A Technology Broker and Management Company Specializing in KM Systems
Favorite Organizations building AI agents face a common challenge: Every request, whether a routine status check or complex analysis, typically gets routed to a single large, expensive model. This one-size-fits-all approach can drive up API costs, introduce unpredictable latency, and remove the possibility of offline operation. Even routine queries that
Read More
Shared by AWS Machine Learning October 22, 2026
Favorite Building a multi-agent system is now pretty straightforward. Taking it to production is where the real challenges begin. Thanks to open source frameworks and cloud services, you can set up an agent in a matter of days. A few tool calls, a few lines of prompt, and you have
Read More Shared by AWS Machine Learning October 22, 2026
Favorite When enterprise data sits behind a corporate firewall or in a private on-premises data center, there’s no direct network path from AWS, even though the AI agents and dashboards in Amazon Quick can query virtually any data source. Administrators must make sure their sensitive enterprise data isn’t exposed to
Read More Shared by AWS Machine Learning October 22, 2026
Favorite Every engineering team knows the ritual, and Rescale was no exception. A nightly test suite fails. An on-call engineer pulls the logs, classifies the root cause, and routes it to the right team, only to repeat the process the next morning. At scale, across distributed services and dozens of repositories,
Read More Shared by AWS Machine Learning October 22, 2026
Favorite A traffic spike hits your self-hosted text-to-speech (TTS) endpoint, and requests start queueing. Latency climbs past your service level objective (SLO) right when the most callers are listening. The auto scaling policy behind that endpoint was supposed to prevent this, but the target value it scales on was a
Read More Shared by AWS Machine Learning October 22, 2026
Favorite A customer asks an AI voice agent a question and waits. The agent already has an answer, it’s midway through generating a long, multi-paragraph response, but the customer hears nothing for five seconds. By the time an answer arrives, the customer is frustrated, confused, and no longer paying attention.
Read More Shared by AWS Machine Learning October 22, 2026
Favorite Healthcare organizations want the same thing from AI that every other business wants: faster answers, less manual work, and insights that reach decision-makers in time to act on them. However, what makes the healthcare industry different is that the data is uniquely complex, deeply contextual, and strictly regulated. So
Read More Shared by AWS Machine Learning October 22, 2026
Favorite Fine-tuning a large language model (LLM) today usually forces a tradeoff between training loop control and operational simplicity. With fully managed services you select a base model, provide data and hyperparameters, and the service owns everything else, which works well when a standard, prepackaged recipe fits your problem. Less
Read More Shared by AWS Machine Learning October 22, 2026
Favorite Companies getting real results from AI aren’t only using foundation models (FMs) off the shelf. They’re customizing open-weight models using post-training techniques such as reinforcement learning. A model trained on your business context, product catalog, or clinical vocabulary will be more accurate for your task and cost less to
Read More Shared by AWS Machine Learning October 22, 2026
Favorite IskayPet’s office teams had a knowledge challenge. Answers to everyday operational questions, such as a return policy, a veterinary protocol, or an onboarding step, lived in hundreds of Microsoft SharePoint documents spread across four languages and three countries. Finding the right information in large document repositories took a long
Read More Shared by AWS Machine Learning October 22, 2026