AI Infrastructure Roadmap

- Overview

Aartificial intelligence (AI) infrastructure refers to the hardware and software required to create and deploy AI-driven applications and solutions. 

Robust AI infrastructure enables developers to efficiently create and deploy AI and machine learning (ML) applications, such as chatbots such as OpenAI’s Chat GPT, facial and speech recognition, and computer vision. Companies of all sizes and industries rely on AI infrastructure to help them realize their AI ambitions. 

To develop its own AI industry, a country needs a strong foundation in digital infrastructure, including robust computing power, access to large datasets, skilled workforce with expertise in AI, supportive government policies encouraging research and development, regulations to ensure ethical use of AI, and a focus on data privacy and security measures.

- The AI Data Stacks

A new infrastructure paradigm built for AI is emerging to enhance the next wave of enterprise data software development in the AI ​​era. The AI revolution is promoting the evolution of data stacking.

First, AI is powering modern data stacks, and existing data infrastructure companies have begun incorporating AI capabilities into data management for synthesis, retrieval, and enrichment. Additionally, recognizing the strategic importance of the AI ​​wave as a business opportunity, some incumbents have even launched entirely new products to support AI workloads and AI-first users. For example, many database companies now support embedding as a data type, either as a new feature or as a standalone product.

Secondly, data and AI are inextricably linked. Data continues to grow at an astonishing rate, exceeding the limitations of current infrastructure tools. Driven by the ML/AI boom and the synthetic data produced by generative models of all modalities, the amount of data generated (especially unstructured data) is expected to soar to 612 ZB by 2030. (A zettabyte = one trillion gigabytes or one billion terabytes.) In addition to the volume of data, the complexity and diversity of data types and sources is also growing. The company is responding by developing new hardware, including more powerful processors (e.g., GPUs, TPUs), better networking hardware to facilitate efficient data movement, and next-generation storage devices. 

Finally, based on recent advances in machine learning and hardware, a new wave of AI-native and AI-embedded startups are emerging - companies that are either leveraging AI/ML from the ground up or using it to enhance their existing ability. Unfortunately, much of the current data infrastructure and tools are still not optimized for AI use cases. Similar to forcing a square peg into a round hole, AI engineers must create workarounds or hacks within their current infrastructure.

The lack of native and purpose-built tools paves the way for a new AI infrastructure stack for AI-native and embedded AI companies.

- Data and the AI Stack

Data is a fundamental component of the AI stack, which is a collection of tools, technologies, and frameworks used to develop, deploy, and maintain AI solutions. 

The data management layer of the AI stack is responsible for collecting, storing, and processing data to prepare it for analysis and model training. 

Here are some ways that data is used in the AI stack:

• Data quality: The quality of the data is critical to the performance of the model. Poor data quality can lead to biased models and inaccurate decision-making.
• Data sources: AI can use a variety of data sources, including social media, IoT devices, and enterprise databases.
• Data acquisition tools: Tools like web scrapers, APIs, and data brokers can help collect data from different sources.
• AI algorithms: AI algorithms can be used to automate data cleaning and classification, optimize data storage and retrieval, and more.
• AI models: AI models can analyze data to find patterns and insights that can help businesses make real-time decisions.

The stages of the AI stack include: data preparation, model development, deployment, monitoring and optimization, and feedback and improvement.

[More to come ...]