ModelOps Monitoring Model KPI’s and its Benefits | A Quick Guide

Introduction to ModelOps Monitoring

Nowadays, organizations prefer Artificial Intelligence to solve serious business issues, predict future actions, and leverage data in complicated ways, even a few years ago. By using innovative tools and technologies, creating predictive algorithms has become the standard practice for data scientists. Still, companies struggle to deploy and maintain those algorithms effectively, often called the “last mile” of the AI journey.

ModelOps, which operationalizes AI models, has been gaining traction to successfully automate the distribution and maintenance of AI, pushing it through the finish line and ensuring that it continues to improve and rise in value. Consider Alexa: to update the hundreds of algorithms that must be designed to answer many new questions, you'd need an army of workers. So, what is the answer? That is where automating the AI lifecycle is the only way to handle the growing armies of algorithms.

ModelOps enables technology to converge multiple AI objects, solutions, and AI frameworks while maintaining scalability and governance. Click to explore about, What is ModelOps and its Operationalization?

What is Modelops?

• According to Gartner, ModelOps is primarily concerned with the governance and life cycle management of many AI models.

• It automates AI solution development, validation, scoring, deployment, governance, and upkeep.

• ModelOps enables businesses to shorten production cycles and deliver results to end users at scale while continuously improving outcomes.

• ModelOps guarantees that the data used to train AI models also considers the operational data used in production and the modelling and retraining necessary down the road by cooperating between data science teams and IT. Because IT personnel aren't typically educated to comprehend analytical models, deploying them without help might be problematic.

Monitoring each machine learning model requires attention from many different perspectives to ensure that each aspect runs accurately and efficiently.

Why do we need to monitor ModelOps?

According to SAS, models can degrade as soon as they are implemented. Of course, certain elements will have a more significant influence on your models' performance than others. The following are some of the most common problems that you will almost certainly encounter.

Data Quality

Subtle changes or tweaks in data that could go unnoticed or have a modest impact on traditional analytical approaches may have a bigger influence on machine learning model accuracy.

As part of your ModelOps operations, it's important to accurately analyze the data sources and variables accessible for use by your models so you can answer the following:

• What data sources will you employ?

• Would you alert a customer if a decision was made based on this information?

• Do the data inputs directly or indirectly breach any restrictions?

• What measures have you taken to combat model bias?

• How often do you add or update new data fields?

• Do you believe you can replicate your feature engineering in the production environment?

Time to Deployment

• Because the development/deployment cycle for models can be extensive, you should establish the length of your organization's cycle and then set benchmarks to measure progress.

• Break your process down into steps, then compare and evaluate projects to see what works and what doesn't.

• To help automate some processes, consider adopting model management software.

Degradation

• Be on the lookout for issues like bias and drift. The solution to these problems is to create a strong stewardship model in your company.

• If everyone, from model developers to business users, takes responsibility for the health of your models, these concerns may be addressed before they impact your bottom line.

ModelOps enables you to transfer models as rapidly as possible from the lab to validation, testing, and production while assuring quality outcomes. Click to explore about, ModelOps in Artificial Intelligence Projects

Different Perspectives for Monitoring Models and Their Importance

Monitoring from a Data Science Perspective

• Data scientists focus on identifying drift in models.

• Drift occurs when data becomes irrelevant or ineffective due to constant changes.

• Continuous monitoring ensures that model inputs remain similar to those used during training.

• Failure to monitor can lead to data tampering or poor model performance.

Monitoring from an Operational Perspective

• It's crucial to track resource consumption, including CPU, memory, disk, and network I/O.

• These metrics indicate the model's performance and health.

• Key operational performance indicators include:

Throughput: The amount of data successfully transmitted in a given timeframe.

• Monitoring these factors is essential for maintaining operational efficiency.

Monitoring from a Cost Perspective:

• Organizations should track the number of records processed per second by analytic models.

• While this provides insights into model efficiency, it’s important to assess the benefit versus the cost.

• Monitoring records, processing speed, and costs helps determine the model's financial viability.

• This analysis allows organizations to evaluate whether the value generated by the model justifies its cost.

Monitoring from a Service Perspective

• Service Level Agreements (SLAs) are essential for many business functions.

• For example, software companies may commit to a four-hour response time for critical issue patches.

• Developing, monitoring, and meeting agreed-upon SLAs is vital for organizational success

• Analytics SLAs might include:
  Maximum time for model creation.
  Maximum time for model deployment.
  Maximum time for iterations on production models.

ModelOps ensures that a suitable model will be deployed in production. An accurate model can be defined as one that satisfies the business requirements and generates output accordingly. Click to explore about, ModelOps for Scaling and Governing AI initiatives

What are the KPIs for monitoring ModelOps?

The KPIs for monitoring ModelOps are highlighted below.

Training

• Accuracy

• Number of rows and columns

• Time

• Variable Importance

Scoring

• Iteration data-Validation

• Prediction

• Data Health Summary

• Accuracy Summary

• Data error rate

• Cache hit rate

Monitoring

• How many models are in the production stage?
• Where are models running?
• How long have they been in business?
• Have models been validated and approved?
• Who approved them?
• What tests were run?
• Are results reliable/accurate?
• Are our compliance and regulatory requirements being satisfied?
• Are models performing within the threshold?
• What is the ROI for a model?

What are the benefits of enabling ModelOps?

• Get Started Quickly: Reduce the time it takes for AI to be implemented in production from months to minutes. To add AI power to any application, use APIs and SDKs to have your models up and running in minutes.

• Flexibility: Run your business how you choose, set your usage limitations, and pay as you go. Multi-instance, allowing you to set up many teams and users for your company.

• Create Efficiencies: By automating model management, you can enable model exchange and reuse while saving time for your team.

• Simple Integration: With ModelOps for teams, you can add AI power wherever needed. It connects to your existing data storage tools, continuous integration/continuous delivery pipelines, model training tools and frameworks, and business apps. The open design allows for future integrations and flexibility.

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Conclusion

KPIs are a series of indicators tied to a set of strategic goals. Your business must have a solid foundation for accurately recording and conveying data. The metrics can then be fine-tuned to produce actionable statistics regarding key activities or projects that are understandable to all stakeholders. KPIs enhance decision-making and provide executive-level information on a project's or act's success. Once your business has used KPIs, you may further validate outcomes and alter your path to accomplish your goals.

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