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

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size855.5 MB
  • Uploaded Bytutsnode
  • Downloads61
  • Last checkedJul. 04th '21
  • Date uploadedJul. 01st '21
  • Seeders 13
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Description

Important Note: The intention of this course is to teach MLOps fundamentals and not Azure ML. Azure demo section is included as a proof to show the working of an end-to-end MLOps project. All the codes involved in pipeline are explained though.

“MLOps is a culture with set of principles, guidelines defined in 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, they must be operationalized i.e. push the models to production. 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.

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.
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

Requirements

Basics of DevOps & Machine learning

Last Updated 5/2021

Files:

MLOps Fundamentals CICDCT Pipelines of ML with Azure Demo [TutsNode.com] - MLOps Fundamentals CICDCT Pipelines of ML with Azure Demo 07 Azure Machine Learning Studio - Crash course
  • 002 Azure Machine learning studio UI Tour.mp4 (57.1 MB)
  • 002 Azure Machine learning studio UI Tour.en.srt (14.6 KB)
  • 001 Introduction to Azure Machine Learning.en.srt (6.5 KB)
  • 001 Introduction to Azure Machine Learning.mp4 (16.9 MB)
01 Introduction
  • 002 Traditional Machine Learning Lifecycle - Part 1.en.srt (14.9 KB)
  • 004 Roles & Responsibilities in ML projects.en.srt (7.2 KB)
  • 003 Traditional Machine Learning Lifecycle - Part 2.en.srt (7.0 KB)
  • 001 What is MLOps.en.srt (6.0 KB)
  • 002 Traditional Machine Learning Lifecycle - Part 1.mp4 (33.2 MB)
  • 003 Traditional Machine Learning Lifecycle - Part 2.mp4 (24.0 MB)
  • 004 Roles & Responsibilities in ML projects.mp4 (20.8 MB)
  • 001 What is MLOps.mp4 (13.9 MB)
02 Challenges in existing ML projects
  • 002 Activities needed to productionize models.en.srt (14.9 KB)
  • 001 Problems in traditional ML lifecycle.en.srt (8.0 KB)
  • 002 Activities needed to productionize models.mp4 (30.4 MB)
  • 001 Problems in traditional ML lifecycle.mp4 (20.9 MB)
03 MLOps - A solution
  • 002 MLOps implementation.en.srt (14.5 KB)
  • 004 Difference between DevOps & MLOps.en.srt (9.3 KB)
  • 001 Standards_Principles in MLOps.en.srt (8.3 KB)
  • 003 Benefits of MLOps.en.srt (7.3 KB)
  • 002 MLOps implementation.mp4 (39.7 MB)
  • 004 Difference between DevOps & MLOps.mp4 (23.3 MB)
  • 003 Benefits of MLOps.mp4 (18.2 MB)
  • 001 Standards_Principles in MLOps.mp4 (18.1 MB)
05 MLOps Tools_Platforms Stack
  • 001 MLOps Platform requirements.en.srt (14.4 KB)
  • 003 Which MLOps Platform to choose_.en.srt (9.4 KB)
  • 002 MLOps Platforms comparison.en.srt (7.2 KB)
  • 001 MLOps Platform requirements.mp4 (38.5 MB)
  • 003 Which MLOps Platform to choose_.mp4 (21.0 MB)
  • 002 MLOps Platforms comparison.mp4 (14.2 MB)
04 Maturity levels in MLOps
  • 001 MLOps level 0.en.srt (8.9 KB)
  • 002 MLOps level 1.en.srt (7.4 KB)
  • 004 Importance of Maturity levels.en.srt (2.1 KB)
  • 003 MLOps level 2.en.srt (3.6 KB)
  • 002 MLOps level 1.mp4 (22.3 MB)
  • 001 MLOps level 0.mp4 (17.8 MB)
  • 003 MLOps level 2.mp4 (10.5 MB)
  • 004 Importance of Maturity levels.mp4 (2.5 MB)
10 Demo - CI_CD MLOps Pipeline
  • 006 Run the Pipeline.en.srt (8.6 KB)
  • 002 Continuous Integration (CI) script.en.srt (6.6 KB)
  • 003 Code to publish the pipeline.en.srt (6.2 KB)
  • 004 Code to run the published package.en.srt (6.0 KB)
  • 005 Continuous Deployment (CD) script.en.srt (4.9 KB)
  • 001 Overview.en.srt (3.9 KB)
  • 006 Run the Pipeline.mp4 (47.0 MB)
  • 003 Code to publish the pipeline.mp4 (40.2 MB)
  • 004 Code to run the published package.mp4 (33.9 MB)
  • 002 Continuous Integration (CI) script.mp4 (32.2 MB)
  • 005 Continuous Deployment (CD) script.mp4 (26.4 MB)
  • 001 Overview.mp4 (9.3 MB)
09 Demo - Orchestrated Experiment ML codes
  • 004 Scoring code.en.srt (6.6 KB)
  • 001 Model Training code.en.srt (6.4 KB)
  • 002 Model Evaluation code.en.srt (6.1 KB)
  • 003 Model Registry code.en.srt (6.1 KB)
  • 001 Model Training code.mp4 (31.5 MB)
  • 003 Model Registry code.mp4 (31.2 MB)
  • 002 Model Evaluation code.mp4 (30.9 MB)
  • 004 Scoring code.mp4 (26.7 MB)
08 Demo - Data scientist's experiment
  • 001 EDA notebook.en.srt (5.9 KB)
  • 002 Azure DevOps & Azure ML connections.en.srt (4.8 KB)
  • 003 Training & Evaluation notebook.en.srt (4.5 KB)
  • external-assets-links.txt (0.1 KB)
  • 001 EDA notebook.mp4 (29.6 MB)
  • 003 Training & Evaluation notebook.mp4 (18.0 MB)
  • 002 Azure DevOps & Azure ML connections.mp4 (16.7 MB)
11 BONUS
  • 001 Bonus lecture.en.srt (4.1 KB)
  • external-assets-links.txt (0.7 KB)
  • 001 Bonus lecture.mp4 (19.4 MB)
06 Demo - Build & Run MLOps pipeline in Azure Cloud
  • 001 Notice.en.srt (3.8 KB)
  • 002 Project requirements.en.srt (2.6 KB)
  • external-assets-links.txt (0.2 KB)
  • 002 Project requirements.mp4 (6.6 MB)
  • 001 Notice.mp4 (5.0 MB)
  • TutsNode.com.txt (0.1 KB)
  • [TGx]Downloaded from torrentgalaxy.to .txt (0.6 KB)
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