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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
4h
Prerequisites

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

• Familiarity with the Databricks Data Intelligence Platform and basic workspace operations (create clusters, run code in notebooks, use basic notebook operations, import repos from git)

• Intermediate programming experience with Python, including data manipulation libraries (pandas, numpy) and working with APIs (REST endpoints, JSON payloads)

• Basic knowledge of MLflow for experiment tracking, model logging, model registry operations, and model lifecycle management

• Understanding of machine learning fundamentals, including model training, evaluation, deployment workflows, and performance monitoring concepts

• Familiarity with MLOps concepts, including data quality assessment, feature engineering, model testing, and continuous monitoring practices

• Basic experience with command-line interfaces and authentication setup for cloud platforms and development tools

• Understanding of Lakeflow Jobs and workflow orchestration concepts (task dependencies, conditional logic, scheduling, notifications)

• Basic knowledge of model monitoring and drift detection principles including performance metrics and anomaly detection

Outline

Modern MLOps

• Defining MLOps

• MLOps on Databricks


Architecting MLOps Solutions

• Opinionated MLOps Principles

• Recommended MLOps Architectures


Monitoring MLOps Solutions

• Type of Model Monitoring

• Monitoring in Machine Learning

Upcoming Public Classes

Date
Time
Your Local Time
Language
Price
Oct 22
09 AM - 01 PM (Europe/Paris)
-
English
$750.00
Oct 22
01 PM - 05 PM (America/New_York)
-
English
$750.00
Oct 29
09 AM - 01 PM (Asia/Kolkata)
-
English
$750.00
Nov 06
01 PM - 05 PM (Australia/Sydney)
-
English
$750.00
Nov 06
09 AM - 01 PM (Europe/Paris)
-
English
$750.00
Nov 06
09 AM - 01 PM (America/Los_Angeles)
-
English
$750.00
Dec 04
09 AM - 01 PM (Asia/Kolkata)
-
English
$750.00
Dec 04
01 PM - 05 PM (Europe/Paris)
-
English
$750.00
Dec 04
09 AM - 01 PM (America/New_York)
-
English
$750.00
Jan 05
09 AM - 01 PM (Europe/Paris)
-
English
$750.00
Jan 08
09 AM - 01 PM (Asia/Singapore)
-
English
$750.00
Jan 08
01 PM - 05 PM (America/New_York)
-
English
$750.00

Public Class Registration

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Upcoming Public Classes

Databricks Performance Optimization - Mandarin Chinese

Databricks Performance Optimization 课程向数据工程师和分析师讲授如何在 Databricks Data Intelligence Platform 上诊断、衡量和修复性能瓶颈,以及如何将性能改进与成本关联起来。本课程遵循"先衡量、先利用平台"的工作流:让平台的自动优化功能承担繁重工作,通过 Query Profile 和系统表验证已应用的优化,再在必要时进行手动调优。

学员将以 Query Profile、Performance Insights 和查询历史记录系统表为基础,建立衡量性能的能力。在此基础上,他们将探索关键优化技术,包括数据布局与自调优托管表、liquid clustering、缓存与中间结果、shuffle、数据倾斜、溢出、行爆炸、驱动程序性能、Python UDF、serverless compute、Photon 以及成本归因。

在整个课程中,学员将针对合成零售数据中刻意设计的慢查询进行探索,并观察不同优化技术对性能的影响。他们将利用文件裁剪、任务执行时间、Photon 覆盖率和成本等依据来评估改进效果。两个基于场景的实验将强化从诊断到验证的完整优化工作流。

注意:Databricks Academy 正在将 Databricks 环境中的课堂教学转为基于 notebook 的形式,不再使用幻灯片进行授课。您可以在 Vocareum 实验环境中访问课程 notebook。

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

Paid
4h
Lab
instructor-led
Professional

Questions?

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