Big Data and The AI Stack
- [The University of Illinois at Urbana-Champaign]
- Overview
Big Data provides the fuel (data), while AI acts as the engine (algorithms), enabling us to: analyze large datasets for key trends and connections that would otherwise be impossible to identify using traditional manual approaches. Make decisions based on actual experiences, which improves both efficiency and precision.
An AI stack is a collection of technologies, frameworks and infrastructure components that facilitate using artificial intelligence (AI) systems. It provides a structure for building AI solutions by layering these components to support the end-to-end AI lifecycle.
In today’s competitive landscape, AI has transformed from a luxury to a necessity. A deep understanding of the AI stack is beneficial and critical to building cutting-edge products that can revolutionize business operations.
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.
- Big Data And AI Work Together
Big data and AI work together. Big data analytics leverages AI for better data analysis. AI, on the other hand, requires large amounts of data to learn and improve the decision-making process. When it comes to the basic working principles of these two technologies, big data and AI are very similar.
The former involves processing data better, generating insights. The latter uses this data to automate systems and make decisions without any external help. The more data we feed through machine learning (ML) models, the better they work. It’s a virtuous cycle.
With this convergence, you can more easily leverage advanced analytics capabilities, such as augmented or predictive analytics, and more effectively derive actionable insights from large amounts of data.
With big data AI analytics, you can provide users with intuitive tools and powerful techniques to help them extract high-value insights from data, build data literacy across the organization, and benefit from becoming a truly data-driven organization.
How big data and AI work together:
- AI leverages big data: AI algorithms require large amounts of data to learn patterns and make accurate predictions, and large data sets can provide these predictions.
- Big data benefits from AI: AI technology can effectively analyze and extract insights from large, complex data sets, leading to better data management and decision-making.
- Big Data and AI Systems
Big Data and AI systems are intertwined technologies that together revolutionize how we collect, analyze, and utilize information. Big data refers to massive datasets that are complex, diverse, and rapidly growing, often surpassing the capabilities of traditional data processing methods.
AI, on the other hand, focuses on creating intelligent systems that can perform tasks that typically require human intelligence, like learning, problem-solving, and decision-making.
Big data provides the fuel for AI systems, offering the vast amounts of data necessary for machine learning algorithms to learn and improve. Conversely, AI algorithms enable the efficient processing and analysis of these massive datasets, unlocking valuable insights and driving automation.
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 - Linkedin]
- AI Fuels Data Analytics
AI fuels data analytics means that AI technology acts as the driving force behind the power of data analytics, allowing for deeper insights, faster processing, and more accurate predictions by leveraging ML algorithms to analyze large datasets effectively, essentially making data analysis much more efficient and impactful.
Key characteristics about AI in data analytics:
- Data as fuel: Just like a car needs fuel to operate, AI systems require large amounts of data to learn and generate meaningful results.
- Automated analysis: AI can automate repetitive data analysis tasks, freeing up human analysts to focus on more complex problems.
- Pattern recognition: AI algorithms can identify complex patterns and relationships within data that may not be readily apparent to humans.
- Predictive modeling: By analyzing historical data, AI can create predictive models to forecast future trends and outcomes.
- Benefits and Applications of AI in Data Analytics
When we talk about AI in data analytics, we usually mean its subset: machine learning (ML). Machine learning algorithms can automate and optimize analytics, giving businesses a better return on insights. In particular, it can identify things that the human eye can’t.
As a result, you can expand your scope of exploration and drive your business further. AI in data analytics offers new potential for businesses in any field, but customer-facing organizations may find it particularly advantageous. That’s because AI enables businesses to gain more insights from interactions and tailor their services accordingly.
Data is a critical business asset. It’s the driving force behind today’s innovations, enabling businesses to stay competitive in the global marketplace. Now, with the convergence of big data and AI, companies can more easily leverage advanced analytics capabilities like predictive analytics and more efficiently gain actionable insights from their vast data repositories.
With big data and AI analytics, companies can equip users with intuitive tools and powerful techniques to extract high-value insights from data, improve data literacy across the organization, and enjoy the benefits of being a truly data-driven organization.
Examples of AI applications in data analytics:
- Customer churn prediction: Identifying customers likely to leave a company based on their past behavior.
- Fraud detection: Real-time detection of fraudulent transactions in financial systems.
- Market trend analysis: Identifying emerging trends in consumer behavior based on market data.
- Supply chain optimization: Predicting demand fluctuations to optimize inventory management
- Building a Data Architecture to Scale AI
Large-scale data modernization and rapidly evolving data technologies can hinder AI transformation. For today’s data and technology leaders, there is increasing pressure to create a modern data architecture that can fully drive the company’s digital and AI transformation.
Well-managed data architecture and AI technologies are expected to drive future IT innovation and bring better opportunities for enterprises through technological disruption.
However, these trends also show that enterprises need highly capable data science domain experts who are proficient in skills such as artificial intelligence, predictive modeling, machine learning, and deep learning to drive this transformative technology leadership.
This is the key to scaling enterprise-level AI, which can become your greatest competitive advantage. What is data architecture? It describes how your data is managed (from collection to use) using the models, policies, rules, and standards set by your organization.
[More to come ...]
