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Machine Learning using Databricks
Skills Gained Data Preparation for Machine Learning: Covers the foundational role of data quality in ML success, walks through the full data preparation lifecycle from ingestion through feature...
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Duration
2 days
Course Code
WA3927
Available Formats
Classroom
Overview
Skills Gained
- Data Preparation for Machine Learning: Covers the foundational role of data quality in ML success, walks through the full data preparation lifecycle from ingestion through feature engineering, and introduces Databricks tools including the Feature Store and Unity Catalog for governance and lineage tracking.
- Data Exploration and Exploratory Data Analysis (EDA): Teaches students to inspect, visualize, and clean data using PySpark and Python, identify quality issues, engineer and manage features, implement data validation frameworks, and apply best practices for preparing data for model training.
- Machine Learning Model Development: Guides students through the end-to-end ML workflow on Databricks, including AutoML, MLflow experiment tracking and model registry, distributed training, Mosaic AI governance, and the Data Science Agent Mode for accelerating model development.
- Machine Learning Model Deployment: Explores the model deployment lifecycle from serialization and packaging through serving and monitoring, covering deployment strategies, scalability considerations, model governance, and feature store integration in production environments.
- Machine Learning Operations: Introduces MLOps principles and their implementation in Databricks, covering the MLOps lifecycle, model monitoring, drift detection, automated retraining pipelines, multi-environment deployment, and cost optimization on AWS.
Course Details
Course Details
WA3927 Introduction
- Agenda
- Prerequisites
- Outline
- Timing
Data Preparation for Machine Learning
- Why Data Preparation Matters
- Prepare, Build and Deploy ML Models
Data Exploration and Exploratory Data Analysis (EDA)
- Understanding Your Data
- Loading and Inspecting Data in Databricks
- Visualizing Data Distributions
- Using Python Libraries for Visualization
- Identifying Data Quality Issues
- Data Cleaning and Transformation
- Handling Duplicates
- Bias vs Skewness
- Why is Feature Engineering Important
- Feature Engineering in the ML Lifecycle
- Feature Storage and Management
- Consuming Features for Model Training
- Data Quality and Validation
- Automated Data Quality Checks Example
- Data Governance with Unity Catalog
- ML Lifecycle Integration
- Best Practices and Common Pitfalls
- Data Preparation Best Practices
- Common Pitfalls to Avoid
- Genie Code - Data Science Agent
- Reflective Questions
- Resources
- Review
Machine Learning Model Development
- Why Model Development Matters
- Why monitoring and managing developed models is essential
- The ML Workflow on Databricks
- Databricks AutoML: Automating Model Development
- Databricks Runtime for Machine Learning
- MLflow Integration
- Model Governance and Monitoring
- Distributed Training for Deep Learning
- Best Practices for Model Development on Databricks
- Mosaic AI: Unified Data and AI Governance
- Introduction to Data Science Agent Mode
- Reflective Questions
- Resources
- Review
- Take Away
Machine Learning Model Deployment
- What is Model Deployment?
- Why Model Deployment Matters
- Monitoring and managing deployed models
- Why is Team Collaboration Important
- Model Deployment Lifecycle
Model Deployment of Trained ML Models
- Model Deployment Challenges
- Model Deployment Process
- Model Deployment Example
- Key Concepts in Model Deployment
- Production Considerations
- Best Practices for Model Deployment
- Reflective Questions
- Resources
- Review
Machine Learning Operations
- Why Machine Learning Operations Matters
- Key Strategies for Effective MLOps
- Overview: Understanding MLOps in Databricks
- Deep Dive: Databricks MLOps Implementation
- Monitoring and Maintenance
- Advanced Topics: Best Practices on Databricks AWS
- Best Practices Summary
- Reflective Questions
- Resources
- Review
WA3927 Conclusion
- Summary
- Additional Resources
- Thank You
Schedule
FAQ
Does the course schedule include a Lunchbreak?
Classes typically include a 1-hour lunch break around midday. However, the exact break times and duration can vary depending on the specific class. Your instructor will provide detailed information at the start of the course.
What languages are used to deliver training?
Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.
What does GTR stand for?
GTR stands for Guaranteed to Run; if you see a course with this status, it means this event is confirmed to run. View our GTR page to see our full list of Guaranteed to Run courses.
How do I find an Ascendient Learning training location?
We have training locations across the United States and Canada - View a complete list of classroom training locations.
Which delivery formats are available?
At Ascendient Learning, we offer training that is Instructor-Led, Online, Virtual, and Self-Paced.
Does Ascendient Learning deliver group training?
Yes, we provide training for groups, individuals and private on sites. View our group training page for more information.
What does vendor-authorized training mean?
As a vendor-authorized training partner, we offer a curriculum that our partners have vetted. We use the same course materials and facilitate the same labs as our vendor-delivered training. These courses are considered the gold standard and, as such, are priced accordingly.
Is the training too basic, or will you go deep into technology?
It depends on your requirements, your role in your company, and your depth of knowledge. The good news about many of our learning paths, you can start from the fundamentals to highly specialized training.
How up-to-date are your courses and support materials?
We continuously work with our vendors to evaluate and refresh course material to reflect the latest training courses and best practices.
Are your instructors seasoned trainers who have deep knowledge of the training topic?
Ascendient Learning instructors have an average of 27 years of practical IT experience and have also served as consultants for an average of 15 years. To stay current, instructors spend at least 25 percent of their time learning new, emerging technologies and courses.
Do you provide hands-on training and exercises in an actual lab environment?
Lab access is dependent on the vendor and the type of training you sign up for. However, many of our top vendors will provide lab access to students to test and practice. The course description will specify lab access.
Will you customize the training for our company’s specific needs and goals?
We will work with you to identify training needs and areas of growth. We offer a variety of training methods, such as private group training, on-site of your choice, and virtually. We provide courses and certifications that are aligned with your business goals.
How do I get started with certification?
Getting started on a certification pathway depends on your goals and the vendor you choose to get certified in. Many vendors offer entry-level IT certification to advanced IT certification that can boost your career. To get access to certification vouchers and discounts, please contact info@ascendientlearning.com.
Will I get access to content after I complete a course?
You will get access to the PDF of course books and guides, but access to the recording and slides will depend on the vendor and type of training you receive.
How do I request a W9 for Ascendient Learning?
View our filing status and how to request a W9.
Reviews
Concise and good to follow along. Although it is a lot to take in under a short period of time.
Topics, material and specially instructor (Graham Godfrey) was beyond my expectations.
Great company -- easy to sign up and very organized. Loved my teacher and class overall.
Some Labs are very good but some steps it ask to update but its already updated, but overall its very good training.
Great training it covered the most importan topics if GitHub copilot with good explanation and good labs.
