ModelOps: How to operate the model lifecycle

ModelOps is how analytic models are cycled from the data science team to the IT production team in a regular cadence of implementation and updates. In the process of realizing value from AI models, it is a winning ingredient that only a few companies use.

Organizations are increasingly relying on machine learning (ML) models to turn massive amounts of data into new insights and information. These ML models are not limited by the number of data dimensions they can effectively access and use large amounts of unstructured data to identify patterns for predictable purposes.

Introduction. . . ModelOps

But model development and implementation is difficult. Only approx. 50% of models are ever put into production and those that take at least three months to be ready for implementation. This time and effort corresponds to the real operating costs and also means a slower value for value.

All models deteriorate and if they do not receive regular attention the performance suffers. Models are like cars: To ensure quality performance, you need to perform regular maintenance. Model performance depends not only on model construction, but also on data, fine tuning, regular updates and retraining.

ModelOps allows you to move models from the lab for validation, testing and production as quickly as possible while ensuring quality results. It allows you to manage and scale models to meet demand and continuously monitor them to spot and correct early signs of deterioration. ModelOps is based on long-standing DevOps principles. It is a must to implement scalable predictable analysis. But let’s be clear: Model development practices are not the same as software engineering best practices. The difference should become clearer as you continue to read.

Measuring results from start to finish

For a ModelOps first step, monitor the performance of your ModelOps program. Why? Because ModelOps represents a cycle of development, testing, implementation and monitoring, but it can only be effective if it makes progress toward the goal of delivering the scale and accuracy required by your organization.

You need at the highest level to determine the effectiveness of your ModelOps program. Has implementing ModelOps practices helped you achieve the scale, accuracy and process stability that your organization needs?

Then, at the operational level, you need to monitor the performance of each model. When broken down, they need retraining and redistribution. Here are some considerations when creating a performance dashboard:

  • For models (or classes of models), you need to set accuracy targets and track them through the development, validation and implementation of dimensions such as operation and degradation.
  • Identify business metrics influenced by the model in operation. For example, A model designed to increase subscribers having a positive effect on subscription rates?
  • Track metrics such as data size and frequency of updates, locations, categories and types. Sometimes model performance issues are caused by changes in the data and sources, and these metrics can help in your study.
  • Monitor how much data processing resources or memory models consume.

In relation to metrics, model validation is an important foundation in ModelOps. Some use validation and verification of each other, but their intention is different.

CONTROLS confirms that a model is properly implemented and functioning as designed. validation ensures that the model produces the results it needs, based on the model’s basic goals. Both are important best practices in developing and implementing quality models.

Three common issues addressed using a ModelOps approach

Models can deteriorate as soon as they are implemented (sometimes every day). Of course, there are things that will affect the performance of your models more than others. Below are some common issues – issues that you will almost certainly encounter.

data Quality

Subtle changes or shifts in data that may go unnoticed or may have a minor effect on some traditional analytical processes may have a more significant effect on the accuracy of the machine learning model.

As part of your ModelOps efforts, it is important to properly evaluate data sources and variables available for use with your models so that you can answer:

  • What data sources do you want to use?
  • Would you be comfortable telling a customer that a decision was made based on this data?
  • Does data entry violate direct or indirect rules?
  • How did you address model bias?
  • How often are new data fields added or changed?
  • Can you repeat your functional technique in production?

Time for implementation

Since the model development / implementation cycle can be long, you first assess how long this cycle is for your organization, and then set benchmarks to measure improvement. Divide your process into discrete steps, then measure and compare projects to identify best and worst practices. Also consider model management software that can help automate some activities.


Be on the lookout for things like drift and bias. The answer to these problems is to create a strong approach to model management in your organization. If everyone from model developers to business users takes ownership of the health of your models, these issues can be solved before they affect your bottom line.

When to update your models

The most difficult thing about machine learning is to implement models and maintain precision. It always means searching for newer, better data to feed them to improve accuracy.

Is there a standard plan that you can set for retraining models that fall below your accuracy thresholds? The simple answer is “no.” The reason? One reason is that models break down different speeds. Another is that the need for precision is relative to what you are trying to achieve. For example, where the risk of an inaccurate prediction is costly or dangerous, model updates may be continuous.

Therefore, it is important that you understand your models’ accuracy levels by monitoring results and your own accuracy measurements.

The danger of ignoring ModelOps

The predictability of these models in combination with the availability of big data and increasing computing power will continue to be a source of competitive advantage for smart organizations. Those who fail to embrace ModelOps face increasing challenges in scaling theirs analytics and come during the competition.

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