch deployment computes predictions and saves them somewhere for later use Locked
Identify live serving benefits of querying precomputed batch predictions Locked
Identify less performant data storage as a solution for other use cases Locked
Load registered models with load_model Locked
Deploy a single-node model in parallel using spark_udf Locked
Identify z-ordering as a solution for reducing the amount of time to read predictions from a table Locked
Identify partitioning on a common column to speed up querying Locked
Practical benefits of using the score_batch operation Locked
Describe Structured Streaming as a common processing tool for ETL pipelines Locked
Structured streaming as a continuous inference solution on incoming data Locked
Why complex business logic must be handled in streaming deployments Locked
Identify that data can arrive out-of-order with structured streaming Locked
Continuous predictions in time-based prediction store as a scenario for streaming deployments Locked
Convert a batch deployment pipeline inference to a streaming deployment pipeline Locked
Convert a batch deployment pipeline writing to a streaming deployment pipeline Locked
Benefits of real-time inference for a small number of records or fast prediction computations Locked
Identify JIT feature values as a need for real-time deployment Locked
Query a Model Serving enabled model in the Production stage and Staging stage Locked
How cloud RESTful services in containers is best for production-grade real-time deployments Locked
+ Solution and Data Monitoring 12 lectures
Compare and contrast label drift and feature drift Locked
Scenarios in which feature or label drift are likely to occur Locked
Concept drift and its impact on model efficacy Locked
Summary statistic monitoring as a simple solution for numeric feature drift Locked
Mode, unique values, and missing values as simple solutions for categorical feature drift Locked
Tests as more robust monitoring solutions for numeric feature drift than simple summary statistics Locked
Tests as more robust monitoring for categorical feature drift than summary statistics Locked
Compare Jenson-Shannon divergence vs Kolmogorov-Smirnov tests for numerical drift detection Locked
Identify a scenario in which a chi-square test would be useful Locked
Common workflow for measuring concept drift and feature drift Locked
When retraining and deploying an updated model is a probable solution to drift Locked
Test whether the updated model performs better on the more recent data Locked
+ Challenge Your 10 FREE Practice Exams 1 lecture
Where to go from here Locked
Description

About this course.

This professional-level course shows you how to master the tools and best practices for operationalizing machine learning on the Databricks Lakehouse Platform, from data processing to deployment and monitoring.


✅ Implement scalable data preparation pipelines and advanced feature engineering using Spark and Delta Lake

✅ Leverage MLflow to track experiments, package models, and manage the complete model lifecycle

✅ Master deployment strategies for batch scoring and real-time inference using Databricks Model Serving

✅ Apply advanced concepts like distributed model training and production monitoring to ensure reliability


Whether you're a data scientist aiming to operationalize your models or an ML engineer building robust MLOps pipelines, this course provides the focused preparation needed to master the Databricks ecosystem.


🎁 Includes 10 full-length practice exams. Deepen your platform knowledge. Validate your skills.


If you're ready to transition from building models to deploying enterprise-grade ML solutions—this is your MLOps playbook

Ready to start building?

Move your machine learning models from the notebook to production. It's time to build and manage solutions that deliver real-world impact at scale.

Buy lifetime access →