Mastering AI Training Platforms for Business Success

Exclusive Insights from Aetina’s Product Manager, Troy Lin (Part 1)

In the rapidly evolving landscape of artificial intelligence (AI), training platforms have emerged as pivotal tools for developers. These platforms are not just facilitating the development of AI models but are revolutionizing the way we approach machine learning and algorithm training. We interviewed Troy Lin, Head of Aetina’s MegaEdge/SuperEdge Product Line, to learn more about AI training platforms, and their benefits for different businesses.

What are (Edge) AI training platforms

An AI training platform is a powerful tool that empowers developers to enhance AI models efficiently and intelligently. These platforms leverage high-performance GPUs, such as those from NVIDIA's Quadro or Data Center line-ups, coupled with extensive SSD storage and advanced CPUs capable of sophisticated encoding and decoding operations.

The significance of AI training platforms extends beyond hardware capabilities. They are instrumental in training AI algorithms for diverse applications like object detection, image recognition, and robotic automation.

A notable subcategory within these platforms is Edge AI training platforms. Unlike conventional models relying on centralized data centers, Edge AI training platforms facilitate localized training. This not only reduces energy consumption but also ensures heightened data accuracy and privacy.

Many Edge AI training platforms favor commercial GPUs for cost-efficiency. However, data center GPUs, which are way more powerful and designed for optimal thermal dissipation, are often more suited for intensive AI tasks. For instance, our AIP-FR68 model, a dual-data-center-card system and NVIDIA-certified platform (NCS), is exemplifies the compatibility, performance, and long-term support needed for AI algorithms training, compared to conventional commercial GPUs.

How do AI training and AI inference difference from each other?

In the realm of artificial intelligence, AI inference is often seen as the counterpart to AI training.

While training involves learning from data and building the model, inference is about applying the model to real-world data to make decisions or predictions.

In terms of hardware requirements, inference systems, such as those used in edge computing, prioritize efficiency and rapid response over raw power. They're engineered to run pre-trained models and to make quick decisions, often with constrained resources. This is particularly crucial in applications like real-time language translation, autonomous vehicle navigation, and instant image processing. An example of AI inference platform is Aetina’s AIP-SQ67, an expandable AI inference platform that gives AI developers and system integrators superior graphics and edge AI capabilities through 12/13th Gen Intel Core? processors and its extension AI accelerator MXM module.

On the other hand, AI training is a more resource-intensive process. It's where algorithms learn from vast amounts of data. A fitting example is in the realm of factory automation. Here, AI models are trained to recognize and categorize objects, ensure quality control, and optimize production lines. Such training demands robust computational resources. For instance, training AI to accurately identify defects in manufacturing requires analyzing thousands of images. These tasks necessitate powerful AI training platforms, equipped with high-performance GPUs and efficient data management systems, to handle large datasets and complex algorithms.

To sum up, AI inference and AI training serve distinct functions in the AI lifecycle. Inference systems, typically located at the edge, are designed to execute commands or AI models with minimal computing power. Therefore, AI training demands significantly higher computing power to accommodate extensive GPU support. While inference systems like Aetina’s Jetson Orin-powered devices are compact for edge deployment, they may not offer the same training efficiency as larger platforms equipped with data center cards.

How do AI training platforms differ from traditional software development platforms and other machine learning tools?

The evolution of AI training platforms marks a significant shift from traditional software development paradigms. Unlike conventional software development, which often relies on centralized processing and cloud-based infrastructures, AI training demands more localized and powerful computing resources. This shift is primarily driven by the need for handling large volumes of data and performing complex computations that are intrinsic to AI and machine learning.

A Gartner report highlights that by 2025, 75% of enterprise-generated data will be created and processed outside a traditional centralized data center or cloud, up from less than 10% in 2019. This underscores the need for robust AI training platforms that can cater to the increasing demand for sophisticated AI models and that allow for localized, efficient, and timely processing of data, crucial for sectors like healthcare, manufacturing, and retail where real-time data processing is vital.

Furthermore, the traditional approach to machine learning, which often involves simpler algorithms and smaller datasets, is rapidly evolving. Today's AI models require not only large datasets but also more computational power for training, which is where specialized AI training platforms come in. These platforms are designed to accelerate the training process, reduce latency, and improve the efficiency of model development.

Opting for data-center GPU-based AI training platforms dramatically reduces the time required for training AI algorithms.

For instance, tasks that might take a day using commercial GPU cards can be completed in a few minutes with data-center GPU cards, like those in Aetina’s AIP-FR68. This stark difference in efficiency makes AI training platforms a superior choice over traditional software development platforms for advanced AI model development, demonstrating that these advanced platforms offer the necessary computational power and architectural support to train sophisticated models efficiently.

Curious about the key benefits of AI Training platforms and how to get started? Stay tuned as we unveil more in the second part of the interview!

Explore Aetina's AI Training & Inference platforms here: https://www.aetina.com/products-features.php?t=336