Is your enterprise facing a gap between machine learning development and enterprise production deployment? Is time-to-deployment too high? Is Data Scientists being too much dependent on IT for operationalizing their model ?

MLOps is the bridge. It is an engineering culture and practice that aims at unifying ML system development and ML System operations to facilitate data processing, ML pipeline, model training, experimentation, model evaluation, model registry, model deployment, model monitoring, and model serving and scaling.

Why do enterprises need MLOps?

“According to a recent study by NewVantage Partners, of 70 leading enterprise companies, only 15% have deployed AI capabilities into widespread production. AI that is not deployed to generate value is only a very costly experiment. These experiments are complex technical accomplishments, but they don’t translate into ROI. MLOps allows companies paving the way to AI with ROI.”

MLOps unifies data collection, pre-processing, model training, evaluation, deployment, and retraining to a single process that teams work to maintain. It is an ML engineering culture that includes the following practices:

MLOps

Continuous Integration (CI)

extends the testing and validating code and components by adding testing and validating data and models.

MLOps

Continuous Delivery (CD)

concerns with delivery of an ML training pipeline that automatically deploys another the ML model prediction service.

MLOps

Continuous Training (CT)

is unique to ML systems property, which automatically retrains ML models for re-deployment.

MLOps

Continuous Monitoring (CM)

concerns with monitoring production data and model performance metrics, which are bound to business metrics.

MLOps workflow

MLOps

Advantages of MLOps

MLOps

Accelerate time-to-value and time-to-deployment

MLOps

Effectively audit and monitor your models

MLOps

Democratizing model and artifacts

MLOps

Efficiently manage data error and model performance.

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Decrease dependency on IT for model deployment

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Increase model scalability during training and serving

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Enabling model management, governance, lineage tracking, deployment automation and monitoring

Fractal offering in MLOps

Fractal provides complete solutioning in the space of AI automation in all 3 major clouds. This includes 3 engagement models

Our experts

MLOps
Snehotosh Banerjee
Lead [email protected], Machine Vision and Conv. AI
MLOps
Suraj Amonkar
[email protected], Machine Vision and Conv. AI