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

  • Easy to use data exploration interface.
  • Integrations with various cloud providers for training models with automated start-stop instances.
  • Logging and visualization of various useful metrics and resource consumption.
  • Generate reports for business and technical people.
  • One-click access to jupyter notebooks.
  • Monitor deployed model for business metrics.

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.

Create RoadMap

  • 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.