jagadeshchilla/MLOPS
This is self study about the MLOPS
This project offers a structured learning path for data scientists and machine learning engineers to master the full lifecycle of machine learning operations (MLOps). It guides users through developing robust machine learning models, tracking experiments, managing data versions, and deploying models to cloud environments like AWS and Azure. By completing the curriculum, users will gain the skills to take raw data, build predictive models, and implement them into production systems.
No commits in the last 6 months.
Use this if you are a data scientist or machine learning engineer looking for a comprehensive, hands-on guide to building and deploying machine learning models in a production-ready MLOps framework.
Not ideal if you are looking for a plug-and-play solution or a quick tool to solve a specific, isolated problem without diving into the underlying MLOps principles.
Stars
8
Forks
2
Language
Jupyter Notebook
License
—
Category
Last pushed
Jul 05, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/mlops/jagadeshchilla/MLOPS"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
apache/airflow
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
mlrun/mlrun
MLRun is an open source MLOps platform for quickly building and managing continuous ML...
clearml/clearml
ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data...
argoproj-labs/hera
Hera makes Python code easy to orchestrate on Argo Workflows through native Python integrations....
argoproj/argo-workflows
Workflow Engine for Kubernetes