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Machine Learning in Production

In this course, you will learn the best practices for managing machine learning experiments and models with MLflow. There are two main components in this course: (i) using MLflow to track the machine learning lifecycle, package models for deployment, and manage model versions (ii) examining various production issues, different deployment paradigms, and post-production concerns. By the end of this course, you will have built an end-to-end pipeline to log, deploy, and monitor machine learning models.

   
Skill Level
Professional
Duration
8h
Prerequisites
Intermediate experience with Python and pandas, Working knowledge of machine learning and data science (scikit-learn, TensorFlow, etc.), Familiarity with Apache Spark

Outline

Day 1

  • ML in production overview    
  • Data management with Delta and Databricks Feature Store    
  • Experiment tracking and versioning with MLflow Tracking    
  • Model management with MLflow Models and Model Registry    
  • Automated testing with webhooks    
  • Deployment paradigms     
  • Monitoring and CI/CD

Upcoming Public Classes

Date
Time
Language
Price
Dec 12 - 13
09 AM - 01 PM (Australia/Sydney)
English
$1000.00
Dec 12
09 AM - 05 PM (Europe/Paris)
English
$1000.00
Dec 18
09 AM - 05 PM (America/New_York)
English
$1000.00
Dec 23 - 24
09 AM - 01 PM (Asia/Singapore)
English
$1000.00
Jan 10
09 AM - 05 PM (Asia/Kolkata)
English
$1000.00
Jan 10
09 AM - 05 PM (Europe/Paris)
English
$1000.00
Jan 10
09 AM - 05 PM (America/Los_Angeles)
English
$1000.00
Jan 13 - 14
09 AM - 01 PM (Asia/Kolkata)
English
$1000.00
Jan 15
09 AM - 05 PM (Europe/Paris)
English
$1000.00
Jan 28
09 AM - 05 PM (Europe/London)
English
$1000.00
Feb 05
09 AM - 05 PM (Europe/Paris)
English
$1000.00
Feb 12 - 13
01 PM - 05 PM (America/New_York)
English
$1000.00
Feb 14
09 AM - 05 PM (Europe/Paris)
English
$1000.00
Feb 19
09 AM - 05 PM (America/Los_Angeles)
English
$1000.00
Feb 20
09 AM - 05 PM (Australia/Sydney)
English
$1000.00
Feb 27 - 28
11 AM - 03 PM (Asia/Singapore)
English
$1000.00

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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Blended Learning

Self-paced and weekly instructor-led sessions for every style of learner to optimize course completion and knowledge retention. Go to Subscriptions Catalog tab to purchase

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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.