AI Platforms – The Next Step for Artificial Intelligence

Some organizations have reaped significant rewards from the use of Big Data. AI platforms (Artificial Intelligence), which are a recent evolution in Big Data processing, have taken it to a whole new level. AI platforms are expected to have a significant impact on the world (and disrupt it) in the coming decade. AI will improve Business Intelligence and Analytics, among other technologies, by processing massive datasets.

Anil Kaul is the CEO and cofounder of Absolutdata. He says that in mid-2000s, a concept was developed to use Big Data to “train Artificial Intelligence. This concept has been successful with many successes. Machine Learning (ML), Deep Learning(DL), and New Data Architectures led to a smarter AI in the years that followed.

Machine Learning is a method of analyzing data and learning from it. Algorithmic methods include decision-tree learning clustering reinforcement training and logic programming. Deep Learning is a method of developing Artificial Intelligence using algorithms and “artificial neuron networks”.

What is Artificial Intelligence (AI)?

Anil Kaul, in a recent DATAVERISTY interview, discussed the current landscape for these technologies and how new AI platforms such as those created by Absolutdata, are changing the Data Management industry. Kaul says that Artificial Intelligence (AI) and Machine Learning is used today as a personal assistant for internet research, answering the phone, making predictions about sales, and driving cars.

Kaul also spoke about the structure of these technologies that are highly aligned. Machine Learning is generally viewed as both a training process for AI as well as a more primitive version of AI. Artificial Intelligence is the more advanced concept. They are both allied technologies that work together. Deep Learning, which is also used in teaching, is considered to be a more sophisticated version of Machine Learning. These technologies are already showing great promise when used with a targeted focus.

“As an instance, while everyone uses analytics to create email campaigns, we found that the AI-driven campaign increased sales by 51 percent. AI can generate and recommend campaigns, while analytics can determine who to target.

The AI Platform

A framework that is designed to be more intelligent and efficient than other frameworks. Kaul said that “when designed well” the AI platform is a powerful tool. It allows organizations to collaborate more efficiently and effectively with Data Scientists. A Data Governance platform can be used to ensure that a team consisting of AI engineers and scientists follows best practices. It can also help to ensure that the work is distributed evenly and completed faster.

The five logic layers that make up an Artificial Intelligence Platform are usually organized as follows:

  • Data and Integration Layer allows access to data. This is important because developers don’t hand-code rules. AI “learns” the rules by using the data that it has.
  • Data Scientists can develop, test and prove hypotheses using the Experimentation Layer. An Experimentation layer that is well designed offers automated feature engineering, feature selection and model selection.
  • The Operations and Deployment Layer is responsible for model governance and deployment. Here, the model governance team can validate model risk assessments. This layer provides tools for managing the deployment of “containerized models” and components across the entire platform.
  • The Intelligence layer supports AI when it is working (training takes place in the Experimentation layer). The Intelligence Layer is responsible for organizing and delivering intelligent services. It is also the main component in service delivery. This layer should have implemented concepts such as dynamic services discoveries to provide a flexible platform for cognitive interaction.
  • The Experience Layer is a layer that interacts with the user through technologies like augmented reality and conversational UI. This layer is usually controlled by a team of cognitive experience experts who strive to create meaningful and rich experiences using AI technology.

Artificial intelligence: pros and cons

Exceeding Expectations

Kaul said that using Artificial Intelligence for the analysis of Big Data could provide a better understanding of both external and internal dynamics that impact a business. Artificial Intelligence can be supported by the latest Big Data Architectures and Machine Learning. Kaul says that a cutting-edge AI platform would include:

  • AI can access all data
  • It can learn from the past of a client or a prospect
  • It uses the experience of previous clients and shows strategies that have worked in the past.
  • AI learns from patterns that humans may miss.
  • AI adapts to new data in real time and learns from it.
  • The system provides guidance based upon changing data
  • Machine Learning is a part of AI

Kaul explains that there are three prerequisites to maximize the benefits of cutting-edge Artificial Intelligence. First, an Analytical Framework. Analytical Frameworks have been developed to solve complex business problems. Kaul said that using an Analytical Framework was critical to supporting the system’s Artificial Intelligence capabilities and Machine Learning.

Context is a must. Artificial Intelligence (AI) and Machine Learning have a very difficult time determining context. AI can detect trends and determine what’s happening in data. But to go beyond that to make recommendations about what the staff should do, “context is required,” said Kaul. AIs are not yet capable of determining context. This is something that is being hoped for. A human must determine and add context to the model.

Third, the technology must be appropriate. A platform that is AI-supported must be scalable, unlike traditional Analytical System, in order for AI to learn and create solutions. An Analytical System delivers insights into the data while AI provides recommendations in real-time.

Different approaches are used to scale databases up to large sizes while simultaneously increasing the number of transactions per second. The majority of Database Management Systems use partitioning to break up tables with a lot of data. This technique allows the database to be scaled across multiple database servers. Multi-core CPUs and large SMP multiprocessors as well as 64-bit microprocessors are now able to support multiple-threaded implementations, which can provide a significant scaling-up of transaction processing capacity.


The NAVIK AI Platform was developed by Absolutdata and supports a growing number of AI-enabled solutions designed for sales and marketing. Absolutdata provides AI-powered SaaS products to sales professionals, front line marketers, and Analytics teams, said Kaul. Absolutdata comes with over 300 Data Engineers, Visualization Experts, and Data Scientists on staff, allowing them to integrate Machine Learning models, build custom dashboards, and help develop the AI platforms to their full potential.

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