ModelOps, which stand for model operations, is a collection of automated procedures and tools for deploying, managing, monitoring and improving production models.
Everything is instrumented around the model, from deployment to governance to inference and monitoring to scaling, under this approach, which is model-centric.
Artificial intelligence and machine learning are becoming increasingly popular, and new data science projects created to develop predictive and analytical models for various reasons.
While organizations want to scale up advanced Artificial Intelligence solutions in a fair amount of time, the unpleasant fact is that adoption of these solutions is frequently blocked since firms tend to focus more on development than on model operationalization. ModelOps, on the other hand, comes to the rescue, providing AI advancement.
ModelOps Tools
Because the ModelOps methodology brings all the stakeholders together, numerous new start-ups and corporate firms provide ModelOps solutions to coordinate these components collectively in an end-to-end automated model life cycle.
ModelOp centre and other advanced platforms usually connect with development platforms, IT systems, and enterprise applications, allowing organizations to utilize and expand their investments in AI and IT. As a result, data scientists may operate at scale with the technologies they are most acquainted with
Use of ModelOps
- The application of ModelOps is concerned with the governance and life cycle management of AI and decision models (including machine learning, knowledge graphs, rules, optimization, linguistic and agent-based models). Management of model development environments, model repository, champion-challenger testing, rollout/rollback, and CI/CD (continuous implementation/continuous delivery) integration are all core features.
- ModelOps allows for the retuning, retraining, or rebuilding of AI models, allowing for an unbroken flow between model creation, operationalization, and maintenance inside AI-based systems.
- ModelOps enables business domain experts to review the quality (understand the outputs and validate KPIs) of AI models in production and promote or demote AI models for inferencing without relying only on data scientists or ML engineers.
Advancement In AI
- AI responds to Distress and Help-Calls
In the case of a disaster, emergency relief agencies inundate with distress and aid calls. Managing such a large volume of calls is time-consuming and costly to do manually. There is also the danger of crucial information being lost or being unnoticed. During such circumstances, AI can serve as a dispatcher around the clock. AI systems and voice assistants can analyze several calls, assess the incident and confirm the location.
- Proactive Disaster Management Using Predictive Analytics
Machine learning and other data science methods are not confined to aiding on-the-ground rescue teams or simply after the catastrophe struck. Predictive analytics, for example, may examine previous occurrences to discover and extract trends and populations prone to natural disasters. To detect at-risk regions and enhance forecasts of future events, use a wide range of supervised and unsupervised learning methods.
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