What is MLOps?
MLOps is a cutting-edge methodology that helps organizations streamline their machine learning pipeline - from data collection and processing to model training and deployment. With MLOps, businesses can achieve greater efficiency and productivity in their ML efforts. At our company, we use a combination of the latest MLOps technologies to construct an infrastructure that can handle all of these tasks efficiently and effectively.
MLOps have been proven to increase the productivity of a machine learning engineer by 10x.
Features of our ML infrastructure setup
Ease of Management
- Combined data, code and model versioning.
- Experiment tracking and comparison.
- Role based access management to project data and experiments.
- Easily scalable to hardware and cloud services.
- Centralized model storage and one-click deployment.
- Automated queueing of training jobs and alerts for when the job finishes.
Ease of Use
Steps for End-To-End MLOps Implementation
- Analyze the current ML development cycle, available hardware and software resources, and your infrastructure.
- Map business expectations, identify ML capabilities and constraints.
- Devise MLOps implementation strategy.
- Deciding ML model evaluation metrics to automate model comparison.
- Advise on the data centric approach for model development and integration of continuous model testing into the CI/CD pipeline.
- Design an automated monitoring solution.
- Locate MLOps-specific challenges and elaborate on their solutions.
- Mentor your ML engineers on how to use MLOps infrastructure to get the most productivity.