What is ModelOps?
Model Operations are at the centre of every organization's enterprise AI approach. They enable technology to converge multiple AI objects, solutions, and frameworks while maintaining scalability and governance. A diverse set of Operationalized AI (Artificial Intelligence) and decision models, including machine learning, knowledge graphs, rules, optimization, and linguistic and agent-based models, focus mostly on governance and life cycle management (or AI platform operationalization).
ModelOps allows you to move models from the lab to validation, testing and production as quickly as possible while ensuring quality results
Types of ModelOps Processes
To harness the benefits of ModelOps and foster transformative leadership, enterprises should focus on the following:
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Intelligent Use of AI and DevOps Automation:
Implementing automation in AI lifecycle management streamlines the management of applications and models. This involves reviewing or redesigning end-to-end processes to promote continuous learning and effectively handle training and output data. AI can enhance core DevOps activities, including infrastructure maintenance, automated testing, process improvement, and quality assurance. -
Synchronization of DevOps and ModelOps:
Aligning application development with model lifecycles ensures efficient model deployment in applications and facilitates feedback for model retraining and application upgrades.
ModelOps incorporates a variety of technologies, each playing an important role.Key Technologies in Model Ops
1. Artificial Intelligence and Machine Learning: At the heart of ModelOps are AI and ML algorithms. These technologies are used to create predictive models and intelligent applications capable of learning from data, making decisions, and improving over time.
2. Data Processing and Analytics: Effective ModelOps requires robust data processing capabilities to handle large volumes of data. Data analytics technologies are also vital for extracting insights and guiding the development of AI models.
3. Cloud Computing: Cloud platforms provide the scalable infrastructure needed for ModelOps. They offer the flexibility to handle the computational demands of training and deploying AI models, as well as the ability to scale these operations up or down as needed.
4. Containerization and Orchestration: Technologies like Docker and Kubernetes play a big role in ModelOps. They help pack models and their dependencies into containers, making deployment consistent and scalable across different environments.
5. Version Control and Model Management: Tools for version control and model management are necessary for tracking changes, managing different model versions, and ensuring reproducibility.
As enterprises scale up their AI initiatives to become a true Enterprise AI organizations, having full operationalized analytics capability puts ModelOps in the centre. Click to explore our, connecting both DataOps and DevOps.- Forbes
How to operationalize ModelOps?
Enterprises need to monitor the performance of the ModelOps software for the first phase of a ModelOps process. Since ModelOps is a growth, testing, rollout, and monitoring cycle, it will only be successful if it progresses against delivering the organization's size and accuracy. Then, we need to track each model's success at the organizational stage.
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For models (or model classes), set accuracy goals and monitor them for dimensions such as drift and degradation through development, validation, and deployment.
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Identify market indicators that are influenced in service by the model. Is a model designed to raise users, for instance, having a beneficial impact on subscription rates?
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Track metrics such as the size of data and update frequency, positions, groups, and forms. Model performance concerns are often attributable to changes in the data and its sources, and these metrics can assist in your investigation.
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Track the volume used by processing power or memory models.
Why is ModelOps Important?
Organizations also struggle with analytics operationalization because they lack a formal system to organize resources through analytics, IT, and the organization. Since data alone doesn't drive the business, decisions do. They are making decisions that impact organizations each day. Since analytically driven decisions are smarter choices, it helps them make the right choices every time, even while making thousands or millions of them every day, by integrating analytics into the decision-making processes. This includes the operationalization of scale analytics.
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The number of models - To account for business process variations, personalization, and specific customer groups, each organization must handle hundreds of models.
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Technology Complexity - The fast and continuing innovation in data & analytics contributes to unmanageable difficulty for even the most expert IT teams.
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Regulatory Compliance - As the use of AI spreads across markets, adhering to strict and ever-increasing models, regulatory criteria become more challenging.
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Organization Silos - Scaling AI in trends that are in businesses needs industry, technology, and data to come together. To ensure its free flow through the enterprise, the organizational data needs to be opened. This cannot occur in a siloed work culture, and organizations must develop an interdisciplinary team to push AI in the organization.
ModelOps (or AI model operationalization) is focused primarily on the governance and life cycle management of a wide range of operationalized artificial intelligence. Click to explore our, AI model operationalization - Gartner
How do industry leaders drive AI at scale?
Enterprises drive AI into core processes at scale by focusing on three areas:
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Model-centric approach: Leaders manage models as first-class business properties, enabled by a model-centric infrastructure that facilitates long-term continuous implementation performance independent of the data science workbench and execution platforms.
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Operations First Mentality - Leaders recognize that deploying models to operation systems allows them to run 24×7 without delay, using the same operating controls, software, and automation that enable other technologies.
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Automation - Leaders completely automate ModelOps processes, from implementation to monitoring and governance, to efficiently handle business model SLAs, eliminate manual execution, and mitigate risk, cost, and model time for business.
ModelOps vs MlOps
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Aspect |
ModelOps |
MLOps |
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Focus |
Operationalization of both AI and decision models. |
Exclusively focuses on the operationalization of ML models. |
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Scope |
Covers the entire lifecycle of AI models, including optimization models and decision models. |
Focuses on machine learning models only. |
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Integration |
Involves AI and decision models across various departments and processes. |
Primarily integrates machine learning models. |
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Deployment |
Manages implementation and model workflow during application creation and deployment lifecycle. |
Reduces time to deploy ML models from months to hours. |
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Tools and Performance Monitoring |
Provides transparency in deploying and using AI across the enterprise; monitors model performance in real-time. |
Offers tools to monitor ML model performance but lacks comprehensive enterprise-level transparency. |
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Explainability |
Ensures AI-enabled outcomes are explainable and transparent across the enterprise. |
Limited explainability focused primarily on ML models. |
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Organizational Impact |
Bridges the gap between teams building and deploying AI, facilitating widespread use of AI in business operations. |
Addresses ML model deployment but leaves a gap in end-to-end enterprise integration. |
Click to explore MLOps Challenges and Solutions while building and deploying Model
ModelOps Solutions for Enterprises Challenges
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Acceleration of AI/ML Activities: Large enterprises have been increasing their AI and machine learning efforts in recent years using ModelOps, which enables the scaling of machine learning models across the enterprise for numerous use cases.
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Model Debt Accumulation: Many companies face an increasing amount of "model debt" due to the lack of deployment and refresh of models. This has resulted in missed opportunities, leaving millions of dollars unutilized.
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Emergence of AutoML and Citizen Data Scientists: With the rise of AutoML and evolving market trends, citizen data scientists deploy applications without proper oversight from enterprise IT, creating governance challenges.
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Governance and Accountability Issues: The lack of accountability and proper controls in these systems can lead to governance risks and increased costs. Large-scale models require close governance during deployment to ensure regulatory compliance.
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ModelOps as a Solution: ModelOps addresses these challenges by acting as a partner for the data science team, enabling better use of technologies and tapping into data repositories across different business units.
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Operationalizing Models: ModelOps focuses on operationalizing models generated from historical data and deploying them on production data, ensuring smooth analytics integration throughout the entire ModelOps lifecycle.
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End-to-End ModelOps Lifecycle: ModelOps supports the entire lifecycle of model development and deployment, ensuring all business contexts are covered, improving business outcomes, and reducing model debt.
Use case of ModelOps
In the insurance industry, algorithms are supervised to ensure that no redlining occurs. This can be complicated and add more uncertainty to decision-making processes. To ensure compliance, audit trails, scores, and monitoring reports must be produced. ITOps can use the CommandCenter during the whole model's life cycle to respond quickly to troubleshooting as required and rely on various resources to solve problems.
After implementing the ModelOps Life Cycle with ModelOps, some large enterprise customers are finding new possibilities. A major multinational insurance corporation has recently arrived at ModelOp after successfully developing data science and computer learning technologies to upgrade their legacy models to machine learning models. To deploy their capabilities of data science and machine learning, they used ModelOps. They found that with this process, they could deploy many more models after using ModelOps. They improved the overall insurance policy decisions by creating a full end-to-end policy adjustment process. Before that, they had no idea that it was feasible to establish a full end-to-end policy adjustment process. They got an opportunity for an Enterprise AI solution to boost their current business workflow until they used ModelOps.
How can ModelOps be beneficial for given use cases in the Insurance Industry?
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Insurance Advice: From handling the customer's first interaction to completing service, which includes the customer requirements made possible by machine learning algorithms that will review their profiles and recommend personalized products.
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Claim Processing: To automate and improve operational efficiency from the registration to the settlement of claims and enhance the customer experience.
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Fraud Prevention: To identify fraudulent claims faster and more accurately. This will improve the productivity of the claim settlement process and improve fraud prevention.
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Risk management: Early identification of risks in the process of determining policy losses and premiums helps insurers better use underwriters' time and provides them with a significant competitive advantage.
Final Thoughts
The ModelOps Life Cycle is relevant as more organizations expand their Enterprise AI initiatives. Probably in the next few years, as more businesses use ModelOp to ramp up their Enterprise AI systems, companies will gradually see a major reduction in "model debt" across organizations.
What we do - We have the expertise and solutions to help you leverage the power of AI/ML on a scale that will improve your core processes. With ModelOps, we help our clients scale and control their enterprise AI initiatives.
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Explore more about Machine Learning Observability and Monitoring
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Learn more about the MLOps Roadmap for the Model's Interpretability
