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Marco Barbosa is a Data Engineering Manager at Caylent with over 20 years of experience working in technology consultancy, helping many diverse industries such as telecomunications, logistics consumer goods, insurance, and agriculture. Marco is curious and passionate about learning about different businesses and applying technology to make things work better. He conducted data projects related to support decision-making, M&A, capacity and resource optimization, operational transformation and digitization, customer satisfaction, digital transformation, innovation, and creation of data products.
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As Caylent's VP of Customer Solutions, Mark leads a team that's entrusted with envisioning and proposing solutions to an infinite variety of client needs. He's passionate about helping clients transform and leverage AWS services to accelerate their objectives. He applies curiosity and a systems thinking mindset to find the optimal balance among technical and business requirements and constraints. His 20+ years of experience spans team leadership, technical sales, consulting, product development, cloud adoption, cloud native development, and enterprise-wide as well as line of business solution architecture and software development from Fortune 500s to startups. He recharges outdoors - you might find him and his wife climbing a rock, backpacking, hiking, or riding a bike up a road or down a mountain.
View Mark's articlesThe ever-increasing pace of technological innovation is pushing businesses to seek ways to stay ahead of the curve and leverage the latest technologies. One area that has gained significant traction is artificial intelligence (AI) and machine learning (ML). With the advent of Generative AI (GenAI), many business leaders have been challenging their teams on how GenAI could enable new business cases, improve efficiency and productivity, and also reduce costs.
Many organizations initially turned to OpenAI to explore these capabilities due to its first-mover advantage. However, the past 12 months have shown the world that putting all your eggs in one basket with one model provider is not a safe bet, as Anthropic briefly launched into the lead with Claude 3, and then Meta released Llama 3 as open source, which quickly proved it has the best price-performance, followed by GPT-4o, only to be supplanted by Claude 3.5 Sonnet's release. As the landscape evolves, these same organizations are realizing the need to continuously look for better alternatives to simplify and scale model deployment with more flexibility, faster integration with corporate systems, native integration with AI tools, protection of intellectual property, enhanced compliance, security and privacy capabilities, and focusing on the business core instead of managing complex infrastructure components.
At Caylent we're building flexible architectures that anticipate evolution and seeing an increasing demand from customers eager to move from OpenAI to this type of approach. For these customers, Amazon Bedrock becomes a compelling option to consider. It is a serverless managed service that allows you to choose from a range of foundation models developed by a growing list of different providers, such as AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon themselves, giving the option to have different models for different tasks.
This marketplace of models makes it quick and easy for you to get started with AI as a developer. Bedrock itself is growing into a suite of services beyond serving foundation models, including Agents for Amazon Bedrock, Guardrails for Amazon Bedrock, Bedrock Studio and more. Additionally, Amazon Bedrock benefits from native integration with other AWS services, like Amazon SageMaker and Amazon Kendra; Lambda and container services, S3, Athena, Redshift, RDS, and other data services; and IAM, CloudWatch, CloudTrail, SecurityHub, and other security and operations services. Your data is securely kept in your own AWS account, ensuring that you have full control and comply with your organization's and industry's security standards.
In this blog post, we will share a few customer stories and review some of the architectural best practices and features of Amazon Bedrock that are attracting product teams away from OpenAI. We will also look at some of its key advantages and how you can leverage them to kickstart your AI journey.
Before we get into the technical details, we wanted to share a few real-world examples. With Amazon Bedrock we can apply Caylent’s frameworks, reference architectures and Catalyst solutions that will help organizations at any size achieve their key results from the first use case giving them full visibility and control needed to scale solutions rapidly and reduce business risks.
Let’s take a look at a few success stories:
OpenAI recently experienced a notable outage. Instead of putting all your eggs in one basket, we recommend designing a highly resilient architecture and having an ecosystem of partners. AWS' global presence consists of dozens of globally distributed regional options, each having 3 or more fault-isolated availability zones, enabling geographically distributed solutions that can be built for stringent SLAs through redundant and self-healing implementations.
Amazon Bedrock enables you to access many different best in class GenAI models from multiple providers, such as Anthropic's Claude family, Cohere Command and Embed, A121 Labs Jurassic, Meta Llama 3, Mistral AI models, Stability AI Stable Diffusion and Amazon Titan (of course) in a single API, across multiple regions worldwide. These providers have launched more than 3 dozen major releases since Bedrock debuted.
You can select the best model for any specific use case, achieving a better price-performance ratio versus relying on a one stop shop solution. To accomplish this, we recommend establishing an LLM benchmarking capability to measure and optimize the LLMs used in your applications. GenAI test suites like the one we developed for the customer example above will pay extra dividends when exploring model alternatives. This approach holds even if your next model change is a planned upgrade from a version to another.
Bedrock Studio can streamline your prototyping process by leveraging a web interface that offers to your development team a rapid manner to create and test new GenAI based solutions, creating multiple workspaces integrated with your corporate authentication for enhanced security.
With the new Converse API, it's simple to switch between different models with little or no change in your application and a fast learning curve for the development team in a more complex environment. This can help accelerate your agent based solutions integrating corporate systems and data sources to extract maximum value from LLMs.
Amazon Bedrock is built to scale. Companies can start small and expand their usage as needed, leveraging AWS’s robust infrastructure to handle increased loads efficiently. This scalability is crucial for businesses expecting growth or fluctuating demand. If necessary, you can even count on provisioned throughput instead of suffering with rate limits without dedicated nodes.
Do you have your own fine-tuned model? No problem, you can easily import models from SageMaker or any other third party provider (currently supports Flan T5, Llama and Mistral models) and benefit from Bedrock's solutions (some product restrictions apply).
With Amazon Bedrock you can focus on what really matters for your business and let AWS take care of the technical details, focusing your effort on prototyping, evaluating models, applying fine tuning and RAG (Retrieval Augmented Generation) techniques, building agents and deploying tasks.
It is possible to enhance models performance monitoring by integrating Amazon Bedrock with CloudWatch, so you can analyze your running models in real time.
At AWS you can benefit from a proven environment with comprehensive built-in products and solutions so you can guarantee governance, privacy, observability and compliance.
With Bedrock, data and customizations are maintained securely in service-team escrowed accounts. By keeping all of your data and code securely within your own AWS accounts, you have full control over your data and can comply with your organization's security policies and industry regulations.
Amazon DataZone can help catalog, discover, share, and govern data stored across AWS, on premises, and third-party sources so you can have a single data management tool for all your data spreaded over the organization and apply fine grained access controls to it.
Amazon Bedrock brings you Guardrails, a safeguards solution that help you creating your AI policies, filter harmful content, disallow denied topics, redact or block sensitive information such as PII (personally identifiable information) that can be shared with different applications and use cases, including Agents and Knowledge Bases for Amazon Bedrock, reducing business risks related to AI misuse.
Amazon Bedrock offers several advantages over other AI solutions, including:
Migrating to Amazon Bedrock presents a compelling opportunity for organizations seeking innovation, cost-effectiveness, and control over their AI solutions. By leveraging Bedrock's powerful capabilities and seamless AWS integration, businesses can unlock new possibilities, drive operational efficiencies, and deliver exceptional customer experiences.
Is your company planning your journey with generative AI? Are you replacing first-generation solutions or prototypes built on OpenAI? Consider engaging a partner with proven experience to help you get there. At Caylent, we have a full suite of GenAI offerings spanning the entire AI adoption lifecycle.
Starting with our Generative AI Strategy Caylent Catalyst, we can start the ideation process and guide you through all the possibilities that can be impactful for your business. Using these new ideas, our Generative AI Proof of Value Caylent Catalyst can help you build your first production-ready AI solution aligned to business outcomes. To strategically adopt AI at scale, our Innovation Engine offers a continuous innovation framework that accelerates your journey to production across a portfolio of use cases.
As part of these Catalysts, our teams can help you create, implement, and operate your custom roadmap for GenAI. For companies ready to take their GenAI initiatives beyond the scope of Caylent's prebuilt offerings, we can tailor an engagement exactly to your requirements. Get in touch to discuss how we can help.
Caylent Catalysts™
Accelerate your generative AI initiatives with ideation sessions for use case prioritization, foundation model selection, and an assessment of your data landscape and organizational readiness.
Caylent Catalysts™
Accelerate investment and mitigate risk when developing generative AI solutions.
Marco Antonio Peres Barbosa
Mark Olson