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Machine Learning Model Deployment

This course is designed to introduce three primary machine learning deployment strategies and illustrate the implementation of each strategy on Databricks. Following an exploration of the fundamentals of model deployment, the course delves into batch inference, offering hands-on demonstrations and labs for utilizing a model in batch inference scenarios, along with considerations for performance optimization. The second part of the course comprehensively covers pipeline deployment, while the final segment focuses on real-time deployment. Participants will engage in hands-on demonstrations and labs, deploying models with Model Serving and utilizing the serving endpoint for real-time inference.


Languages Available: English | 日本語 | Português BR | 한국어

Skill Level
Associate
Duration
4h
Prerequisites

At a minimum, you should be familiar with the following before attempting to take this content:

  • Knowledge of fundamental machine learning models

  • Knowledge of model lifecycle and MLflow components

  • Familiarity with Databricks workspace and notebooks

  • Intermediate level knowledge of Python

Outline

  • Model Deployment Strategies
  • Model Deployment with MLflow
  • Introduction to Batch Deployment
  • Introduction to Pipeline Deployment
  • Introduction to Real-time Deployment
  • Databricks Model Serving





Upcoming Public Classes

Date
Time
Language
Price
Jan 13
08 AM - 12 PM (Europe/London)
English
$750.00
Jan 14
01 PM - 05 PM (Europe/London)
English
$750.00
Jan 15
09 AM - 01 PM (America/Los_Angeles)
English
$750.00
Jan 16
08 AM - 12 PM (Asia/Kolkata)
English
$750.00

Public Class Registration

If your company has purchased success credits or has a learning subscription, please fill out the Training Request form. Otherwise, you can register below.

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If your company is interested in private training, please submit a request.

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Registration options

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Skills@Scale

Comprehensive training offering for large scale customers that includes learning elements for every style of learning. Inquire with your account executive for details

Upcoming Public Classes

Generative AI Engineer

Generative AI Engineering with Databricks

This course is aimed at data scientists, machine learning engineers, and other data practitioners who want to build generative AI applications using the latest and most popular frameworks and Databricks capabilities. 

Below, we describe each of the four, four-hour modules included in this course.

Generative AI Solution Development: This is your introduction to contextual generative AI solutions using the retrieval-augmented generation (RAG) method. First, you’ll be introduced to RAG architecture and the significance of contextual information using Mosaic AI Playground. Next, we’ll show you how to prepare data for generative AI solutions and connect this process with building a RAG architecture. Finally, you’ll explore concepts related to context embedding, vectors, vector databases, and the utilization of Mosaic AI Vector Search.

Generative AI Application Development: Ready for information and practical experience in building advanced LLM applications using multi-stage reasoning LLM chains and agents? In this module, you'll first learn how to decompose a problem into its components and select the most suitable model for each step to enhance business use cases. Following this, we’ll show you how to construct a multi-stage reasoning chain utilizing LangChain and HuggingFace transformers. Finally, you’ll be introduced to agents and will design an autonomous agent using generative models on Databricks.

Generative AI Application Evaluation and Governance: This is your introduction to evaluating and governing generative AI systems. First, you’ll explore the meaning behind and motivation for building evaluation and governance/security systems. Next, we’ll connect evaluation and governance systems to the Databricks Data Intelligence Platform. Third, we’ll teach you about a variety of evaluation techniques for specific components and types of applications. Finally, the course will conclude with an analysis of evaluating entire AI systems with respect to performance and cost.

Generative AI Application Deployment and Monitoring: Ready to learn how to deploy, operationalize, and monitor generative deploying, operationalizing, and monitoring generative AI applications? This module will help you gain skills in the deployment of generative AI applications using tools like Model Serving. We’ll also cover how to operationalize generative AI applications following best practices and recommended architectures. Finally, we’ll discuss the idea of monitoring generative AI applications and their components using Lakehouse Monitoring.

Paid
16h
Lab
instructor-led
Associate

Questions?

If you have any questions, please refer to our Frequently Asked Questions page.