Why You Need MLOps

MLOps allows organizations to automate and scale the deployment and management of trusted machine learning in production.

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A Clear Case for 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 to easily deploy, monitor, and update models in production, paving the way to AI with ROI.

Four Reasons Why You Need MLOps

1. Issues with Deployment

Businesses don’t realize the full benefits of AI because models are not deployed. Or if they are deployed, it’s not at the speed or scale to meet the needs of the business.

MLOps Deployment Helps You With:

Multiple languages and multiple teams are used to build models.
Models are sent to IT but are not making it into production.
Models must be rewritten in different languages for deployment.
There is a large backlog of models waiting to be deployed.
Data scientists spend a lot of time troubleshooting models during the deployment process.
A standardized process for elevating models from development to production is missing or flawed.
There is a complex process for putting models into production that requires updating multiple systems.

2. Issues with Monitoring

Evaluating machine learning model health manually is very time-consuming and distracts resources from model development.

MLOps Monitoring Helps You With:

Models are in production, but no monitoring has ever been performed.
Models are deployed across the organization and in various systems without a consistent way to monitor them.
Models have been in production for a long time and never refreshed.
Model performance must be determined with a manual process performed by a data scientist.
There is no centralized way to view model performance across the entire organization or to offload accountability to Ops teams.

3. Issues with Lifecycle Management

Even if they can identify model decay, organizations cannot regularly update models in production because the process is resource intensive. There are also concerns that manual code is brittle, and the potential for outages is high.

MLOps Lifecycle Management Helps You With:

Models are not being updated in production.
Data scientists do not hear about model decay after initial deployment.
Data scientists are heavily involved in production model updates.
Only a small percentage of new project demand is met due to high maintenance demands of existing models.

4. Issues with Model Governance

Businesses need time-consuming and costly audit processes in order to ensure compliance as a result of varied deployment processes, modeling languages, and the lack of a centralized view of AI in production across an organization.