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Big Data and The AI Stack

The University of Illinois at Urbana-Champaign_031422A
[The University of Illinois at Urbana-Champaign]


- Overview

In today’s competitive landscape, AI has transformed from a luxury to a necessity. A deep understanding of the AI ​​technology stack is beneficial and critical to building cutting-edge products that can revolutionize business operations.

The AI stack is a conceptual model that represents the different layers of technologies and components used to develop and deploy artificial intelligence.

The AI stack uses machine learning frameworks to facilitate predictive analytics based on statistical theory and probabilistic models. Some of the most commonly used frameworks include: TensorFlow, PyTorch, Scikit-Learn, Spark ML, Torch, Keras.

The Generative AI tech stack is structured in three layers: Applications, Model, and Infrastructure. 

The layers of AI systems include:

  • Hardware
  • Libraries and Frameworks
  • Pre-trained Models
  • Training and Fine-tuning
  • AI Applications and Services
  • Adaptive AI Applications
  • AI-to-AI Interaction

 The data collection layer of an AI stack is composed of:

  • Software that interfaces with devices
  • Web-based services that supply third-party data

 Some alternatives to Stack AI include: Langdock, Deeploy, FieldDay, Obviously AI.


- The AI Stack

The AI Stack is for the development and deployment of Artificial Intelligence (AI), and the strategic investment in research, technology, and organizational resources required to achieve asymmetric capability. 

Over the past few years, there has been a drastic acceleration in the development of AI fueled by exponential increases in computational power and machine learning. 

This has resulted in corporations, institutions, and nation-states vastly accelerating their investment in AI to: 

  • perceive and synthesize massive amounts of data, 
  • understand the contextual importance of the data and potential tactical/strategic impacts, 
  • accelerate and optimize decision-making, and 
  • enable human augmentation and deploy autonomous systems. 


Big Data And AI Work Together

Big data and AI have synergies. Big data analytics leverages AI for better data analysis. AI, in turn, requires vast amounts of data to learn and improve decision-making processes. When it comes to the fundamental workings of the two technologies, Big Data and AI can not be far apart. The first one deals with better handling of data, generating insights. And the second one uses that data to automate systems and make decisions without any external help. The more data we put through the Machine Learning (ML) models, the better they get. It’s a virtuous cycle.  

Through this fusion, you can more easily leverage advanced analytics capabilities such as augmented or predictive analytics and more effectively derive actionable insights from massive amounts of data.

With big data AI analytics, you can provide users with the intuitive tools and powerful techniques they need to extract high-value insights from data, building data literacy across your organization while reap the benefits of being a true data-driven organization.


- Big Data and AI Systems

Big data is no longer the necessary term a few years ago, but that doesn't mean it's everywhere. If anything, big data is just getting bigger. This might once have been considered a major challenge. But now, it is increasingly seen as an ideal state, especially in organizations that are experimenting and implementing machine learning and other AI disciplines. 

AI and ML now give us new opportunities to use the big data we already have, and unlock a plethora of new use cases through new data types. For example, we now have more pictures, videos available and data in the form of speech [for example]. In the past, we might have tried to minimize the amount of such data we captured because we couldn’t do much with it, but [it] would have a huge cost of storage.”

For AI systems to decode massive amounts of data, make connections, and generate insights that can be processed, you need to feed them the right data. Unfortunately, this is where most organizations fail. Failure to integrate raw data from disparate sources will allow AI systems to give you useless results. In order for the algorithm to draw accurate conclusions, make sure you run with comprehensive data.  


The AI Stack_120823A
[The AI Stack - Linkedin]

- AI Fuels Data Analytics

When we talk about AI in data analytics, often we are referring to its subset: machine learning (ML). ML algorithms enable the automation and optimization of analytics, giving businesses a better return on their insights. In particular, it can identify things the human eye simply couldn't. 

As a result, you widen your pool of exploration and drive your business further. AI in data analytics offers new potential to businesses in any arena, but customer-facing organisations may find it particularly advantageous. This is because AI enables businesses to pull more insights from interactions and tailor their services in turn. 

Data is a critical business asset. It’s what drives innovation today and enables firms to stay competitive in the global marketplace. And now with the convergence of big data and AI, companies can more easily leverage advanced analytics capabilities like predictive analytics and more efficiently surface actionable insights from their vast stores of data. 

With big data and AI-powered analytics, firms can empower their users with the intuitive tools and robust technologies they need to extract high-value insights from data, fostering data literacy across the organization while reaping the benefits of becoming a truly data-driven organization.


- The Convergence of Big Data and AI

Big data and AI have a synergistic relationship. Data is the fuel that powers AI. The massive, complex, and rapidly evolving datasets referred to as big data make it possible for machine learning applications to do what they were built to do: learn and acquire skills. Big data supplies AI algorithms with the information necessary for developing and improving features and pattern recognition capabilities. 

Without large quantities of high-quality data, it wouldn’t be possible to develop and train the intelligent algorithms, neural networks, and predictive models that make AI a game-changing technology. 

AI, in turn, helps users make sense of sprawling, diverse datasets and sort through unstructured data that can’t be organized into neat rows and columns. AI enables firms to use big data for analytics by making advanced analytics tools more powerful and accessible, helping users discover surprising insights in data that was once locked away in enterprise information silos. 

Leveraging big data, AI, and advanced analytics, companies can provide their decision-makers with greater clarity and understanding of the many factors influencing their business while encouraging creative, intuitive exploration of large-scale, multi-dimensional datasets.  


Building a Data Architecture to Scale AI

Large-scale data modernization and rapidly evolving data technologies can tie up AI transformations. For today’s data and technology leaders, the pressure is mounting to create a modern data architecture that fully fuels their company’s digital and AI transformations. 

Well-managed Data Architecture and AI technologies are poised to drive future innovations in IT, which will bring in better opportunities for businesses through technological disruptions. However, these trends also indicate that the businesses will need highly capable Data Science field experts, groomed in AI, predictive modeling, ML, and DL, among other skills, to drive this transformative tech leadership. 

Big data platforms and big data analytics software focuses on providing efficient analytics for extremely large datasets. These analytics helps the organizations to gain insights, by turning data into high quality information, providing deeper insights about the business situation. This enables the business to take advantage of the digital universe.

Information Architecture plays a key role in establishing order in the continuous evolution of emerging data technologies. Organizations should take to embrace AI and streaming data technologies, and the long-range impact of General Data Protection Regulation (GDPR) on enterprise Data Management practices. While streaming data is the only way to deal with the high velocity of big data, strong Data Governance measures will ensure GDPR compliance. 


- The New Enterprise AI Technology Stack

Digital transformation requires new software technology stacks and new ways of developing enterprise AI applications.

To develop an effective enterprise AI or IoT application, it is necessary to aggregate data from across thousands of enterprise information systems, suppliers, distributors, markets, products in customer use, and sensor networks, in order to provide a near-real-time view of the extended enterprise. 

Today’s data velocities are dramatic, requiring the ability to ingest and aggregate data from hundreds of millions of endpoints at very high frequency, sometimes exceeding 1,000Hz cycles. The data need to be processed at the rate they arrive, in a highly secure and resilient system that addresses persistence, event processing, machine learning, and visualization. This requires massively horizontally scalable elastic distributed processing capability offered only by modern cloud platforms and supercomputer systems. 

The resultant data persistence requirements are staggering. These data sets rapidly aggregate into hundreds of petabytes, even exabytes. Each data type needs to be stored in an appropriate database capable of handling these volumes at high frequency. Relational databases, key-value stores, graph databases, distributed file systems, and blobs are all necessary, requiring the data to be organized and linked across these divergent technologies.


[More to come ...]

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