DevOps for AI and Machine Learning

Learn how to apply DevOps knowledge to the machine learning and AI development, test, delivery, and operations process.

Upcoming Classes

Dates
Mode
Location
Price
Aug 27Aug 28, 2024
Virtual Classroom
Virtual Classroom
$1,495
Dec 03Dec 04, 2024
Virtual Classroom
Virtual Classroom
$1,495
Sep 22Sep 23, 2024
Anaheim, CA
Anaheim, CA at STARWEST
$1,545
Oct 13Oct 14, 2024
Orlando, FL
Orlando, FL at Agile + DevOps East
$1,545
Call to Schedule
Anytime
Your Location
Your Location
Select a learning mode button (Public, Live Virtual, etc.) for pricing, details, and a downloadable fact sheet.
Ways to Save

Software Summer School - BOGO - JUNE, JULY, & AUGUST CLASSES

Our Software Summer School is back in session! Buy One, Get One Free on our most popular live virtual training classes offered between June 1 and August 31, 2024. Take both classes yourself or split them with a colleague.

Our practical, hands-on courses can help you and your team in areas like AI, GitHub, agile, leadership, DevOps, software testing, and many more. Live virtual training allows you to dive deep into these topics with instructor-led classes from the comfort of your home or office.

Ready to join? Click the Register button below to register via email.

This is a limited time offer, and certain restrictions do apply. See details below.

OFFER DETAILS: Register for any participating live virtual class being offered in June, July, or August 2024 and get a second class of equal or lesser value free. Classes must take place between June 1 and August 31. Not all classes are valid. Contact Client Support for specifics on which classes are included. Register directly with our Client Support team at 929.777.8102 or through the email above. This offer is only valid for new registrations and cannot be combined with other promotions. Optional certification test vouchers must be purchased one per person who wishes to add them on and are NOT included in the Buy 1 Get 1 Free deal that is limited to just the class.
 
PREQUISITES: Many of our classes require previous skills, experience, or certifications to ensure understanding. Please be sure to check prerequisites for each class to ensure you have the experience necessary to actively participate. Questions? Call us at 929.777.8102.
 
Choose from the following classes.  ( = Guaranteed to run dates):
 
June Classes

July Classes

August Classes

 
  

 

Description

MLOps is the application of DevOps practices and principles to the machine learning (ML) process. In DevOps for AI and Machine Learning, DevOps engineers and testers will learn the foundational knowledge and practical skills necessary to integrate machine learning operations (MLOps) into existing AI Model development workflows.

This workshop covers the entire MLOps lifecycle, focusing on essential concepts, tools, and methodologies required to deploy and maintain machine learning models within DevOps environments.

Key takeaways from this class include:

  • Gaining an in-depth understanding of MLOps principles and their integration with DevOps practices.
  • Learning to set up automated data engineering pipelines.
  • Tracking model experiments effectively.
  • Implementing CI/CD pipelines for machine learning models.
  • Establishing robust monitoring and retraining strategies.
  • Navigating security and compliance considerations in MLOps.

Throughout this workshop, students will gain real-world context through practical hands-on exercises, such as setting up feature stores, implementing CI/CD processes for ML models, and deploying and monitoring models.

Who Should Attend
This workshop is ideal for DevOps engineers, software testers, and operations personnel looking to expand their skill set into MLOps. Professionals involved in software development, deployment, infrastructure management, quality assurance, or operations who wish to understand the unique challenges and best practices in deploying and maintaining machine learning models will benefit. It caters to individuals with a technical background in DevOps practices but with limited exposure to machine learning, aiming to bridge the gap between traditional DevOps workflows and the specialized requirements of MLOps.

Questions? 929.777.8102 [email protected]
Course Outline

Session 1: Introduction to MLOps

  • Definition and importance of MLOps
  • Cross-disciplinary collaboration
  • Key challenges in deploying and maintaining machine learning models
  • Machine Learning Process & Roles
  • The MLOps lifecycle
  • Exercise #1

Session 2: Automated Data Engineering

  • Data engineering process and ETL/ELT transformations
  • Understanding and managing data and feature sets
  • Introduction to feature stores and their role in data-centric ML pipelines
  • Exercise #2: Creating datasets and setting up a feature store

Session 3: Model experiment tracking

  • Model experimentation process
  • Testing and validating models with datasets
  • Introduction to collaborative notebooks
  • Capturing experimentation information
  • Deploying notebooks vs. model code
  • Exercise #3: Track experiments during model development

Session 4: CI/CD for MLOps

  • Introduction to CI/CD pipelines and their role in MLOps
  • Managing model and data versioning
  • Automating model deployment with CI/CD (models vs. code)

  • Integration testing and model evaluation
  • Model registries and best practices in deploying ML models (AB Testing, Canary)
  • Exercise #4: Setup CI/CD process and integrate testing

Session 5: Monitoring, Logging, and Retraining

  • Setting up monitoring systems for deployed ML models
  • Scalability and auto-scaling considerations for models
  • Implementation of logging and error-tracking systems
  • Retraining strategies when model accuracy deteriorates
  • Exercise #5: Deploy and monitor a model

Session 6: Security and Compliance

  • Security aspects in MLOps: understanding the threats
  • Compliance considerations such as GDPR, HIPAA
  • Implementing authentication and authorization
  • Creating and using AIBOMs
  • Exercise #6: Setting up security measures and AIBOMs

Exercise #7: Putting it all together
Q&A and Wrap-Up

  • Summary and wrap-up of the course
  • References
  • Q&A session to address participant queries
Dates
Mode
Location
Price
Sep 22Sep 23, 2024
Anaheim, CA
Anaheim, CA at STARWEST
$1,545
Oct 13Oct 14, 2024
Orlando, FL
Orlando, FL at Agile + DevOps East
$1,545
Price: $1,545 USD
Course Duration: 2 Days
Description

MLOps is the application of DevOps practices and principles to the machine learning (ML) process. In DevOps for AI and Machine Learning, DevOps engineers and testers will learn the foundational knowledge and practical skills necessary to integrate machine learning operations (MLOps) into existing AI Model development workflows.

This workshop covers the entire MLOps lifecycle, focusing on essential concepts, tools, and methodologies required to deploy and maintain machine learning models within DevOps environments.

Key takeaways from this class include:

  • Gaining an in-depth understanding of MLOps principles and their integration with DevOps practices.
  • Learning to set up automated data engineering pipelines.
  • Tracking model experiments effectively.
  • Implementing CI/CD pipelines for machine learning models.
  • Establishing robust monitoring and retraining strategies.
  • Navigating security and compliance considerations in MLOps.

Throughout this workshop, students will gain real-world context through practical hands-on exercises, such as setting up feature stores, implementing CI/CD processes for ML models, and deploying and monitoring models.

Who Should Attend
This workshop is ideal for DevOps engineers, software testers, and operations personnel looking to expand their skill set into MLOps. Professionals involved in software development, deployment, infrastructure management, quality assurance, or operations who wish to understand the unique challenges and best practices in deploying and maintaining machine learning models will benefit. It caters to individuals with a technical background in DevOps practices but with limited exposure to machine learning, aiming to bridge the gap between traditional DevOps workflows and the specialized requirements of MLOps.

Questions? 929.777.8102 [email protected]
Course Outline

Session 1: Introduction to MLOps

  • Definition and importance of MLOps
  • Cross-disciplinary collaboration
  • Key challenges in deploying and maintaining machine learning models
  • Machine Learning Process & Roles
  • The MLOps lifecycle
  • Exercise #1

Session 2: Automated Data Engineering

  • Data engineering process and ETL/ELT transformations
  • Understanding and managing data and feature sets
  • Introduction to feature stores and their role in data-centric ML pipelines
  • Exercise #2: Creating datasets and setting up a feature store

Session 3: Model experiment tracking

  • Model experimentation process
  • Testing and validating models with datasets
  • Introduction to collaborative notebooks
  • Capturing experimentation information
  • Deploying notebooks vs. model code
  • Exercise #3: Track experiments during model development

Session 4: CI/CD for MLOps

  • Introduction to CI/CD pipelines and their role in MLOps
  • Managing model and data versioning
  • Automating model deployment with CI/CD (models vs. code)

  • Integration testing and model evaluation
  • Model registries and best practices in deploying ML models (AB Testing, Canary)
  • Exercise #4: Setup CI/CD process and integrate testing

Session 5: Monitoring, Logging, and Retraining

  • Setting up monitoring systems for deployed ML models
  • Scalability and auto-scaling considerations for models
  • Implementation of logging and error-tracking systems
  • Retraining strategies when model accuracy deteriorates
  • Exercise #5: Deploy and monitor a model

Session 6: Security and Compliance

  • Security aspects in MLOps: understanding the threats
  • Compliance considerations such as GDPR, HIPAA
  • Implementing authentication and authorization
  • Creating and using AIBOMs
  • Exercise #6: Setting up security measures and AIBOMs

Exercise #7: Putting it all together

Q&A and Wrap-Up

  • Summary and wrap-up of the course
  • References
  • Q&A session to address participant queries

Class Schedule

Sign-In/Registration 7:30 - 8:30 a.m.
Morning Session 8:30 a.m. - 12:00 p.m.
Lunch 12:00 - 1:00 p.m.
Afternoon Session 1:00 - 5:00 p.m.
Times represent the typical daily schedule. Please confirm your schedule at registration.

Class Fee Includes

• Digital course materials
• Continental breakfasts and refreshment breaks
• Lunches

Instructors
Dates
Mode
Location
Price
Aug 27Aug 28, 2024
Virtual Classroom
Virtual Classroom
$1,495
Dec 03Dec 04, 2024
Virtual Classroom
Virtual Classroom
$1,495
Price: $1,495 USD
Course Duration: 3 Days
Ways to Save

Software Summer School - BOGO - JUNE, JULY, & AUGUST CLASSES

Our Software Summer School is back in session! Buy One, Get One Free on our most popular live virtual training classes offered between June 1 and August 31, 2024. Take both classes yourself or split them with a colleague.

Our practical, hands-on courses can help you and your team in areas like AI, GitHub, agile, leadership, DevOps, software testing, and many more. Live virtual training allows you to dive deep into these topics with instructor-led classes from the comfort of your home or office.

Ready to join? Click the Register button below to register via email.

This is a limited time offer, and certain restrictions do apply. See details below.

OFFER DETAILS: Register for any participating live virtual class being offered in June, July, or August 2024 and get a second class of equal or lesser value free. Classes must take place between June 1 and August 31. Not all classes are valid. Contact Client Support for specifics on which classes are included. Register directly with our Client Support team at 929.777.8102 or through the email above. This offer is only valid for new registrations and cannot be combined with other promotions. Optional certification test vouchers must be purchased one per person who wishes to add them on and are NOT included in the Buy 1 Get 1 Free deal that is limited to just the class.
 
PREQUISITES: Many of our classes require previous skills, experience, or certifications to ensure understanding. Please be sure to check prerequisites for each class to ensure you have the experience necessary to actively participate. Questions? Call us at 929.777.8102.
 
Choose from the following classes.  ( = Guaranteed to run dates):
 
June Classes

July Classes

August Classes

 
  

 

Ways to Save

Software Summer School - BOGO - JUNE, JULY, & AUGUST CLASSES

Our Software Summer School is back in session! Buy One, Get One Free on our most popular live virtual training classes offered between June 1 and August 31, 2024. Take both classes yourself or split them with a colleague.

Our practical, hands-on courses can help you and your team in areas like AI, GitHub, agile, leadership, DevOps, software testing, and many more. Live virtual training allows you to dive deep into these topics with instructor-led classes from the comfort of your home or office.

Ready to join? Click the Register button below to register via email.

This is a limited time offer, and certain restrictions do apply. See details below.

OFFER DETAILS: Register for any participating live virtual class being offered in June, July, or August 2024 and get a second class of equal or lesser value free. Classes must take place between June 1 and August 31. Not all classes are valid. Contact Client Support for specifics on which classes are included. Register directly with our Client Support team at 929.777.8102 or through the email above. This offer is only valid for new registrations and cannot be combined with other promotions. Optional certification test vouchers must be purchased one per person who wishes to add them on and are NOT included in the Buy 1 Get 1 Free deal that is limited to just the class.
 
PREQUISITES: Many of our classes require previous skills, experience, or certifications to ensure understanding. Please be sure to check prerequisites for each class to ensure you have the experience necessary to actively participate. Questions? Call us at 929.777.8102.
 
Choose from the following classes.  ( = Guaranteed to run dates):
 
June Classes

July Classes

August Classes

 
  

 

Description

MLOps is the application of DevOps practices and principles to the machine learning (ML) process. In DevOps for AI and Machine Learning, DevOps engineers and testers will learn the foundational knowledge and practical skills necessary to integrate machine learning operations (MLOps) into existing AI Model development workflows.

This workshop covers the entire MLOps lifecycle, focusing on essential concepts, tools, and methodologies required to deploy and maintain machine learning models within DevOps environments.

Key takeaways from this class include:

  • Gaining an in-depth understanding of MLOps principles and their integration with DevOps practices.
  • Learning to set up automated data engineering pipelines.
  • Tracking model experiments effectively.
  • Implementing CI/CD pipelines for machine learning models.
  • Establishing robust monitoring and retraining strategies.
  • Navigating security and compliance considerations in MLOps.

Throughout this workshop, students will gain real-world context through practical hands-on exercises, such as setting up feature stores, implementing CI/CD processes for ML models, and deploying and monitoring models.

Who Should Attend
This workshop is ideal for DevOps engineers, software testers, and operations personnel looking to expand their skill set into MLOps. Professionals involved in software development, deployment, infrastructure management, quality assurance, or operations who wish to understand the unique challenges and best practices in deploying and maintaining machine learning models will benefit. It caters to individuals with a technical background in DevOps practices but with limited exposure to machine learning, aiming to bridge the gap between traditional DevOps workflows and the specialized requirements of MLOps.

Questions? 929.777.8102 [email protected]
Course Outline

Session 1: Introduction to MLOps

  • Definition and importance of MLOps
  • Cross-disciplinary collaboration
  • Key challenges in deploying and maintaining machine learning models
  • Machine Learning Process & Roles
  • The MLOps lifecycle
  • Exercise #1

Session 2: Automated Data Engineering

  • Data engineering process and ETL/ELT transformations
  • Understanding and managing data and feature sets
  • Introduction to feature stores and their role in data-centric ML pipelines
  • Exercise #2: Creating datasets and setting up a feature store

Session 3: Model experiment tracking

  • Model experimentation process
  • Testing and validating models with datasets
  • Introduction to collaborative notebooks
  • Capturing experimentation information
  • Deploying notebooks vs. model code
  • Exercise #3: Track experiments during model development

Session 4: CI/CD for MLOps

  • Introduction to CI/CD pipelines and their role in MLOps
  • Managing model and data versioning
  • Automating model deployment with CI/CD (models vs. code)

  • Integration testing and model evaluation
  • Model registries and best practices in deploying ML models (AB Testing, Canary)
  • Exercise #4: Setup CI/CD process and integrate testing

Session 5: Monitoring, Logging, and Retraining

  • Setting up monitoring systems for deployed ML models
  • Scalability and auto-scaling considerations for models
  • Implementation of logging and error-tracking systems
  • Retraining strategies when model accuracy deteriorates
  • Exercise #5: Deploy and monitor a model

Session 6: Security and Compliance

  • Security aspects in MLOps: understanding the threats
  • Compliance considerations such as GDPR, HIPAA
  • Implementing authentication and authorization
  • Creating and using AIBOMs
  • Exercise #6: Setting up security measures and AIBOMs

Exercise #7: Putting it all together

Q&A and Wrap-Up

  • Summary and wrap-up of the course
  • References
  • Q&A session to address participant queries

Class Fee Includes
  • Easy course access: Attend training right from your computer and easily connect your audio via computer or phone. Easy and quick access fits today’s working style and eliminates expensive travel and long days in the classroom.
  • Live, expert instruction: Instructors are sought-after practitioners, highly-experienced in the industry who deliver a professional learning experience in real-time. 
  • Valuable course materials: Courses cover the same professional content as our classroom training, and students have direct access to valuable materials. 
  • Rich virtual learning environment: A variety of tools are built in to the learning platform to engage learners through dynamic delivery and to facilitate a multi-directional flow of information.
  • Hands-on exercises: An essential component to any learning experience is applying what you have learned. Using the latest technology, your instructor can provide hands-on exercises, group activities, and breakout sessions. 
  • Real-time communication: Communicate real-time directly with the instructor. Ask questions, provide comments, and participate in the class discussions.
  • Peer interaction: Networking with peers has always been a valuable part of any classroom training. Live Virtual training gives you the opportunity to interact with and learn from the other attendees during breakout sessions, course lecture, and Q&A.
  • Small class size: Live Virtual courses are limited in small class size to ensure an opportunity for personal interaction.

Bring this course to your team at your site. Contact us to learn more at 929.777.8102.

Dates
Mode
Location
Price
Call to Schedule
Anytime
Your Location
Your Location
Course Duration: 3 Days
Description

MLOps is the application of DevOps practices and principles to the machine learning (ML) process. In DevOps for AI and Machine Learning, DevOps engineers and testers will learn the foundational knowledge and practical skills necessary to integrate machine learning operations (MLOps) into existing AI Model development workflows.

This workshop covers the entire MLOps lifecycle, focusing on essential concepts, tools, and methodologies required to deploy and maintain machine learning models within DevOps environments.

Key takeaways from this class include:

  • Gaining an in-depth understanding of MLOps principles and their integration with DevOps practices.
  • Learning to set up automated data engineering pipelines.
  • Tracking model experiments effectively.
  • Implementing CI/CD pipelines for machine learning models.
  • Establishing robust monitoring and retraining strategies.
  • Navigating security and compliance considerations in MLOps.

Throughout this workshop, students will gain real-world context through practical hands-on exercises, such as setting up feature stores, implementing CI/CD processes for ML models, and deploying and monitoring models.

Who Should Attend
This workshop is ideal for DevOps engineers, software testers, and operations personnel looking to expand their skill set into MLOps. Professionals involved in software development, deployment, infrastructure management, quality assurance, or operations who wish to understand the unique challenges and best practices in deploying and maintaining machine learning models will benefit. It caters to individuals with a technical background in DevOps practices but with limited exposure to machine learning, aiming to bridge the gap between traditional DevOps workflows and the specialized requirements of MLOps.

Questions? 929.777.8102 [email protected]
Course Outline

Session 1: Introduction to MLOps

  • Definition and importance of MLOps
  • Cross-disciplinary collaboration
  • Key challenges in deploying and maintaining machine learning models
  • Machine Learning Process & Roles
  • The MLOps lifecycle
  • Exercise #1

Session 2: Automated Data Engineering

  • Data engineering process and ETL/ELT transformations
  • Understanding and managing data and feature sets
  • Introduction to feature stores and their role in data-centric ML pipelines
  • Exercise #2: Creating datasets and setting up a feature store

Session 3: Model experiment tracking

  • Model experimentation process
  • Testing and validating models with datasets
  • Introduction to collaborative notebooks
  • Capturing experimentation information
  • Deploying notebooks vs. model code
  • Exercise #3: Track experiments during model development

Session 4: CI/CD for MLOps

  • Introduction to CI/CD pipelines and their role in MLOps
  • Managing model and data versioning
  • Automating model deployment with CI/CD (models vs. code)

  • Integration testing and model evaluation
  • Model registries and best practices in deploying ML models (AB Testing, Canary)
  • Exercise #4: Setup CI/CD process and integrate testing

Session 5: Monitoring, Logging, and Retraining

  • Setting up monitoring systems for deployed ML models
  • Scalability and auto-scaling considerations for models
  • Implementation of logging and error-tracking systems
  • Retraining strategies when model accuracy deteriorates
  • Exercise #5: Deploy and monitor a model

Session 6: Security and Compliance

  • Security aspects in MLOps: understanding the threats
  • Compliance considerations such as GDPR, HIPAA
  • Implementing authentication and authorization
  • Creating and using AIBOMs
  • Exercise #6: Setting up security measures and AIBOMs

Exercise #7: Putting it all together

Q&A and Wrap-Up

  • Summary and wrap-up of the course
  • References
  • Q&A session to address participant queries

Questions?

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