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:
Continuous Integration (CI)
extends the testing and validating code and components by adding testing and validating data and models.
Continuous Delivery (CD)
concerns with delivery of an ML training pipeline that automatically deploys another the ML model prediction service.
Continuous Training (CT)
is unique to ML systems property, which automatically retrains ML models for re-deployment.
Continuous Monitoring (CM)
concerns with monitoring production data and model performance metrics, which are bound to business metrics.
Advantages of MLOps
Accelerate time-to-value and time-to-deployment
Effectively audit and monitor your models
Democratizing model and artifacts
Efficiently manage data error and model performance.
Decrease dependency on IT for model deployment
Increase model scalability during training and serving
Enabling model management, governance, lineage tracking, deployment automation and monitoring
Data drift: Identifying. Preventing. Automating
Disruption has given a new dimension to the data drift challenge. Businesses grappled with changing consumer behaviors, leading to changing data patterns. How can we architect for change, manage data drift and even harness its power to accelerate digital transformation for your business?
3 pillars to move from AI pilots to AI at scale
Find how to take AI pilot projects to scale by implementing these three pillars. Making AI projects successful as a complete project needs a solution that is planned, scalable and has the right technology combinations.