AWS Machine Learning - AI
NOVEMBER 6, 2024
Traditionally, building frontend and backend applications has required knowledge of web development frameworks and infrastructure management, which can be daunting for those with expertise primarily in data science and machine learning. For more information on how to manage model access, see Access Amazon Bedrock foundation models.
Martin Fowler
MAY 21, 2024
When building a server-side rendered web application, it's valuable to test the HTML that's generated through templates. While these can be tested through end-to-end tests running in the browser, such tests are slow and more work to maintain than unit tests.
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Martin Fowler
FEBRUARY 7, 2023
A perennial problem has been mixing non-UI logic into the UI framework itself, leading to code that's both hard to understand and near-impossible to test. In this first part he gives an overview of how a React application can evolve into a better modular structure. My colleague Juntao Qiu writes about how to untangle such a mess.
AWS Machine Learning - AI
APRIL 9, 2025
Organizations are increasingly using multiple large language models (LLMs) when building generative AI applications. This strategy results in more robust, versatile, and efficient applications that better serve diverse user needs and business objectives. In this post, we provide an overview of common multi-LLM applications.
Speaker: Anindo Banerjea, CTO at Civio & Tony Karrer, CTO at Aggregage
When developing a Gen AI application, one of the most significant challenges is improving accuracy. 💥 Anindo Banerjea is here to showcase his significant experience building AI/ML SaaS applications as he walks us through the current problems his company, Civio, is solving. .
Martin Fowler
FEBRUARY 13, 2024
Here David Tan and Jessie Wang reflect on how regular engineering practices such as testing and refactoring helped them deliver a prototype LLM application rapidly and reliably. LLM engineering involves much more than just prompt design or prompt engineering.
CIO
APRIL 17, 2025
ChatGPT ChatGPT, by OpenAI, is a chatbot application built on top of a generative pre-trained transformer (GPT) model. Launched in 2022, its the most-used gen AI tool in the enterprise, with 62% of respondents to the recent Wharton survey saying they currently use it and 28% saying they dont currently use it but are evaluating or testing it.
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Think your customers will pay more for data visualizations in your application? Discover which features will differentiate your application and maximize the ROI of your embedded analytics. Five years ago they may have. But today, dashboards and visualizations have become table stakes. Brought to you by Logi Analytics.
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Codebases evolve rapidly, and your test automation should keep pace. However, many teams struggle with brittle scripts and growing test debt, which hinder delivery and increase risk. This guide illustrates how engineering leaders leverage AI to enhance the resilience and scalability of testing.
Speaker: J.B. Siegel, VP of Client Services, Seamgen
He’ll discuss how user testing allows you to really understand your users - and how to use the insights to inform your product strategy. In this webinar, you'll learn: How to define your MVP application. The right tools for successful user testing. The benefits of user acceptance testing.
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In a cloud native world, applications are created from loosely coupled microservices instead of being a monolithic entity. Microservices are small, autonomous components, organized around business domains, that are easily monitored, tested, and updated, bringing greater business and operational agility.
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Embedding analytics in your application doesn’t have to be a one-step undertaking. In fact, rolling out features gradually is beneficial because it allows you to progressively improve your application. You can get new capabilities out the door quickly, test them with customers, and constantly innovate.
Speaker: Prem Chandrasekaran
In his best-selling book Patterns of Enterprise Application Architecture, Martin Fowler famously coined the first law of distributed computing—"Don’t distribute your objects"—implying that working with this style of architecture can be challenging. Focusing on the right amount and kinds of tests in your pipelines.
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