Introduction
A rising trend among businesses in nearly all industries is the development of products using artificial intelligence.
It’s a strategic move, positioning them well for the evolving technology landscape and likely setting them ahead of lagging peers.
But it also comes with many challenges, including acquiring expertise, managing vast amounts of data, effectively connecting different systems, training or integrating AI models, maintaining compliance with ever-changing regulations, and deploying the product to the cloud.
What’s more, what today is a source of competitive advantage will be table stakes soon, and companies that cannot master this technology risk rapid obsolescence on a scale that will dwarf even the rise of the Internet and Web 2.0.
Some fortunate companies who have invested in public cloud infrastructure have in-house personnel who can manage the cloud deployment and know how to optimize their cloud environment for their AI products, but this is not always the reality for growing businesses with limited internal resources.
It helps tremendously to partner with a cloud provider with strong AI expertise and support, such as Amazon Web Services (AWS), which offers a solid platform for customers to host their AI products.
However, although companies may be adept at using AWS, AI’s complexities change how they will be accustomed to interacting with the software, and it’s essential to go into the process armed with the correct information and strategy.
This report is the second in Defiance’s two-part series on AI in the Cloud, and it covers AWS specifically.
It offers a guide to AWS’s generative AI services and capabilities, benefits, and important considerations.
It also explores how cloud-managed service providers can help you with this process by offloading the lower-value cloud ops functions and offering AWS-specific advice and assistance, helping keep your AWS environment stable and secure.
Basics of AI in the Cloud
Before diving into AWS specifically, this is a brief primer on AI and building in the cloud in general.
Artificial Intelligence (AI) is a broad discipline within computer science focused on developing systems capable of performing tasks typically associated with human intelligence.
AI encompasses various techniques, such as machine learning, natural language processing, and problem-solving. Some essential terms in AI include:
- Machine learning (ML): A subset of AI where algorithms are trained on data to learn patterns and make predictions or decisions without explicit programming.
ML techniques such as deep learning, or multi-layered neural networks, create the foundation for models like Generative AI. - Generative AI: An AI category aimed at generating new, original content.
Unlike conventional AI models that operate within predefined rules for specific tasks, generative AI can produce novel outputs, such as images, text, or other data types. - Large Language Models (LLMs): Specific applications of machine learning and generative AI, such as OpenAI’s GPT, trained on extensive text data to understand, generate, and manipulate human-like language for tasks like text completion, translation, and question answering.
From a business case perspective, AI technologies offer opportunities to enhance productivity and efficiency in operations and beyond.
When utilized effectively, AI-based solutions can automate repetitive tasks, improve product output, and enhance decision-making through analytics.
For instance, leveraging foundational models like LLMs enables businesses to train AI systems for domain-specific tasks, such as developing advanced chatbots for customer engagement and support.
Despite the potential benefits, implementing AI solutions poses challenges.
Building and training AI products are complex, and deploying them in the cloud entails many considerations and decisions that vary based on the size of the company, its infrastructure, its goals with the product, and its resource constraints.
Some important factors include whether to build a custom or use a pre-trained AI model, deciding whether to build, train, and test AI models on-premise or in the cloud, structuring your data correctly, and establishing the correct security protocols.
Your current cloud provider influences many of these decisions; for example, some providers offer training support to enable their customers to train AI models in their cloud environment, such as AWS with its Sagemaker tool, influencing whether or not you train on-premise.
To dig deeper into these and other considerations, review Part 1 in our AI series.
The following section provides an overview of setting up AI in the cloud, focusing on tools and services offered by AWS cloud service.
AI in AWS
AI is the wave of the future, but businesses need more straightforward methods to build and deploy generative AI applications securely.
While we wait for supportive and advanced tools to assist with building AI products, partnering with a cloud provider like AWS goes a long way toward streamlining this process.
AWS is the dominant player in the cloud computing market, holding a significant market share.
With AI-specific tools for application development, extensive security expertise and controls, and robust AI-centric partner and integration capabilities, they are committed to helping their many customers achieve their AI technology goals.
However, is AWS the right choice for every business, and how do customers make that decision?
AWS Offerings and Value
Customers may choose AWS for several reasons, and AWS offers a diverse range of tools and services to accommodate companies with different goals, infrastructure requirements, and other variables.
Below are some of the most valuable offerings and benefits of AWS.
Multi-Model Support
AWS’s platform allows customers to deploy many different open-source large language models in the cloud, making it user-friendly and expansive, and they can choose the suitable model at the right size for their use case.
Businesses can customize AI solutions according to their unique needs, such as fine-tuning pre-trained models or blending multiple models to create highly specialized applications.
Companies can also select the most efficient model for their task, avoiding the higher costs associated with using a more robust model than necessary until they need to scale usage to meet changing demand.
Flexible Instance Computing
AWS offers customers an extensive and flexible range of accelerated computing options as they build, train, and test their models and end products.
This is great for companies that want to host their AI product development entirely in the cloud or divide the process between AWS and their local environments.
WS’s specialized EC2 instances, in particular, are designed to provide high-performance computing resources for machine learning tasks, enabling companies to leverage advanced machine learning capabilities without investing in expensive hardware or managing complex infrastructure.
AWS also offers broad functionality for model-building tasks, including data annotation, distributed training, and distributed inference through SageMaker.
AI Application Support
AWS enables creating and using AI applications on its platform with Amazon Bedrock.
The platform offers pre-built applications that companies can customize, software tools that help them create new applications, and native or optimized integrations so that customers can connect with familiar software products.
To help development further, AWS also offers Amazon CodeWhisperer, which enhances the productivity of software developers and allows them to build apps faster and more securely with an AI coding companion.
Security
Customers can rely on AWS’s operational maturity in security, reliability, and performance, complemented by a comprehensive suite of operational excellence tools.
Security in AI is a moving target and especially difficult to keep up with if a company is focused on growth and simply staying afloat.
Still, customers gain peace of mind by trusting AWS with the lion’s share of it.
AWS’s built-in security tools include embedded security and privacy measures.
AWS also collaborates with partners to address many common security challenges, including IP and data privacy, toxicity, and fidelity, catering to specialized demands and unique business requirements.
AWS also prioritizes data privacy and ensures that tech vendors and partners comply with data policies; partners vetted through AWS can be trusted, saving customers time.
Partner Network
The AWS Partner Network (APN) offers a variety of specialized generative AI applications from a large community of customers and partners.
This is useful for growing companies with varying in-house resources and personnel.
When a growing company has gaps in expertise and tools, it can access an extensive network of partners, from AI models to DevOps automation tools to MSPs.
AWS’s flexibility and scalability are vital advantages, as they allow companies to adapt their cloud infrastructure and services to their specific needs, even as they evolve.
Cost Efficiency
Using AWS for your AI tool is especially advantageous if you are a customer already.
Some other cloud providers, like Google Cloud Platform (GCP), have AI support tools that receive positive feedback.
While this report will not delve into a competitive analysis, we can point out the cost-benefit of building and maintaining your AI tool in your existing cloud environment.
For example, while GCP has several tools for training and inference, training datasets are typically massive, and ingress/egress fees can be substantial.
When considering the total cost of ownership, including migration costs, ongoing maintenance, and any potential disruptions to operations during the transition, the costs may outweigh the benefit of using a different provider.
AWS Tools and Products
AWS offers a broad range of tools and services to facilitate AI development.
They aim to provide businesses with simplified methods for deploying generative AI applications while ensuring responsible and secure development.
Below are some of the most impactful AWS tools and products that collectively provide a robust infrastructure for businesses to develop, train, and deploy AI models efficiently and at scale.
Amazon Bedrock: Amazon Bedrock is a tool that enables customers to develop and scale generative AI applications with built-in security and privacy features.
It facilitates foundational model fine-tuning and integration. With Amazon Bedrock, users can accelerate the development of generative AI applications using foundational models through an API without the need to manage infrastructure.
Amazon Bedrock supports leading foundation models, including those from AI21 Labs, Anthropic, Stability AI, and Amazon.
Users can also privately customize FMs using their organization’s data and leverage comprehensive AWS security capabilities.
Amazon Titan: Amazon Titan helps developers create and use advanced language models and algorithms more quickly. It’s designed to automate tasks like summarizing or generating new text, through Amazon Titan Text FM, making it faster and simpler to build applications that can understand and generate human-like text.
It also enhances search accuracy and improves personalized recommendations with Amazon Titan Embeddings FM.
Amazon CodeWhisperer: Amazon CodeWhisperer is an ML-powered service that enhances developer productivity by generating real-time code recommendations based on developers’ comments within the integrated development environment (IDE).
CodeWhisperer also works within the command line with features like personalized code completions, inline documentation, and AI natural-language-to-code translation.
Additionally, CodeWhisperer can scan code to identify hard-to-find vulnerabilities, flag code that resembles open-source training data, and filter by default.
Amazon Sagemaker: Amazon SageMaker is for companies that want to build their own foundational models.
It provides developers and data scientists the tools to build, train, and deploy machine learning models quickly and at scale.
Its features include data labeling, model training, hyperparameter tuning, and deployment.
SageMaker is designed to simplify the machine learning workflow so that users can experiment with different algorithms, optimize models, and deploy them into production.
AI-Focused E2 Instances: AWS EC2 instances are part of Amazon’s Elastic Compute Cloud (EC2) service.
This specialized hardware provides scalable computing capacity in the cloud and is designed for specific tasks such as machine learning inference and training.
- AWS Inferentia is a custom-designed machine learning inference chip that provides high-performance inference for various machine learning frameworks.
It is optimized for deep learning workloads and can deliver low-latency, high-throughput inference. - AWS Trainium is a custom machine learning chip designed for training machine learning models.
It offers high performance and efficiency for training deep learning models, enabling faster training times and lower costs than traditional GPU-based instances. - AWS Inferentia2 is an updated version of the AWS Inferentia chip, offering improved performance and efficiency for machine learning inference workloads.
It is designed to provide even faster inference times and lower costs than the original AWS Inferentia chip.
RAG Tools: Retrieval-augmented generation (RAG) enhances the output of large language models by referencing an authoritative external knowledge base.
This method helps manage the vast amounts of data in large language models.
Amazon Bedrock users can connect FMs to their data sources to employ RAG, automating tasks like vector conversions and retrievals and improving output generation.
Alternatively, organizations managing their RAG processes can use Amazon Kendra, an optimized Retrieve API and high-accuracy semantic ranker, as an enterprise retriever for RAG workflows.
The Value of an MSP for AI Products
The primary value of an MSP lies in guidance, consulting, and expertise in navigating AWS; for organizations aiming to develop an AI-driven product like a ChatGPT-based chatbot integrated with their data in the cloud, complexities arise, especially when standard EC2 instances don’t suffice.
In such scenarios, they seek an MSP’s expertise to navigate AWS solutions tailored to their specific requirements and to help with things like…
- Navigating AWS: An MSP leverages established partnerships with AWS; they help integrate existing AWS resources with other offerings and demystify the process for clients.
- Creating a Data Strategy: MSPs help create a data strategy, helping you assemble the information and plan necessary for setting up a retrieval augmented generation pipeline and optimizing a vector database.
- Assisting AI Model Selection: MSPs provide insights; they might highlight better-performing models, present benchmarks, and guide implementation on AWS, ensuring optimal choices for specific needs.
- Troubleshooting with AWS Expertise: MSPs handle daily operations and decipher AWS-related issues. If unexpected errors arise, they leverage their AWS knowledge to identify solutions or direct clients to specialized AWS support for specific concerns.
- Optimizing Costs: MSPs analyze AWS usage, recommend cost-effective services, and implement savings plans for running specific EC2 instances.
- AWS Service Integration: MSPs help integrate AI products with various AWS services, such as S3 for data storage, Lambda for serverless computing, and SageMaker for machine learning model training and deployment.
- AWS Infrastructure Management: MSPs manage the underlying infrastructure required for AI products on AWS, ensuring it is scalable, reliable, and cost-effective.
- AWS Well-Architected Framework: MSPs can help ensure that AI products adhere to the Framework, following best practices for security, reliability, performance efficiency, cost optimization, and operational excellence.
MSPs are crucial in helping businesses fully leverage AWS’s capabilities.
Partnering with an MSP can be invaluable for companies looking to develop and deploy AI solutions without the in-house capability to manage the intricacies of AWS.
Conclusion
Knowledge and support are the keys to success for companies that want to invest in AI while taking advantage of the many tools that AWS has to offer.
This combination will help them navigate the complexities of AI and AWS and ensure their success in the ever-evolving digital landscape. Equip yourself with information and reliable partners to thrive.
