Unveiling the True Potential of Cloud AI With AWS
When considering AI for your business, the choice between cloud and on-premises solutions may seem like comparing two similar options. Is the cloud merely an extension of your own data center, managed by a hyper-scaler? The answer is no.
The reality of AI cloud, especially with AWS, goes far beyond just offloading infrastructure management. It involves leveraging a suite of expertly crafted services designed to streamline operations and accelerate your time to value.
The myths abound about cloud AI services, but with partners like WWT and AWS, navigating your AI-driven future is so straightforward it almost takes the fun puzzle work out of the AI journey. To this point, AWS stands out in the AI arena due to its unparalleled mix of agility, scalability, cost-efficiency, sustainability, and robust security. Their extensive suite of incredibly useful and effective tools and platforms allow companies to deploy AI solutions without the heavy lifting of setting up their own physical infrastructure.
AWS coupled with WWT's independent expertise and tools together present a formidable resource for any business looking to leverage AI. Many corporations have a hybrid strategy and you can get started on AWS today while you work with WWT to acquire NVIDIA infrastructure. Our Edge HPC experts can help you to fully optimize your on-premises hardware in conjunction with the cloud. Whether you are bursting workloads to the cloud, training in the cloud, performing inference on-premises, or any combination of on-premises to cloud with AI, WWT has the experts to assist with your integration.
A Word About Agility
The ability to quickly mobilize extensive computing resources for AI training or inference is a prime advantage of AWS AI. Unlike traditional setups where you might endure delays purchasing and setting up GPUs, AWS allows you to activate the necessary computing power within minutes. This ease of scaling is important, particularly when dealing with large datasets or developing complex language models. Cost is another factor at this juncture. If you were to manage this in-house, the initial hardware investment alone could run into the millions, and you also must factor in wait times for your orders to ship and be set up.
Conversely with AWS, you simply reserve the required GPU instances for the duration of your project. This not only eliminates the hefty upfront costs but also the ongoing expense of maintaining new infrastructure. Once your project is done, you stop paying for the resources—so you have no lingering financial burdens.
You also have cutting-edge hardware from NVIDIA, Intel, and AWS, maintained by AWS and available through a service instance. This model not only optimizes your financial investment but also drastically reduces logistical burdens such as power and cooling requirements, which are far from trivial in large-scale AI operations.
Scale With Ease Without Upfront Hardware Investments
Another important issue is scalability. Consider the scenario of training a large machine learning model. Traditionally, this would require significant upfront investments in hardware and infrastructure. But AWS turns this model on its head. With AWS, you can spin up the necessary resources, handle terabytes to petabytes of data, train your model, and then scale down resources when you're done, paying only for what you use.
The AWS Green Story
AWS is not only powerful and secure; it's also green. AWS has made massive investments in renewable energy, leading the charge among cloud providers to reduce the carbon footprint of digital operations. Opting for AWS AI services means contributing to a more sustainable future, something every business should be thinking about in our climate-conscious world. The carbon footprint of an AWS cloud solution is going to be far more sustainable than anything you are likely do on your own. Some quick data points: AWS infrastructure is 4.1x more energy efficient than on-premises workloads. 3.9 billion liters of water are returned to communications each year from replenishment projects completed or underway. 100% of electricity consumed by Amazon was matched with renewable energy sources in 2023. Source: https://sustainability.aboutamazon.com/products-services/the-cloud?energyType=true
Common Misconceptions
To get into the weeds a little, here are just some of many essential AWS AI capabilities that will address some of the most common myths associated with implementing AI in the cloud.
Myth: The cloud is only used for small AI projects rather than massive trainings.
Truth: Many large models are built on AWS. Anthropic, one of the leading companies with AI models, entered into a strategic collaboration agreement with AWS to train and deploy its future foundation models on AWS Trainium and Inferentia chips. Source: https://press.aboutamazon.com/2023/9/amazon-and-anthropic-announce-strategic-collaboration-to-advance-generative-ai
Myth: You can take any data and combine it with AI, and it will create a model.
Truth: Data management and preparation are oftentimes the most time-consuming aspects of an AI project. There is a lot more work at the data level, from adding your data to a database to putting your data into AI-ready format and pulling all the information you have and organizing it to some degree. AWS offers powerful tools like vector and graph databases, designed to structure database information optimally for AI model consumption. This functionality is integrated through Amazon Bedrock, a foundational layer that facilitates seamless connection of your data to foundational models, generative AI tools, and large language models (LLMs). Bedrock offers many foundation models through its marketplace from which you can create your own generative AI (Gen AI) solutions.
Essentially, Bedrock serves as the critical link between your data foundation and the sophisticated tools needed to harness the data's full potential.
Another game changer is Amazon SageMaker. Amazon SageMaker is a fully managed machine learning (ML) service that enables you to build your own custom models from scratch or in combination with other models. It provides a complete set of tools for MLOps from which you can leverage automation at scale to create models.
For those interested in leveraging natural language processing to access internal company information through simple, human-like queries, Amazon has developed Amazon Q. This AI business assistant allows you to construct a query tool that spans your entire enterprise. It provides a user-friendly interface that empowers employees to interact conversationally with your organization's information, streamlining access and enhancing productivity while maintaining full control over your data.
Another key service to highlight is AWS Kendra, an intelligent search service powered by machine learning. This service enhances the ability to quickly locate precise answers across vast repositories of unstructured data, significantly improving decision-making and productivity for AI-driven projects.
While Bedrock, SageMaker, Amazon Q, and Kendra are all noteworthy, AWS's offers a dizzying array of AI toolsets and services designed to meet the diverse needs of IT leaders in their AI projects. Working with AWS AI experts at WWT, we can help you assess your needs and guide you appropriately.
Myth: If I use LLMs in the AWS cloud, then all my prompts will be public.
Truth: In the AWS environment, there are several mechanisms in place to ensure your data is only seen by you. Your prompts are private as well as your entire environment. Each customer's data is isolated and protected with state-of-the-art encryption and security protocols. Plus, AWS is proactive in its legal and privacy standards, offering assurances and support in compliance with industry and governmental regulations. AWS also offers copyright indemnity coverage for certain popular services. Source: https://aws.amazon.com/service-terms/
Myth: AI in the cloud is prohibitively expensive.
Truth: AWS's pricing model is designed to scale with use, meaning you pay for what you need when you need it. This can significantly reduce costs compared to the capital expenditure of onsite data centers and hardware. With that said, turning off services when they are not in use is key to cost savings.
Myth: The hardware you purchase for on premises AI is better than what you get in the cloud.
Truth: In reality, AWS has NVIDIA's latest processors available in their cloud. AWS also operates UltraClusters for high-scale ML training and HPC. Source: https://aws.amazon.com/ec2/ultraclusters/
Myth: All Gen AI requires GPUs like those created by NVIDIA.
Truth: A lot of foundational AI work is done by CPUs which are much cheaper than GPUs.
So, when you think, 'Oh, I need to go out and buy a data center full of GPUs' that's just not the case. A lot of what you're going to be doing is still going to be on CPUs, which makes the cloud even more attractive because CPUs are at the core. A lot of your choices will depend on the data you are using, the size of the model, and many other factors.
Myth: If you want to use GPUs in the cloud, it is outrageously more expensive than if you bought them.
Truth: Only if you aren't smart about how you use your GPUs. E.G., during training you may be using them 24 hours a day. But when you are done, you can deprovision them and carry no further cost. GPUs are tens of thousands of dollars. If you run GPU in the cloud 24/7 for 30 days and then stop using them when they're not needed, it's going to be far cheaper than if you went out and bought the hardware. You also should build automation (using cloud-native automation tools) to shut down when you're not using them.
To help, AWS has automation programs to manage costs. You can subscribe to a block of processing for a period. For example, you can buy capacity for 1 to 14 days and then it will shut off after that. Source: https://aws.amazon.com/ec2/capacityblocks/
Myth: You must use the most expensive GPUs for your AI strategy.
Truth: Actually, AWS has a processor called Trainium, that was built to train model data. They also have a processor called Inferentia, for high performance at low cost for inference. They may not always reach the same benchmarks, but they commonly reach the benchmarks of cost for performance.
Myth: My data will not be secure enough in the cloud to perform AI work.
Truth: In reality, security is a cornerstone to the AWS business. The AWS architecture was built to be the most secure global cloud infrastructure to build, migrate, and manage applications and workloads.
AWS is also committed to Data Protection and Privacy. They use a shared responsibility model where you are responsible for how you secure your data on the AWS platform while AWS is responsible for protecting the infrastructure that runs the services you utilize. Even if you ran the infrastructure yourself, you would still have to ensure your use of it was secure. AWS relieves a large portion of your operational burden with this model.
WWT's Custom AI Solutions
At WWT, we harness the power of AWS to develop custom AI solutions for our clients. By utilizing tools such as Amazon SageMaker for customized models, AWS Bedrock for seamless AI integration, and Amazon Q for advanced data querying and analytics, our goal is always to deliver tailored solutions that not only meet your specific needs but also adapt and grow alongside your business. We draw on AWS's robust services to construct and support AI solutions, backed by our expert teams of data scientists, engineers, and architects who possess deep and targeted expertise. WWT doesn't just recommend AWS; we rely on it ourselves, using AWS to power our several of our internal tools and boost productivity with AI-enhanced systems.