IAO-DOS is a software platform that provides a unified view of data and accelerates the development and deployment of AI applications. It is built on top of open source technologies, such as Docker, and TensorFlow. IAO-DOS can help organizations of all sizes speed up the development and deployment of AI applications by providing a unified view of data and a set of tools and services for managing data, building and training AI models, and deploying AI applications. IAO-DOS is a flexible and scalable platform that can be used to build AI applications for a variety of industries and applications.
- Data silos: Data silos can make it difficult to find and access the data you need to build AI applications. This can lead to delays in development, inaccurate models, and a lack of visibility into data.
- Data quality: Poor data quality can lead to inaccurate AI models. This can have a negative impact on the performance of AI applications and can lead to costly mistakes.
- Model development and training: Building and training AI models can be a complex and time-consuming process. This can require specialized skills and resources that may not be available to all organizations.
- Model deployment: Deploying AI applications to production can be challenging. This requires a deep understanding of the target environment and the ability to ensure that the applications are secure and reliable.
- AI governance: AI governance is essential for ensuring that AI applications are used responsibly and ethically. This can be a complex and challenging task, especially for organizations that are new to AI.
- The cost of lost opportunities: AI can be used to improve operations, make better decisions, and create new products and services. By not adopting AI, companies may miss out on opportunities to improve their bottom line and stay ahead of the competition.
- The risk of being left behind: The pace of innovation in AI is rapid, and companies that do not adopt AI may be left behind by their competitors. This can lead to lost market share, decreased revenue, and a decline in the company’s valuation.
- The risk of regulatory compliance: As AI becomes more widely used, there is a growing risk of regulatory compliance issues. Companies that do not take steps to comply with the law may face fines or other penalties. This could damage the company’s reputation and make it difficult to attract new customers and investors.
Datakubes can help organizations overcome the challenges of not having IAO-DOS by providing a unified view of data from different sources, improving data quality, automating some of the tasks involved in model development and training, deploying AI applications to production, and implementing AI governance. In addition, Datakubes also provides a built-in machine learning library, a model serving framework, tools for managing data quality and security, tools for monitoring AI applications, and a community of experts who can help organizations with their AI adoption journey.
DataKubes WorkShop, a module of DataKubes Orchestrator, is a complete modular development platform designed to handle the complete extraction, machine learning and visualization workflow process for any kind of data problem.
Once the orchestrator has finished integrating the datapoint and adjusting the modules for running the AutoML or custom modules, it is as easy as creating a DataApp using our no-code DataApp Studio to create a complete UX end user app to be deployed inside or outside the organization.