ML Cloud: How Artificial Intelligence Is Learning in the Cloud

The artificial intelligence capacity to generate texts, create images and brings memes to life is dependent on machine learning. This process is to blame for the AI’s inability to draw hands in 2024 — it simply hasn’t learned this yet. Machine learning is a multifaceted operation, requiring much data to stuff into a model. So it was convenient to move it to a specialized infrastructure, ML Cloud. Here is an explainer on how it is built, why it is convenient to use it and in what way it can be enhanced.

What is ML Cloud?

ML Cloud is a cloud technology, which, thanks to its capacity, is suitable for machine learning. So, this is a set of all tools for algorithm creation and data analytics automation to advance artificial intelligence, which are hosted on remote servers and are accessible to users via the internet.

In the cloud, all machine learning processes can be fully integrated, from data processing to deploying a test environment. ML Cloud is also related to concepts like:

• MLOps (Machine Learning Operations) — the automation of the machine learning process, which involves continuous system updates, monitoring its activity, and working with data. Equipped with powerful GPUs, the cloud helps set up such a seamless system.
• MLaaS (Machine Learning as a Service) — a suite of ready-made tools for machine learning based on cloud technologies.

Since machine learning is a resource-demanding process, placing it in the ML Cloud offers several advantages:

• The cloud can be easily scaled to generate more content and store it.
• Powerful GPUs, processors, and data storage systems are expensive, and renting them from a cloud provider significantly reduces CapEx.
• Cloud providers ensure resource availability and stable operation. For the correct launch of ML models, such an uninterrupted functioning is essential.
• The cloud operator’s IT team takes over infrastructure management, freeing the client’s ML engineers to focus more on their product.

How do cloud solutions for machine learning work?

At the hardware level, this cloud solution works by adding a graphics card (GPU) to the PCI slots of the hosts. The capabilities of this GPU are then virtualized (vGPU) and allocated to individual users.

Each GPU is characterized by parameters like:

• Performance, measured in teraflops (TFLOP), indicating how many trillion operations the GPU can perform per second.
• Video RAM (VRAM), which is dedicated to processing the GPU’s graphical data.
• Memory bandwidth, which shows the amount of data transferred from memory to the processing center in a given unit of time, usually measured in terabytes per second.

Because GPUs are essential for machine learning, GigaCloud implements its ML Cloud as a GPU Cloud.

One key aspect of machine learning is data processing and storage, as AI development requires constant data handling and the generation of new information based on previous data. ML Cloud allows quick handling of large datasets: it aids in data analysis and standardization. It can also host Big Data processing tools like Tableau or Apache.

ML Cloud can be further enhanced through load distribution across multiple platforms using a multicloud approach. For example, Walmart distributed its machine learning platform, Element, between two operators, data centers in different regions, and its own servers. Hundreds of GPUs allow the company to gather information on purchases and consumer preferences, analyze markets, manage supplies, and personalize online product searches.

Thus, with its powerful resources and the ability to host and integrate any SaaS tools, ML Cloud can fully automate machine learning.

Challenges and limitations of cloud solutions for machine learning

One of the risks inherent in artificial intelligence is the potential for data leaks. When entering confidential information into a machine learning model, users risk making it publicly available. To ensure better data security, operators now offer ML Cloud built on Dedicated IaaS, where users receive separate servers and disk groups while the cloud infrastructure for the cluster is shared; or on private cloud infrastructure, where the client fully controls the isolated infrastructure, and the operator only supports its proper functioning and helps maximize customization. This approach makes the system less vulnerable to cyberattacks, hacking, and data leaks.

Another challenge is cost optimization. As machine learning models develop, ML Cloud requires constant expansion, and if cloud resource usage isn’t monitored effectively, operational costs can become significant. Transferring data from one ML Cloud to another or from on-prem servers to the cloud can also be costly due to the large volume of resources being moved and configured. However, some operators, including GigaCloud, offer free data migration with full technical support. Thus, the challenge lies in finding optimal solutions, the best rates, and providers with the most attractive offers.

To function correctly, ML Cloud requires proper configuration and interaction between all its components, which isn’t always easy to achieve independently. According to the 2024 Connectivity Benchmark Report, 90% of IT professionals struggle with AI system integration. Because of this, hyperscalers often create ready-made machine learning environments based on their cloud solutions. However, this problem can also be addressed by partnering with a qualified cloud operator team that can migrate data smoothly, ensuring all ML system components have API integration. Even better, start building the model directly in ML Cloud from the outset to avoid having to reconfigure it later for a new cloud.