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7 signs it’s time to modernize your IT systems

CIO

The bad news, however, is that IT system modernization requires significant financial and time investments. There are multiple examples of organizations driving home a first-mover advantage by adopting and embracing technology modernization when the opportunity presents itself early.” Technology continues to advance at a furious pace.

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Agentic AI design: An architectural case study

CIO

There are many areas of research and focus sprouting from the capabilities presented through LLMs. Agentic AI is the next leap forward beyond traditional AI to systems that are capable of handling complex, multi-step activities utilizing components called agents. In 2024, a new trend called agentic AI emerged.

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Scaling Startups: The Ultimate Guide For Founders

Luis Goncalves

While launching a startup is difficult, successfully scaling requires an entirely different skillset, strategy framework, and operational systems. This isn’t merely about hiring more salespeopleit’s about creating scalable systems efficiently converting prospects into customers. What Does Scaling a Startup Really Mean?

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LLM benchmarking: How to find the right AI model

CIO

There are two main approaches: Reference-based metrics: These metrics compare the generated response of a model with an ideal reference text. Reference-free metrics: These metrics evaluate the quality of a generated text independently of a reference. This approach enables new possibilities that go beyond classic metrics.

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There Is Only One Key Difference Between Observability 1.0 and 2.0

Honeycomb

phenomenon We’ve all heard the slogan, “metrics, logs, and traces are the three pillars of observability.” For every request that enters your system, you write logs, increment counters, and maybe trace spans; then you store telemetry in many places. Multiple “pillars” are an observability 1.0 generation. Observability 1.0 is a scalpel.

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Ground truth generation and review best practices for evaluating generative AI question-answering with FMEval

AWS Machine Learning - AI

Ground truth data in AI refers to data that is known to be factual, representing the expected use case outcome for the system being modeled. By providing an expected outcome to measure against, ground truth data unlocks the ability to deterministically evaluate system quality.

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Build generative AI applications quickly with Amazon Bedrock IDE in Amazon SageMaker Unified Studio

AWS Machine Learning - AI

Building generative AI applications presents significant challenges for organizations: they require specialized ML expertise, complex infrastructure management, and careful orchestration of multiple services. The system will take a few minutes to set up your project. On the next screen, leave all settings at their default values.