ModelOps: mastering AI model lifecycle

In recent years, a significant rise in artificial intelligence initiatives has been witnessed. The machine-learning and analytic models are deployed across various industries and power multiple workflows. However, these models must be properly developed and continuously monitored for maximum efficiency and governance, including risk mitigations and other aspects.

ModelOps is a set of tools and practices that cover the entire AI application lifecycle management. ModelOps governs the data streams, model training, model deployment, and model performance in real-life scenarios.

What is ModelOps and how is it different from MLOps?What is ModelOps and how is it different from MLOps?

ModelOps begins with the model development stage, which includes data collection management and model creation. Then, it walks through the entire model lifecycle, including its deployment, monitoring, and continuous retraining.

Many concepts here are borrowed from machine-learning operations (MLOps), but ModelOps takes things further. It is purpose-built to operationalize all forms of analytics and AI solutions within large companies. In addition to model training and performance monitoring, it also taps into the model’s decision-making abilities. ModelOps explores model explainability and presents the results in a business-centric way for business leaders to understand.

What is ModelOps?What is ModelOps?

ML models are not a one-off solution like most enterprise-grade software applications. They need constant attention throughout their development and production phases. ModelOps encourages the implementation and use of tools and facilitates AI development at every step. These improve data handling and quality, process efficiency, and provide insights regarding model performance.

An AI development lifecycle consists of the following steps:

1. Data gathering and processing: The first step to creating AI solutions is gathering, cleaning, and managing relevant datasets. This is a great challenge as real-world data is generated across various touchpoints and contains all sorts of anomalies.
2. Model training: The next step is to train machine learning models appropriate for the task at hand. The training phase includes featurization, feature selection, model selection, and training.
3. Deployment: Once the model is trained, it will be pushed to deployment and tested against real-world and often real-time data.
4. Monitoring: During model deployment, the model must be constantly monitored for performance in terms of accuracy, inference speed, and the quality of output. Moreover, the data sources must also be monitored for drift.

The lifecycle mentioned above is a challenging but necessary task for successfully and efficiently utilizing AI solutions. Enterprise-scale solutions face greater model management challenges involving various statistical, analytical, and decision models. Large enterprises deal with exponentially more data sources and are focused on gaining business value.

ModelOps practices allow seamless management of all AI solutions across the organization. They ensure robust data and training pipelines and provide a business-level understanding of the model’s capabilities. These insights are crucial for business leaders to make business decisions.

“ModelOps is a capability that focuses on getting models into 24/7 production. It’s a capability that must be owned by the CIO’s organization or the technology center of a large organization.”

— Stu Bailey, Co-Founder and Chief AI Architect of ModelOp, in an interview with Forbes

Features of ModelOpsFeatures of ModelOps

A ModelOps concept includes various modules that handle the different components of an AI application. Each module is designed to streamline and automate particular processes. The ModelOps framework contains the following features:

• Data quality assurance. This includes logical checks, such as checking for negative values where none should exist, such as Age.
• Seamless model pipeline generation: It streamlines model pipelines including featurization and model training. The pipeline generation process is usually carried out in a low-code model development environments with minimal human interaction.
• Performance monitoring and analytics: ModelOps services include integrated visualization tools that provide details regarding training times, inference times, and performance metrics tracking.
• Fairness analysis: In addition to performance monitoring, ModelOps also monitors the model for biases against race or culture, etc.
• And more.

These features make ModelOps a one-stop solution for developing and managing AI models.

Why ModelOps? Benefits of ModelOpsWhy ModelOps? Benefits of ModelOps

Implementing new frameworks requires time and resources, so it is important to understand their benefits. ModelOps is a must-have for enterprises hoping to ride the AI wave and extract maximum return on investment (ROI). ModelOps frameworks solve various AI-related challenges and offer features such as:

• Streamlined retraining, model management, and model deployment: Retraining is challenging as it requires additional data collection and processing, featurization, model training, and deployment. ModelOps provides an intuitive paradigm to build reusable re-training pipelines. The solution is a one-click application that auto-trains and deploys to production while maintaining data and mode quality.
• Better governance: It provides insights regarding model performance and working of the various models deployed in the organization. This also allows business leaders to govern the AI, ensuring it performs accurately and ethically.
• Easy scalability: ModelOps is purpose-built for companies that envision a scalable application. The paradigm includes modules that allow easy addition of new ML models, scalable hardware resources, and accommodating growing data. It handles such unforeseen challenges and provides longevity to the projects.
• Better collaboration: ModelOps techniques implemented organization-wide, and they bring down silos between teams. They allow teams like data engineers, AI engineers, and DevOps to communicate and collaborate on various projects. This improves efficiency and yields better and quicker results.
• Reduced costs: Overall, ModelOps improves efficiency and model development quality, reduces deployment time, and improves time to market. All these factors benefit organizations by reducing time and resource costs and improving return on investment (ROI).

ModelOps vs MLOpsModelOps vs MLOps

The terms ModelOps and MLOps are often used interchangeably, but they have some differences. The MLOps framework is inspired by DevOps and is built to easily create and deploy a machine learning model. ModelOps follows the same path but expands on it by including the governance of a suite of ML models.

ModelOps use casesModelOps use cases

Various companies utilize AI to streamline internal processes. The success of AI in improving efficiency and enabling automation has made it popular across different domains. However, each of these implementations requires a ModelOps framework to set development and deployment standards and allow scalability. Some popular use cases benefitting from ModelOps include:

• Finance: The financial sector uses probability and machine learning models for various tasks such as credit scoring, customer segmentation, fraud detection, etc. All these use cases need to be monitored for performance; model governance is also neccessary. A ModelOps paradigm provides a broader picture of each functionality and presents its findings on a unified dashboard.
• Retail: The retail market, especially e-commerce platforms, deploys AI models for customer segmentation, recommendation engines, and customer-facing chatbots. Each use case impacts business growth and must be monitored for accuracy, response time, and biases. Modelops standardizes the AI solutions and allows scalability for growing retail chains.
• Healthcare: The medical institutes deploy AI models for disease prediction, locating abnormalities in medical images, and building knowledge graphs for drug discovery. All these applications are highly sensitive and linked to the health and well-being of the patients. Medical AI applications also require additional attention to data protection and anonymization practices. ModelOps takes care of all these actions and facilitates the responsible use of AI models.

How ModelOps solves machine learning models lifecycle problemsHow ModelOps solves machine learning models lifecycle problems

The AI model lifecycle comprises data processing, featurization, model training, creating checkpoints (model versions), deployment, and monitoring. The cycle repeats when the model requires retraining upon receiving new data or if it starts to display drift or degradation.

Conventionally, all these modules have to be programmed and executed manually. However, ModelOps provides built-in capabilities to execute each process step with a single click. It allows developers to establish an end-to-end pipeline that can be executed with a single click or scheduled to run at regular intervals. This saves several manhours and resources and improves business impact.

Three common problems addressed using a ModelOps approachThree common problems addressed using a ModelOps approach

ModelOps tackles various challenges in the AI domain. Let’s discuss three common AI-related issues that ModelOps solves.

• Data quality issues: Data is everchanging, and new anomalies are encountered after each iteration. ModelOps allows engineers and data scientists to set data cleaning standards that automatically filter out corrupted information. It also establishes data drift monitoring, which notifies users when certain critical values deviate from an established baseline.
• Long deployment times: AI models carry certain deployment challenges, such as setting up performance metrics, integrating with real-world data, and integrating with existing technologies. ModelOps sets up a deployment pipeline that can be reused after every update or retraining.
• Model degradation: ML models degrade over time due to changing data patterns, resulting in model drift. ModelOps monitors the model performance under various conditions. It includes modules for identifying model drift and notifying users when an improvement or retraining is required.

The danger of ignoring ModelOpsThe danger of ignoring ModelOps

The world is jumping on the AI bandwagon, and companies are racing to develop applications powered by modern deep-learning algorithms. While AI development has become easier in recent times, deploying AI applications and generating revenue still remains a challenge for many. This is because they demand constant monitoring, maintenance, and scalabilty.

ModelOps allows to maintain application’s quality, minimize downtimes, and maintain result accuracy. Ignoring these benefits directly impacts business growth, as an unstable infrastructure is bound to fail. This results in a loss of customer trust, and businesses lose their competitive edge in the AI market. It also puts companies at the risk of scalability challenges as their conventional infrastructure does not accommodate growing data needs and user base.

ConclusionConclusion

The ModelOps framework inherits features from MLOps, such as data and model drift monitoring, creating an end-to-end pipeline, and streamlining deployment and retraining procedures. However, it offers additional benefits for enterprises, such as model monitoring and governance of all models and not just machine learning.

ModelOps is a game-changer for companies working to develop AI applications at scale. It allows engineers and data scientists to build scalable models integrated into an end-to-end pipeline. The pipeline is designed to automate cumbersome processes like data processing, featurization, model training, and deployment and can be triggered with a single click. ModelOps techniques also improves team collaboration and contains built-in analytical capabilities to provide model insights to business leaders.

Given the growing rate of AI applications, ModelOps has become a necessary inclusion. Organizations failing to adopt ModelOps are at risk of building mediocre and unstable applications. ModelOps has become a necessary investment to ensure the robust, long-running, and profitable use of AI.