MLOps Zoomcamp
Objective
Teach practical aspects of productionizing ML services — from training and experimenting to model deployment and monitoring.
Target audience
Data scientists and ML engineers. Also, software engineers and data engineers interested in learning about putting ML in production.
Pre-requisites
- Python
- Docker
- Being comfortable with command line
- Prior exposure to machine learning (at work or from other courses, e.g. from ML Zoomcamp)
- Prior programming experience (at least 1+ year)
Syllabus
- Module 1: Introduction
- Module 2: Experiment tracking and model management
- Module 3: Orchestration and ML Pipelines
- Module 4: Model Deployment
- Module 5: Model Monitoring
- Module 6: Best Practices
- Project
Link: https://github.com/DataTalksClub/mlops-zoomcamp
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #machinelearning #modeldeployment #mlops #modelmonitoring #modelorchestration
@data_science_weekly
Objective
Teach practical aspects of productionizing ML services — from training and experimenting to model deployment and monitoring.
Target audience
Data scientists and ML engineers. Also, software engineers and data engineers interested in learning about putting ML in production.
Pre-requisites
- Python
- Docker
- Being comfortable with command line
- Prior exposure to machine learning (at work or from other courses, e.g. from ML Zoomcamp)
- Prior programming experience (at least 1+ year)
Syllabus
- Module 1: Introduction
- Module 2: Experiment tracking and model management
- Module 3: Orchestration and ML Pipelines
- Module 4: Model Deployment
- Module 5: Model Monitoring
- Module 6: Best Practices
- Project
Link: https://github.com/DataTalksClub/mlops-zoomcamp
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #machinelearning #modeldeployment #mlops #modelmonitoring #modelorchestration
@data_science_weekly
MLOps Guide by Arthur Olga, Gabriel Monteiro, Guilherme Leite and Vinicius Lima
This site is intended to be a MLOps Guide to help projects and companies to build more reliable MLOps environment. This guide should contemplate the theory behind MLOps and an implementation that should fit for most use cases.
What is MLOps?
MLOps is a methodology of operation that aims to facilitate the process of bringing an experimental Machine Learning model into production and maintaining it efficiently. MLOps focus on bringing the methodology of DevOps used in the software industry to the Machine Learning model lifecycle.
In that way we can define some of the main features of a MLOPs project:
- Data and Model Versioning
- Feature Management and Storing
- Automation of Pipelines and Processes
- CI/CD for Machine Learning
- Continuous Monitoring of Models
What does this guide cover?
- Introduction to MLOps Concepts
- Tutorial for Building a MLOps Environment
Link: Direct
Navigational hashtags: #armknowledgesharing #armguides
General hashtags: #mlops #ml #operations
@data_science_weekly
This site is intended to be a MLOps Guide to help projects and companies to build more reliable MLOps environment. This guide should contemplate the theory behind MLOps and an implementation that should fit for most use cases.
What is MLOps?
MLOps is a methodology of operation that aims to facilitate the process of bringing an experimental Machine Learning model into production and maintaining it efficiently. MLOps focus on bringing the methodology of DevOps used in the software industry to the Machine Learning model lifecycle.
In that way we can define some of the main features of a MLOPs project:
- Data and Model Versioning
- Feature Management and Storing
- Automation of Pipelines and Processes
- CI/CD for Machine Learning
- Continuous Monitoring of Models
What does this guide cover?
- Introduction to MLOps Concepts
- Tutorial for Building a MLOps Environment
Link: Direct
Navigational hashtags: #armknowledgesharing #armguides
General hashtags: #mlops #ml #operations
@data_science_weekly
DevOps for Data Science by Alex K Gold
In this book, you’ll learn about DevOps conventions, tools, and practices that can be useful to you as a data scientist. You’ll also learn how to work better with the IT/Admin team at your organization, and even how to do a little server administration of your own if you’re pressed into service.
Link: Direct
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #devops #mlops #datascience
@data_science_weekly
In this book, you’ll learn about DevOps conventions, tools, and practices that can be useful to you as a data scientist. You’ll also learn how to work better with the IT/Admin team at your organization, and even how to do a little server administration of your own if you’re pressed into service.
Link: Direct
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #devops #mlops #datascience
@data_science_weekly
Practitioners guide to MLOps: A framework for continuous delivery and automation of machine learning by Google Cloud
Across industries, DevOps and DataOps have been widely adopted as methodologies to improve quality and reduce the time to market of software engineering and data engineering initiatives. With the rapid growth in machine learning (ML) systems, similar approaches need to be developed in the context of ML engineering, which handle the unique complexities of the practical applications of ML. This is the domain of MLOps. MLOps is a set of standardized processes and technology capabilities for building, deploying, and operationalizing ML systems rapidly and reliably.
The document is in two parts. The first part, an overview of the MLOps lifecycle, is for all readers. It introduces MLOps processes and capabilities and why they’re important for successful adoption of ML-based systems.
The second part is a deep dive on the MLOps processes and capabilities. This part is for readers who want to understand the concrete details of tasks like running a continuous training pipeline, deploying a model, and monitoring predictive performance of an ML model.
Link: Book
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #mlops
Across industries, DevOps and DataOps have been widely adopted as methodologies to improve quality and reduce the time to market of software engineering and data engineering initiatives. With the rapid growth in machine learning (ML) systems, similar approaches need to be developed in the context of ML engineering, which handle the unique complexities of the practical applications of ML. This is the domain of MLOps. MLOps is a set of standardized processes and technology capabilities for building, deploying, and operationalizing ML systems rapidly and reliably.
The document is in two parts. The first part, an overview of the MLOps lifecycle, is for all readers. It introduces MLOps processes and capabilities and why they’re important for successful adoption of ML-based systems.
The second part is a deep dive on the MLOps processes and capabilities. This part is for readers who want to understand the concrete details of tasks like running a continuous training pipeline, deploying a model, and monitoring predictive performance of an ML model.
Link: Book
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #mlops