MLOps Fundamentals: CI/CD/CT Pipelines of ML with Azure Demo

MLOps 使用 Azure DevOps 和 Azure 机器学习的持续集成和持续交付 (CI/CD) 基础知识

本课程的目的是教授 MLOps 基础知识、核心思想、其原理、标准等，而不是 Azure ML。Azure 演示部分仅作为展示端到端 MLOps 项目工作的证明。

“MLOps 是一种在机器学习世界中定义了一套原则和指导方针的文化，用于机器学习模型的顺利实施和生产。”

长期以来，数据科学家一直在试验机器学习模型，但要提供真正的商业价值，它必须是可操作的，即将模型推向生产并根据业务目标衡量其性能。不幸的是，由于当前的挑战和机器学习生命周期的非系统化，80% 的模型从未投入生产，并且仅作为学术实验停滞不前。

作为该问题的解决方案出现的机器学习操作 (MLOps) 是市场上的一种新文化，也是一个快速增长的空间，其中包含将机器学习模型部署到生产中所需的一切，并且是实现这一需求的关键方面价值。

根据市场上的技术讨论，2021 年是 MLOps 之年，并将成为企业 ML 项目的必备技能集。

课程包括什么？

MLOps 核心基础知识和基础知识。

传统机器学习生命周期管理面临哪些挑战？

MLOps 如何解决这些问题，同时在 ML 过程中提供更多的灵活性和自动化。

MLOps 所依据的标准和原则。

MLOps 中的持续集成 (CI)、持续交付 (CD) 和持续训练 (CT) 管道。

与 MLOps 相关的各种成熟度级别。

MLOps 工具堆栈和 MLOps 平台比较。

Azure 机器学习组件的快速速成课程。

Genre: eLearning | MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 848 MB | Duration: 2h 53m
MLOps fundamentals of Continuous Integration & Continuous Delivery (CI/CD) using Azure DevOps & Azure Machine Learning

What you’ll learn
Basics of MLOps, benefits and its implementation.
Challenges faced by teams in the current way of handling Machine learning projects.
Importance of MLOps principles in Machine learning projects.
Standards and principles followed in MLOps culture.
What is continuous integration, continuous delivery and continuous training in MLOps space.
Various maturity levels associated with MLOps.
MLOps tools stack and various MLOps platforms comparison.
Run an end-to-end CI/CD MLOps pipeline using Azure DevOps & Azure Machine learning.

Description
Important Note: The intention of this course is to teach MLOps fundamentals, core idea, its principles, standards etc and NOT Azure ML. Azure demo section is just included as a proof to show the working of an end-to-end MLOps project.

“MLOps is a culture with set of principles, guidelines defined in a machine learning world for smooth implementation and productionization of Machine learning models.”

Data scientists have been experimenting with machine learning models from long time, but to provide the real business value, it must be operationalized i.e. push the models to production and measure their performance against business goals. Unfortunately, due to the current challenges and an non systemization in ML lifecycle 80% of the models never make it to production and remain stagnated as an academic experiment only.

Machine Learning Operations (MLOps), emerged as a solution to the problem, is a new culture in the market and a rapidly growing space that encompasses everything required to deploy a machine learning model into production, and is a crucial aspect to delivering this sought after value.

As per the tech talks in market, 2021 is the year of MLOps and would become the mandate skill set for Enterprise ML projects.

What’s included in the course ?

MLOps core basics and fundamentals.

What were the challenges in the traditional machine learning lifecycle management.

How MLOps is addressing those issues while providing more flexibility and automation in the ML process.

Standards and principles on which MLOps is based upon.

Continuous integration (CI), Continuous delivery (CD) and Continuous training (CT) pipelines in MLOps.

Various maturity levels associated with MLOps.

MLOps tools stack and MLOps platforms comparisons.

Quick crash course on Azure Machine learning components.

Run an end-to-end CI/CD MLOps pipeline for a case study in Azure using Azure DevOps & Azure Machine learning.

Who this course is for:
Data scientists
Data engineers
ML engineers
Devops engineers