MLOps-Basics

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MLOps-Basics is an Open Source MLOps tool that provides resources for understanding and implementing basic MLOps concepts. It offers practical guidance on model building, monitoring, configurations, testing, packaging, and deployment.

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At a glance

Pricing

Free · Open Source

Free tier

Yes

API

Skill level

Technical

About

What is MLOps-Basics?

MLOps-Basics is an open-source GitHub repository designed to help users understand and implement fundamental MLOps concepts. It demystifies complex MLOps principles by breaking them down into practical, week-by-week topics. The repository covers essential areas such as project setup, model monitoring with Weights and Biases, configuration management using Hydra, and data version control with DVC. It also delves into model packaging using ONNX and Docker, continuous integration/continuous deployment (CI/CD) with GitHub Actions, container registry management with AWS ECR, serverless deployment via AWS Lambda, and prediction monitoring using Kibana. This resource is ideal for individuals looking to build and deploy robust machine learning pipelines.

Best used for

Ideal for developers and data scientists who need to learn and implement MLOps practices, deploy machine learning models, and monitor their performance in production. Especially valuable for those seeking practical, hands-on examples for setting up robust ML pipelines.

Common actions

learn MLOps

implement MLOps practices

deploy machine learning models

monitor model performance

manage ML configurations

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Capabilities

Key features

Model monitoring
Configuration management
Data version control
Model packaging
CI/CD implementation
Serverless deployment
Prediction monitoring

Target Audience

developerdata scientist

Integrations

githubweights-and-biaseshydradvconnxdockeraws-ecraws-lambda+ 1 more

Pricing & Plans

Free · Open Source

Free

FAQs

What specific MLOps topics are covered in MLOps-Basics?

MLOps-Basics covers a wide range of topics including project setup, model monitoring with Weights and Biases, configuration management with Hydra, data version control using DVC, and model packaging with ONNX and Docker. It also includes CI/CD with GitHub Actions, AWS ECR, AWS Lambda, and prediction monitoring with Kibana.

What technologies are used in the MLOps-Basics examples?

The project utilizes various technologies such as Huggingface Datasets and Transformers, Pytorch Lightning, Weights and Biases, Hydra, DVC, ONNX, Docker, FastAPI, GitHub Actions, AWS S3, AWS ECR, AWS Lambda, Cloudwatch Logs, Elastic Search, and Kibana.

Is MLOps-Basics suitable for beginners in MLOps?

Yes, MLOps-Basics is designed to help users understand the fundamental concepts of MLOps. It breaks down complex topics into weekly modules with blog posts and practical examples, making it accessible for those new to implementing MLOps pipelines.

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