BentoML Foundation

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2 Days

Available On-Site

Available Virtually

Contact Us for Open Enrollment
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Customizable

BentoML Foundation is a two-day, hands-on course that teaches data and technology professionals how to turn machine learning models into scalable production services. Participants learn BentoML’s architecture and get hands-on practice configuring and managing its components, packaging trained models, and deploying them as production-ready services. By the end, attendees have the skills to move machine learning workflows out of experimentation and into reliable, scalable production systems.

Day 1 opens with a BentoML overview before moving into building and deploying a Bento. A Bento is the packaged unit that bundles a trained model with its runtime dependencies. Participants then work hands-on with Bentos, building and managing models so they can package their own training artifacts for serving. Day 2 shifts toward running BentoML at scale. It starts with services and runners that handle inference workloads, along with the clients and servers that talk to deployed endpoints. Orchestration patterns for coordinating multiple models follow. The course closes with monitoring, logging, and metrics, giving attendees the observability practices needed to keep production model services healthy.

Who Should Attend

Developers, Data Scientists, Data Engineers, Machine Learning Engineers, MLOps professionals

What Attendees Will Learn

Upon completing BentoML Foundation, participants will be able to:

  • Build an in-depth understanding of BentoML, from its architecture, deployment and use
  • Acquire hands-on configuring and managing BentoML and its components
  • Learn how to package, manage and serve models as Bentos
  • Understand how to integrate BentoML into existing infrastructure
  • Develop the skills and expertise to transform machine learning models into production grade end-to-end solutions

Prerequisites

Participants should have intermediate programming skills (preferably in Python), an understanding of mathematics and statistics, basic familiarity with machine learning, a grasp of computer science fundamentals, familiarity with common operating systems and basic command-line operations, and a willingness to review pre-course materials.

These prerequisites will help participants engage effectively with the course material and hands-on labs, making the learning experience more rewarding.

Delivery

Available for Instructor-Led (ILT) in-person/onsite training or Virtual Instructor-Led training (VILT) delivery.

Each attendee will require the ability to ssh into a cloud hosted virtual machine (provided with the course). In environments where SSH is not possible, local lab VMs or browser accessible lab systems can be provided. For web-based delivery, participants require an Internet-connected computer capable of teleconferencing.

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