Five Reasons Why Companies Have To Adopt MLOps In 2022

What is MLOps?

  • Faster experimentation and model development.
  • Faster deployment of updated models into production.
  • Quality Assurance.
ML Project Lifecycle | Image By Author

Why Should Companies Adopt MLOps?

Rapid deployment

Scalability and management

Reusability and reproducibility

Better use of data

Reduced risk and bias

An image showing the intersection of machine learning, DevOps, and data engineering — that makes up MLOps | Image Source

MLOps Challenges and Solutions

Deployment and data preparation challenges

Lack of reusability and consistency

Lack of model versioning

Limited reliability

Observability issues

The Censius AI Observability Platform’s Dashboard
  • Observe the entire ML pipeline.
  • Analyze and improve the models.
  • Receive real-time alerts for monitor violations.
  • Compare a model’s historical performance and a lot more.
  • Detect unwanted bias and fix models.‍

Best Practices for MLOps

Data validation


Model and data versioning

Hybrid teams


MLOps Predictions for 2022

Best MLOps Tools and Platforms





Software Developer and Technical Writer.

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

Fun with NLP

Statistical Language Models

Hello, Video Codec!


Variational Quantum Eigensolvers

What goes on inside NLP neural nets?

Machine-learned Word Embeddings

Bias and Variance in Machine Learning

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Harshil Patel

Harshil Patel

Software Developer and Technical Writer.

More from Medium

How to use MLflow to Track and Structure Machine Learning Projects?

Applying Machine Learning for A/B Testing: Ad Campaign Performance

Design Ideas for Frustrated ML Engineers

Spark of Data Engineers