Challenges In Building AI Applications

Many new AI driven applications and solutions are being developed everyday. In addition, every software product and service developer is envisioning to integrate AI to make their application more intelligent. However, AI application developers are running into many challenges:

  • AI Model Development – The most challenging task is the development of AI models that work for all users of an application.

  • AI Models Performance Decays with Time – It is well known fact that the performance of AI models decays with time for a number of reasons. Today no product or technology exists that can monitor AI models and stop their decay in acceptable timeframe.

  • Availability of good quality training data – Another challenge to the AI development is the availability of labeled training data. Often, the training data available for AI models is insufficient or does not generalize well, resulting in their poor performance.

Robust Machines’s technologies and solutions address these challenges.

Future of AI Driven IOT Applications

AI for IOT applications has very different requirements compared to most other domains. Such applications require a very large number of AI models to achieve good performance. For example, a smart factory has many different types of machines and processes. Each unique type of machine or process will need different type of AI for predictive maintenance, anomaly detection, fault diagnosis, and bottleneck detection. If a machine is complex, we may have to model its components separately with their own AI models. The number of AI models required to manage  large manufacturing organization grows exponentially.  Today most vendors who are developing AI solutions  for IOT, they use similar AI model design and architecture for different types of use cases.  This approach results in poor performance in most cases.

RMC has developed AI4AI technology which significantly reduces the cost of AI development for IOT applications and provides the best possible performance for every use case. RMC’s AI4AI technology can auto generate good quality custom models by using use-case and device specific training data for various IOT applications. For example, RMC will auto-generate different AI models with unique architectures for predictive maintenance for different types of machines. RMC AI4AI service can also collaborate with data scientists to help improve their productivity by manyfold.

Our Solutions

RMC ‘s products enable enterprise and IOT application and solution developers to integrate high quality AI models, and monitor and manage their performance in real time.

AI Service Center

RMC AI Service Center with its MLOPs features takes away mundane activities from data scientists and engineering, and helps enterprises integrate and manage AI in closed loop fashion in their products and services. It is also the only solution that can do real time performance management of AI models.

AI4AI Center for IOT

The AI 4 AI Center for IOT allows auto generation of data driven AI models, simplifies the integration and performance management of AI in various IOT applications, and also offers turn key AI solutions for predictive maintenance, fault diagnosis, anomaly detection, and process monitoring.

AI4AI Center for Anomalies

The AI 4 AI Center for Anomalies allows auto generation and performance management of sophisticated data driven AI models, simplifies the integration and management of AI in anomaly detection applications such as cyber threat detection, fraud detection, and defect detection for manufacturing.









Our Technology

RMC’s product and platforms are based on innovative technologies that have allowed us to monitor the performance of AI models in real time, as well as automatically generate solution centric AI.

What are MLOps and Why Does it Matter?

MLOps is collaboration framework between data scientists and the operations or production team to reduce development cycle, improve productivity, eliminate waste, automate as much as possible, and produce richer, more consistent insights with machine learning. MLOps follows a similar pattern to DevOps. It shortens production life cycles by creating better products with each iteration and drives insights you can trust and put into play more quickly.

AutoML: The Next Wave of Machine Learning

The success of machine learning in a wide range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts¹. Automated machine learning (AutoML) is the process of automating the end-to-end process of applying machine learning to real-world problems. AutoML makes machine learning available in a true sense, even to people with no major expertise in this field.

Bringing the Power of AI to Internet Of Things

With a wave of investment, a raft of new products, and a rising tide of enterprise deployments, artificial intelligence is making a splash in the Internet of Things (IoT). Companies crafting an IoT strategy or seeking to get more value from an existing IoT deployment may want to explore a role for AI. Gartner predicts that by 2022, more than 80 percent of enterprise IoT projects will include an AI component, up from only 10 percent today.

“To raise new questions, new possibilities, to regard old problems from a new angle, requires creative imagination and marks real advance in science.”


Solution Partners & Supported Integrations

RMC supports or planning to support integration with all key EAM/CMMS solution vendors and IOT platforms.

Discover The Future

We are pioneers in integrating AI in a scalable fashion for Enterprise and IOT applications.

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