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

Monitoring

Model and data versioning

Hybrid teams

Automation

MLOps Predictions for 2022

Best MLOps Tools and Platforms

‍Conclusion

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Software Developer and Technical Writer.

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Harshil Patel

Harshil Patel

Software Developer and Technical Writer.

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