The rise of Big Data I want to take you back to 2012 . In those early years, our focus was big data, and everyone else was looking at the cloud. We were pioneering, but in a world that was still beginning to understand the value of data.
Executive Summary Managed Agent Solutions (MAS) are cloud-managed platforms for building, deploying, and operating AI agents. They remove the need to manually set up infrastructure for orchestration, memory, tooling, tracing, guardrails, and evaluation, allowing teams to reach production faster. MAS have matured from early, UI-focused experiments into platforms that support production workloads across many GenAI use cases. They are not a universal solution. Custom agent solutions are still required when teams need advanced observability, custom evaluation, strict cost control, portability, or complex orchestration.
Executive Summary Choosing an agentic AI framework is an architectural decision, not a tooling choice. While many frameworks offer similar building blocks, they differ significantly in abstraction level, ecosystem coupling, observability, governance, and production readiness.
What does the day in the life of a medical specialist who encounters a patient with an unclear diagnosis look like? Its combing through tens or maybe hundreds of scientific papers to find a gene, cell therapy or something else that may be the key to saving their patient’s life. As you can imagine, this can be a lengthy and time-consuming process. But what if there was a tool this specialist could use to get this information through simple queries, cutting down the amount of time it takes to find the needed information?
One of the projects we are working on involves generating code for a custom dialect of a programming language using a large language model (LLM). With a dataset of instructions and their corresponding implementations, we aim to fine-tune a model to automate this process. Given the rapid advancements in AI, fine-tuning LLMs can significantly enhance their performance for specific tasks, offering tailored solutions that generic models might not provide.
ML6 presents a recommended CI/CD pattern for promoting ML model code across environments using Amazon SageMaker.
Machine learning is a powerful technology with the potential to transform businesses and industries. However, for newcomers, it can be a daunting and time-consuming endeavour. The process of learning the fundamentals, setting up the infrastructure, and understanding the intricacies of ML frameworks can be overwhelming.
A screenshot of our chatbot generating personalized marketing messages in Dutch.
Amazon recently announced their foundation model service: Amazon Bedrock. We got our hands on it as part of a limited preview. So, what’s in it for you?