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

Don't see a date that fits your schedule? Contact us for scheduling options at 929.777.8102


Select a learning mode button (Public, Live Virtual, etc.) for pricing, details, and a downloadable fact sheet.
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 AI/ML and MLOps

  • What is Artificial Intelligence (AI)?
  • Concerns in AI and Machine Learning (ML)
  • Steps and challenges in the ML process
  • Definition and importance of MLOps
  • Key MLOps activities
  • Example AI-based application
  • Exercise #1: Review example application

Session 2: MLOps During Model Development

  • What is model development?
  • Model experimentation process
  • Exercise #2: Using Jupyter notebooks
  • Creation of training datasets
  • Model experimentation
  • Experiment tracking
  • Exercise #3: Experiment track with mlflow
  • Model training pipelines
  • Exercise #4: Using nbconvert to export notebook code

Session 3: MLOps During Model Testing and Deployment

  • Approaches to testing and deploying AI models
  • Types of testing to perform
  • Model management
  • Using a model registry for model tracking
  • Exercise #5: Using mlflow to register and track models
  • Continuous delivery / deployment process
  • Exercise #6: Test and deploy models using mlflow and pytest
  • Online experimentation
  • Integration of AI models into applications

MLOps During Model Inference and Monitoring

  • Prediction serving process
  • Model monitoring
  • Types of model monitoring
  • Dealing with model decay
  • Exercise #7: Identify types of model decay
  • Model retraining

Session 5: MLOps for Dataset and Feature Engineering

  • What are features?
  • Defining and managing features
  • Dataset management
  • Using feature stores
  • Exercise #8: Identifying steps in the dataset and feature management process

Session 6: Model Governance and Compliance

  • Model governance vs. compliance
  • Types of model governance
  • Explainability
  • Fairness and bias
  • Data security and privacy
  • Model compliance standards
  • Exercise #9: Identifying types of governance

Putting It All Together and Next Steps

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

Don't see a date that fits your schedule? Contact us for scheduling options at 929.777.8102


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 AI/ML and MLOps

  • What is Artificial Intelligence (AI)?
  • Concerns in AI and Machine Learning (ML)
  • Steps and challenges in the ML process
  • Definition and importance of MLOps
  • Key MLOps activities
  • Example AI-based application
  • Exercise #1: Review example application

Session 2: MLOps During Model Development

  • What is model development?
  • Model experimentation process
  • Exercise #2: Using Jupyter notebooks
  • Creation of training datasets
  • Model experimentation
  • Experiment tracking
  • Exercise #3: Experiment track with mlflow
  • Model training pipelines
  • Exercise #4: Using nbconvert to export notebook code

Session 3: MLOps During Model Testing and Deployment

  • Approaches to testing and deploying AI models
  • Types of testing to perform
  • Model management
  • Using a model registry for model tracking
  • Exercise #5: Using mlflow to register and track models
  • Continuous delivery / deployment process
  • Exercise #6: Test and deploy models using mlflow and pytest
  • Online experimentation
  • Integration of AI models into applications

MLOps During Model Inference and Monitoring

  • Prediction serving process
  • Model monitoring
  • Types of model monitoring
  • Dealing with model decay
  • Exercise #7: Identify types of model decay
  • Model retraining

Session 5: MLOps for Dataset and Feature Engineering

  • What are features?
  • Defining and managing features
  • Dataset management
  • Using feature stores
  • Exercise #8: Identifying steps in the dataset and feature management process

Session 6: Model Governance and Compliance

  • Model governance vs. compliance
  • Types of model governance
  • Explainability
  • Fairness and bias
  • Data security and privacy
  • Model compliance standards
  • Exercise #9: Identifying types of governance

Putting It All Together and Next Steps

  • Comprehensive ML workflow
  • 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

Don't see a date that fits your schedule? Contact us for scheduling options at 929.777.8102


Price: $1,495 USD
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 AI/ML and MLOps

  • What is Artificial Intelligence (AI)?
  • Concerns in AI and Machine Learning (ML)
  • Steps and challenges in the ML process
  • Definition and importance of MLOps
  • Key MLOps activities
  • Example AI-based application
  • Exercise #1: Review example application

Session 2: MLOps During Model Development

  • What is model development?
  • Model experimentation process
  • Exercise #2: Using Jupyter notebooks
  • Creation of training datasets
  • Model experimentation
  • Experiment tracking
  • Exercise #3: Experiment track with mlflow
  • Model training pipelines
  • Exercise #4: Using nbconvert to export notebook code

Session 3: MLOps During Model Testing and Deployment

  • Approaches to testing and deploying AI models
  • Types of testing to perform
  • Model management
  • Using a model registry for model tracking
  • Exercise #5: Using mlflow to register and track models
  • Continuous delivery / deployment process
  • Exercise #6: Test and deploy models using mlflow and pytest
  • Online experimentation
  • Integration of AI models into applications

MLOps During Model Inference and Monitoring

  • Prediction serving process
  • Model monitoring
  • Types of model monitoring
  • Dealing with model decay
  • Exercise #7: Identify types of model decay
  • Model retraining

Session 5: MLOps for Dataset and Feature Engineering

  • What are features?
  • Defining and managing features
  • Dataset management
  • Using feature stores
  • Exercise #8: Identifying steps in the dataset and feature management process

Session 6: Model Governance and Compliance

  • Model governance vs. compliance
  • Types of model governance
  • Explainability
  • Fairness and bias
  • Data security and privacy
  • Model compliance standards
  • Exercise #9: Identifying types of governance

Putting It All Together and Next Steps

  • Comprehensive ML workflow
  • 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.

Don't see a date that fits your schedule? Contact us for scheduling options at 929.777.8102


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 AI/ML and MLOps

  • What is Artificial Intelligence (AI)?
  • Concerns in AI and Machine Learning (ML)
  • Steps and challenges in the ML process
  • Definition and importance of MLOps
  • Key MLOps activities
  • Example AI-based application
  • Exercise #1: Review example application

Session 2: MLOps During Model Development

  • What is model development?
  • Model experimentation process
  • Exercise #2: Using Jupyter notebooks
  • Creation of training datasets
  • Model experimentation
  • Experiment tracking
  • Exercise #3: Experiment track with mlflow
  • Model training pipelines
  • Exercise #4: Using nbconvert to export notebook code

Session 3: MLOps During Model Testing and Deployment

  • Approaches to testing and deploying AI models
  • Types of testing to perform
  • Model management
  • Using a model registry for model tracking
  • Exercise #5: Using mlflow to register and track models
  • Continuous delivery / deployment process
  • Exercise #6: Test and deploy models using mlflow and pytest
  • Online experimentation
  • Integration of AI models into applications

MLOps During Model Inference and Monitoring

  • Prediction serving process
  • Model monitoring
  • Types of model monitoring
  • Dealing with model decay
  • Exercise #7: Identify types of model decay
  • Model retraining

Session 5: MLOps for Dataset and Feature Engineering

  • What are features?
  • Defining and managing features
  • Dataset management
  • Using feature stores
  • Exercise #8: Identifying steps in the dataset and feature management process

Session 6: Model Governance and Compliance

  • Model governance vs. compliance
  • Types of model governance
  • Explainability
  • Fairness and bias
  • Data security and privacy
  • Model compliance standards
  • Exercise #9: Identifying types of governance

Putting It All Together and Next Steps

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

Questions?

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