ModelOps and its Operationalization for Secure and Reliable AI

Organizations need to operationalize, integrate and deploy AI models in line with business value expectations. Many organizations face issues such as decentralization and fragmentation of data science teams which results in delayed development of models due to lack of collaboration between business units. Thus, organizations fail to scale AI by accumulating models that are not implemented and does not match the expectations on the value that should be coming from AI.

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The huge gap between model deployment and model governance can be addressed with ModelOps. Each and every organization’s enterprise AI approach should have a ModelOps strategy at its core. It enables technology to converge multiple AI frameworks, solutions, and AI objects while it also ensures its scalability as well as governance. It includes models based on machine learning, rules, knowledge graphs, natural language techniques, etc. Unlike AIOps and MLOps, ModelOps focuses on operationalization of AI and decision models.

In order to successfully operationalize any type of model, organizations need to take following 4 steps, initially define the end-to-end model life cycle, integrate the model into existing production environment, monitor the models in production and govern model operations. Some of the technologies involved in ModelOps such as multi-cloud data and AI architectures help to move beyond machine learning models. Organizations with an expertise to understand cross-functional nature of AI, capable of carrying out operationalization and ensuring collaboration between data engineer, AI engineer, data scientist, ML validators will have a secure and reliable AI.

Innominds Digital Transformation Services are aligned to meet your stage-specific needs across the entire product life cycle. Leverage Innominds’ power of AI/ML on such a scale that will improve your core processes, we have the expertise and solution. Innominds’ iFusion is the leading autonomous AI platform that accelerates enterprise AI adoption by enabling full lifecycle AI management from data exploration to governance addressing key challenges of maintainability, sustainability and governance.

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