MLOps I

MLOps I

Deploy, manage, monitor and govern AutoML models.

About this mission

This mission will enable you to productize MLDev models through MLOps. You will learn about the different ML model types managed in MLOps and their different deployment options, with emphasis on MLDev models. You will learn how the different components in MLOps can be used to deploy, monitor, manage and govern MLDev models to meet business objectives effectively.

You will get hands-on experience in the productization workflow of MLDev models as you work through the practical sections of this class. 

Format and duration: virtual instructor-led, hands-on, 3.5 hours

By the end of this mission, you will be able to:

  • Perform MLDev deployment workflows in MLOps
  • Monitor service health, data drift, and model accuracy in DataRobot’s MLOps
  • Use segment analysis to pinpoint the causes of ML model service issues
  • Interpret the monitoring visualizations in MLOps 
  • Use Humble AI to manage model risk for MLDev models
  • Set up MLDev challenger models in MLOps
  • Set monitoring notifications and alerts and their associated thresholds
  • Use the Prediction Real-time API and the Batch Prediction API to score data
  • Replace stale MLDev models using MLOps

Who should complete this mission?

This mission is intended for people with a technical background who will be in charge of deploying and managing ML models in production.

Before embarking on this mission, you should complete one of the following:

You only need to complete one of these two missions if you do not have previous experience in the productization of ML models.

Technical requirements

  • Chrome browser
  • The latest version of Zoom
  • During class, you will connect to the DataRobot classroom account and download files. If your organization uses security protocols that block connecting to the classroom account or downloading files (for example, a firewall or VPN), please connect to the session using a personal computer.

Completion requirements

Upon completing this mission, you will receive a Certificate of Completion. Attendance of the instructor-led session as well as passing the skills assessment are required for mission completion,

Please use this time zone conversion tool to find a session that works best for your schedule and time zone: worldtimebuddy.com

PST/PDT: Americas/Pacific (San Francisco/Seattle) 
EST/EDT: Americas/Eastern (Boston/New York/Washington DC)
GMT/BST: Europe/UK (London) 
SGT: Asia/Singapore (Singapore)
AEST/AEDT: Australia/Eastern (Sydney/Melbourne)   

Important Note

To ensure participants are properly configured and enabled across our internal systems, session registration closes 2 business days prior to the start of the session. Please refer to our Education Services Policies for additional info.

MLOps I (3.5 hrs)

Instructor-led Session Date Spaces left
MLOps I 14
MLOps I 8
MLOps I 19
MLOps I 18
MLOps I 20
MLOps I 20
MLOps I 20

Action Items1 Session

  • Instructor-led Session (Virtual)
  • MLOps I (3.5 hrs)
  • Complete Your Mission
  • Assess Your Skills
  • Give Us Your Feedback
  • What's Next...
  • Resources
  • MLOps I resources

About this mission

This mission will enable you to productize MLDev models through MLOps. You will learn about the different ML model types managed in MLOps and their different deployment options, with emphasis on MLDev models. You will learn how the different components in MLOps can be used to deploy, monitor, manage and govern MLDev models to meet business objectives effectively.

You will get hands-on experience in the productization workflow of MLDev models as you work through the practical sections of this class. 

Format and duration: virtual instructor-led, hands-on, 3.5 hours

By the end of this mission, you will be able to:

  • Perform MLDev deployment workflows in MLOps
  • Monitor service health, data drift, and model accuracy in DataRobot’s MLOps
  • Use segment analysis to pinpoint the causes of ML model service issues
  • Interpret the monitoring visualizations in MLOps 
  • Use Humble AI to manage model risk for MLDev models
  • Set up MLDev challenger models in MLOps
  • Set monitoring notifications and alerts and their associated thresholds
  • Use the Prediction Real-time API and the Batch Prediction API to score data
  • Replace stale MLDev models using MLOps

Who should complete this mission?

This mission is intended for people with a technical background who will be in charge of deploying and managing ML models in production.

Before embarking on this mission, you should complete one of the following:

You only need to complete one of these two missions if you do not have previous experience in the productization of ML models.

Technical requirements

  • Chrome browser
  • The latest version of Zoom
  • During class, you will connect to the DataRobot classroom account and download files. If your organization uses security protocols that block connecting to the classroom account or downloading files (for example, a firewall or VPN), please connect to the session using a personal computer.

Completion requirements

Upon completing this mission, you will receive a Certificate of Completion. Attendance of the instructor-led session as well as passing the skills assessment are required for mission completion,

Please use this time zone conversion tool to find a session that works best for your schedule and time zone: worldtimebuddy.com

PST/PDT: Americas/Pacific (San Francisco/Seattle) 
EST/EDT: Americas/Eastern (Boston/New York/Washington DC)
GMT/BST: Europe/UK (London) 
SGT: Asia/Singapore (Singapore)
AEST/AEDT: Australia/Eastern (Sydney/Melbourne)   

Important Note

To ensure participants are properly configured and enabled across our internal systems, session registration closes 2 business days prior to the start of the session. Please refer to our Education Services Policies for additional info.

Instructor-led Sessions

MLOps I (3.5 hrs)

Instructor-led Session Date Spaces left
MLOps I 14
MLOps I 8
MLOps I 19
MLOps I 18
MLOps I 20
MLOps I 20
MLOps I 20

Action Items1 Session

  • Instructor-led Session (Virtual)
  • MLOps I (3.5 hrs)
  • Complete Your Mission
  • Assess Your Skills
  • Give Us Your Feedback
  • What's Next...
  • Resources
  • MLOps I resources