Now more advanced artificial intelligence

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ModelOps is a set of automated tools and practices that help deploy, manage, monitor, and improve models in production. The approach is designed to be model-centric, which means everything is instrumented around the model, from deployment and governance to inference and monitoring at scale.

By far, investments in artificial intelligence and machine learning are increasing dramatically and new data science projects are underway to create predictive and analytical models for various purposes. However, while companies plan to scale sophisticated artificial intelligence solutions to scale within a reasonable time frame, the harsh reality is that adoption of these solutions is often stalled as companies tend to focus more on the market. development than on the operationalization of models. On that note, ModelOps comes to the rescue by bringing advancements in AI.

ModelOps Tools

The ModelOps approach bringing together all players, several emerging start-ups, as well as companies, offer ModelOps solutions to collectively orchestrate these components in a fully automated end-to-end model lifecycle. Let’s take a look at the figure below which shows how, by managing a platform, companies can govern and evolve all AI initiatives.

Powerful platforms such as the ModelOp Center typically integrate with development platforms, IT systems, and enterprise applications so that businesses can leverage and expand on-going investments in AI and technology. computer science. This way, data scientists can work at scale using the tools they know best.

ModelOps-

  • primarily focuses on governance and management of the AI ​​lifecycle and decision models (including machine learning, knowledge graphs, rules, optimization, linguistic and agent-based models ). Core capabilities include managing model development environments, model repository, champion-challenger testing, model deployment / rollback, and CI / CD integration (continuous implementation / continuous delivery)
  • enables the readjustment, recycling or reconstruction of AI models, providing an uninterrupted flow between the development, operationalization and maintenance of models within AI-based systems
  • gives business experts the autonomy to assess the quality (interpret results and validate KPIs) of AI models in production and facilitates the ability to promote or demote AI models for inference without relying entirely data scientists or ML engineers.

More advanced AI

AI responds to distress and help calls

Emergency services are inundated with distress calls and emergency help. Handling so many calls is time consuming and expensive when done manually. Risks that critical information may be lost or not observed are also possible. In such cases, AI can function as a 24/7 dispatcher. AI systems and voice assistants can analyze massive amounts of calls, determine the type of incident that has occurred, and verify the location. They can not only naturally interact with callers and handle those calls, but can also instantly transcribe and translate languages. AI systems can analyze tone of voice for urgency, filter redundant or less urgent calls, and prioritize them based on urgency.

Predictive analytics for proactive disaster management

Machine learning and other data science approaches aren’t limited to helping relief teams in the field or helping only after the real emergency. Machine learning approaches such as predictive analytics can also analyze past events to identify and extract patterns and populations vulnerable to natural disasters. A large number of supervised and unsupervised learning approaches are used to identify areas at risk and improve predictions of future events. For example, clustering algorithms can categorize disaster data based on its severity. They can identify and separate climate patterns that can cause local storms with cloudy conditions that can lead to a widespread cyclone.

Predictive machine learning models can also help managers distribute supplies where people go, rather than where they were, by analyzing people’s behavior and movements in real time.

In addition, predictive analytics techniques can also provide information to understand the economic and human impact of natural disasters. Artificial neural networks collect information such as region, country and type of natural disaster to predict the potential monetary impact of natural disasters.

Recent advancements in cloud technologies and many open source tools have enabled predictive analytics with almost no initial investment in infrastructure. Thus, agencies with limited resources can also create systems based on data science and develop more sophisticated models for analyzing disasters.

As with any advancing technology, AI will also build on its existing capabilities. It has the potential to eliminate blackouts before they are detected and give disaster response officials an informed and clearer picture of the disaster area, saving lives.

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