Machine Learning Operations

This course will guide participants through a comprehensive exploration of machine learning model operations, focusing on MLOps and model lifecycle management. The initial segment covers essential MLOps components and best practices, providing participants with a strong foundation for effectively operationalizing machine learning models. In the latter part of the course, we will delve into the basics of the model lifecycle, demonstrating how to navigate it seamlessly using the Model Registry in conjunction with the Unity Catalog for efficient model management. By the course's conclusion, participants will have gained practical insights and a well-rounded understanding of MLOps principles, equipped with the skills needed to navigate the intricate landscape of machine learning model operations.

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

Skill Level

Associate

Duration

Prerequisites

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

Knowledge of fundamental concepts of machine learning
Knowledge of MLflow tracking
Familiarity with Databricks workspace and notebooks
Intermediate level knowledge of Python

Outline

Modern MLOps

Defining MLOps

MLOps on Databricks

Working with Asset Bundles

Architecting MLOps Solutions

Introduction

Opinionated MLOps Principles

Recommended MLOps Architectures

Model Testing Job with the Databricks CLI

Implementation and Monitoring MLOps Solution

Introduction

Implementation of MLOps Stacks

Type of Model Monitoring

Monitoring in Machine Learning

Lakehouse Monitoring Dashboard

Upcoming Public Classes

Date	Time	Language	Price
Date	Time	Language	Price	Dec 16	09 AM - 01 PM (America/Los_Angeles)	English	$750.00
Jan 27	01 PM - 05 PM (Europe/London)	English	$750.00
Jan 28	08 AM - 12 PM (America/New_York)	English	$750.00
Jan 30	01 PM - 05 PM (Asia/Kolkata)	English	$750.00

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Upcoming Public Classes

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Languages Available: English | 日本語 | Português BR

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

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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.