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Overview of ModelOps in AI

Organizations increasingly depend on machine learning (ML) models to transform enormous amounts of data into new insights and information. These ML models are not constrained by the number of data dimensions they can successfully access, and they can detect patterns in large volumes of unstructured data for predictive purposes. Model development and deployment, on the other hand, are challenging. Only approximately half of all models are ever put into production, and those take at least three months to complete. This time and effort equate to a significant operating expense, as well as a delayed time to value.

A practice that ensures a model is made into production, considering the required business KPIs and error metrics and ensure the deployed model delivers business value. Click to explore about, ModelOps for Scaling and Governing AI initiatives

All models deteriorate, and if they are not maintained regularly, their performance degrades. Models are similar to automobiles in that frequent maintenance is required to guarantee optimal performance. Model performance is determined not just by the model itself but also by data, fine-tuning, regular updates, and retraining.

What is ModelOps?

ModelOps is a competency that focuses on putting models into continuous production 24 hours a day, seven days a week. ModelOps enables you to transfer models as rapidly as possible from the lab to validation, testing, and production while assuring quality outcomes. It allows you to manage and scale models to match demand while continually monitoring them for early symptoms of deterioration.

A genuine ModelOps framework enables uniformity and scalability across these different settings, allowing development, training, and deployment procedures to operate consistently and platform-independently.

Why we need ModelOps?

ModelOps is an extension of MLOps that includes significant capabilities such as continuous retraining, automatic updating, and synchronized creation of more sophisticated machine learning models in addition to routine deployment of machine learning models. Gartner states that having fully operationalized analytics capabilities positions ModeOps right between DataOps and DevOps.

ModelOps enables analytical models to be transferred from the data science team to the IT production team for regular deployment and updates, including validation, testing, and production, as rapidly as feasible while assuring quality outcomes. Furthermore, it enables the management and scaling of models to fit demand and continuous monitoring of them to detect and correct early symptoms of deterioration.

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?

Why ModelOps is beneficial?

Although it is not extensively used, ModelOps can assist organizations facing increasing problems in scaling their analytics in moving models from the data science lab to IT production. ModelOps can help organizations leverage analytics' predictive powers and deliver substantial time and money savings by providing regular updates and deployments. These models are maintained, scaled, monitored, and retrained to put them into production.

How ModelOps improve the efficiency of AI projects?

Below highlighted are the ways by which ModelOps empower the efficiency of Artificial Intelligence:

Addressing the gap between model deployment and model governance with ModelOps

Models have always been seen as critical corporate assets, and AI models demonstrate their capacity to provide considerable value. Enterprises rapidly realize that capturing this value continually while controlling risk necessitates ModelOps strategies for the AI era. As a result, they're investing ModelOps.

ModelOps is quickly becoming a fundamental business competency, with companies investing in more efficient procedures and systems for deploying AI models.

Problems that ModelOps can Solve

  • One of the reasons a ModelOps method is required is owing to a machine learning feature known as "model degradation." All models deteriorate, and if they are not maintained regularly, their performance degrades. This occurs when a data science team assesses model performance early in a project, observes good accuracy, and chooses to proceed. Unfortunately, machine learning models frequently interact with real-world data, and their accuracy might deteriorate with time. ModelOps is helpful for automatically detecting model deterioration, updating a model, and deploying it to production.
  • ModelOps allows you to manage and scale models to match demand while continually monitoring them for early symptoms of deterioration. A business is unable to scale and control AI efforts without ModelOps capabilities. The solution to model decay (or drift) is to have a robust model stewardship strategy in your company.

Benefits at each Level

  • Deploy - During development and deployment
  1. Data scientists may be creative when building models to meet corporate demands.
  2. Packaging approaches may require less involvement from DevOps teams/software engineers.
  3. IT does not need to build a separate environment for each model while maintaining control over data pipeline setup and infrastructure optimization.
  4. Model review, testing, and approvals are automated, and all participants may see the procedures.
  5. Business unit managers see models applied more quickly.
  • Monitor - The execution is efficient and consistent, and it is followed by ongoing monitoring:
  1. Model correctness, performance, data quality, and the demands put on business infrastructure are evaluated regularly so that changes may be implemented as soon as possible.
  2. Retraining and redeployment help to promote continuous model improvement.
  • Govern
  1. With proper leadership, the organization can be certain that not only are the right versions of models deployed but that older versions can be reproduced if needed for audit or compliance purposes.
A process to check the system accepts the requirements of a user or not. It's performed at a time when the system used by actual users. Click to explore about, User Acceptance Testing

Checklist for the ModelOps

  • What problem are you trying to solve?
  • Do you have the data?
  • Do you have a baseline?
  • Is the model ready for deployment?
  • Do you know everything needed to deploy it?
  • How would you monitor it?
  • Can it be used?

What problem are you trying to solve?

At this time, we're concentrating on business goals. Remember that our goal should be to solve an issue rather than play the metric at any cost. What exactly are you attempting to do in simple English?

  • Should you go ahead and do it?
  • Some difficulties are ill-posed; they may appear logical at first, but the underlying issue is something else entirely (e.g., X-Y problem). Another situation when you shouldn't do it is if the technology might have negative consequences (for example, a résumé screening algorithm based on historical data could reinforce societal prejudices in the hiring process).
  • What exactly does it mean to be finished? Are acceptance tests possible to define?
  • Are there any obvious performance indicators that you can use to assess your progress?
  • Is machine learning required for this? Is it worthwhile to invest in machine learning?
  • Do you have the time, expertise, people, and computing power to solve it?

Do you have the data?

  • Do you have access to all of the information you'll need to solve the problem? If not, do you have a plan to collect it?
  • Would this information be available in a production setting?
  • Is it possible to utilize this information (terms of service, privacy, etc.)? Is there any sensitive information there that can't be utilized or has to be anonymized?
  • Is the information labeled? Do you have a good system for labeling it?
  • "Instead of spending a month trying to solve an unsupervised machine learning issue, label some data for a week and train a classifier." Although data labeling is not the most appealing aspect of the work, it is time spent.
  • Is the information current and correct? Have you double-checked the labels for accuracy?
  • Is this data indicative of the target population? What is the size of your population?
  • While having more data is beneficial, it is equally essential to have high-quality data. Having a lot of poor data does not get us any closer to the answer, as Xiao-Li Meng noted in his Statistical paradises and paradoxes in Big Data presentation.
  • Is it possible that utilizing this data may result in skewed results? Is there enough representation for minorities?

Do you have a baseline?

What is your starting point? What methods were used to tackle the problem before (not necessarily utilizing machine learning)?

Do you have the metrics you'll need to compare your solution to the benchmark?

Is there any evidence that machine learning has been utilized to tackle comparable challenges in the past (literature)? What did we take away from it?

Is the model ready for deployment?

Data science magic happens at this point. Data scientists do exploratory data analysis, clean and preprocess data, feature engineering, and train, tweak, and verify models.

Are the data preprocessing steps documented?

  • Are you the one who carried out the exploratory data analysis? What are the possible flaws in this data?
  • Is it possible to document the assumptions made regarding the data? Is it possible to turn these into automatic data checks?
  • Is there documentation for the data cleaning, preprocessing, and feature engineering steps? Is it possible to reproduce these in a production environment?
  • How would you deal with missing data in a production environment?

Does it work?

  • Is the code working (for example, the Jupyter notebook isn't crashing)?
  • Is the model proven to tackle the problem you're seeking to solve?
  • What measures should be used to evaluate the model's performance? Is the performance satisfactory?
  • Have you looked for overfitting?
  • Could there have been any data breaches that inflated the results?
  • Is it possible to recreate the findings (as a code)? Is it possible to duplicate them?
Cloud Governance applies specific policies or principles to the use of cloud computing services. Click to explore about, Cloud Governance Challenges

Did you explore the predictions?

  • Are the forecasts accurate? Do they have any resemblance to genuine data?
  • Do you have any explanations for the predictions (partial dependency plots, subpopulation analysis, Shapley values, LIME, what-if analysis, residual analysis)?
    Have you checked for prejudices (gender, race, etc.)?
  • Have you gone over some of the misclassified cases by hand? When is the model going to make a mistake?

Does the code meet the quality standards?

  • Is the code sufficiently described so that others may use it?
  • Is it compatible with the technological restrictions (technology, memory use, training time, prediction time, and so on)?
  • Is dependencies documentation available (Docker image, virtual environment, a list of all packages and their versions)?

Do we have the tests for the model?

  • Are unit tests included with the model code? Is the test coverage sufficient?
  • Do you have functional tests demonstrating that the model performs as expected for real data?
  • Do you have any testing to see how it acts in extreme scenarios (zeroes, extremely low or extremely high numbers, missing data, noise, and adversarial instances, for example)?

Do you know everything needed to deploy it?

  • Do you have the resources to deliver it (for example, infrastructure and DevOps engineers)?
  • Is it going to be a microservice, a package, or a stand-alone app?
  • Is it going to operate in real-time or batch mode?
  • What are processing resources (e.g., GPUs, RAM) required?
  • What's the relationship between it and other services or software components? What could go wrong?
  • Are you aware of all of the package's dependencies (versions)?
  • Do you need to take any further actions if the model generates unusual predictions (e.g., truncate them or fall back to the rule-based approach if predictions exceed a certain threshold)?
  • What metadata and artifacts (for example, model parameters)? What are your plans for storing them?
  • How would you deal with model and data versioning?
  • What kind of code tests are you going to run? How often do you do it?
  • What methods would you use to distribute a new model version (manual inspection, canary deployment, A/B testing)?
  • How often should the model be retrained? What are the upper ("at least") and lower ("not sooner than") bounds?
  • How would you unroll the rollout if something went wrong?

How would you monitor it?

  • How would you collect "hard" data like runtime, memory use, computation, and disc space?
  • What data quality and model metrics do you need to keep an eye on in the production?
  • What should KPIs be tracked?
  • What metrics would be required to decide whether to switch between models when installing a new model?
  • What methods would we use to keep track of input drift and model degradation?

a. Ground truth evaluation, in which the predictions are compared to the labeled data to detect any drift in performance (model metrics, business metrics).

b. Input Drift Detection refers to tracking the data's distribution over time. This may be accomplished by using the following methods:

  • To discover abnormalities in the input data, use summary statistics (e.g., mean, standard deviation, minimum, maximum) or formal tests (K-S tests, chi-squared tests).
  • Compare the distributions of the model's predictions on old and new data (K-S test).
  • Using a domain classifier, a classification algorithm that attempts to predict old vs. new data and, if successful, indicates that the data has changed.

Is there anything that needs special attention in terms of potential feedback loops?

  • Users are more likely to watch the videos you provide as suggestions if you propose them based on their watching history. As a result, the recommendation algorithm will affect your future data, and retraining the system on such data will enhance the suggestions you've previously made.
  • Would you have easy access to the metrics (MLflow, Neptune, etc.)?
  • Who will be in charge of keeping track of the metrics?

Can it be used?

  • Before starting the project, at least some of those considerations should and would be made, but before deploying, you should ask the questions again.
  • Was it put through its paces in a real-world setting to ensure that it performs as well as it did during development?
  • Was the data subjected to a third-party assessment (e.g., domain experts)?
  • Does the expense of designing, implementing, and maintaining the model exceed the advantages of utilizing it?
  • Was it examined for fairness (e.g., race and gender)?
  • Have you considered the model's possible misuses or negative side effects maybe?
    Is it legal to use it (for example, the GDPR)?
  • Are you keeping all the relevant artifacts (version-controlled code, parameters, data used for training) in case the predictions need to be audited (legal obligations)?
  • Is there a backup plan in case it fails, or are there other issues with the algorithm?
Java vs Kotlin
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Conclusion

The strategic power of AI has been fully demonstrated across numerous sectors and enterprises. This has increased model production. However, investments in the people, procedures, and technologies for model operationalization, i.e., ModelOps — have lagged. To handle day-to-day ModelOps tasks, organizations must develop specialized model operator or model engineer positions.

There is a developing understanding of the function, the issues it solves, the possibilities it generates, and the investments required to sustain it. ModelOps, like DevOps, ITOps, and SecOps before it, is poised to become a key business role in its own right as global AI use advances.

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