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Enhancing Domain-Driven Design Through Collaborative Systems Thinking

Xebia

This article delves into enriching the collaboration topic by emerging the contextual systems and the need to find the purpose underneath the context. But What Are Systems? Despite this text starting with the software scope, systems here are related to the context we, as humans, are connected to.

System 147
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Patterns of Distributed Systems is published by Pearson

Martin Fowler

During the last four years, my colleague Unmesh Joshi been developing a collection of patterns to help us all better understand how modern distributed systems work. We've been publishing drafts of these patterns on this site. Now these have turned into a book, published by Addison-Wesley in my signature series.

System 197
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A Detailed Guide on Conducting Effective System Design Interviews

Hacker Earth Developers Blog

System design interviews are becoming increasingly popular, and important, as the digital systems we work with become more complex. The term ‘system’ here refers to any set of interdependent modules that work together for a common purpose. Uber, Instagram, and Twitter (now X) are all examples of ‘systems’.

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Patterns of Distributed Systems

Martin Fowler

Over the last few months, my colleague Unmesh Joshi has been running workshops to teach developers about distributed systems design. In this work he's been delving into many open-source distributed systems and identifying patterns. As he writes them up, he's publishing the patterns on my site.

System 361
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Humility in AI: Building Trustworthy and Ethical AI Systems

But with the incredible pace of the modern world, AI systems continually face new data patterns, which make it challenging to return reliable predictions. This could mean a catastrophic failure by the system down the line, especially without proper guardrails.

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Patterns of Distributed Systems

Martin Fowler

Over the last few months, my colleague Unmesh Joshi has been running workshops to teach developers about distributed systems design. In this work he's been delving into many open-source distributed systems and identifying patterns. As he writes them up, he's publishing the patterns on my site.

System 349
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How ML System Design helps us to make better ML products

Xebia

With the industry moving towards end-to-end ML teams to enable them to implement MLOPs practices, it is paramount to look past the model and view the entire system around your machine learning model. The classic article on Hidden Technical Debt in Machine Learning Systems explains how small the model is compared to the system it operates in.

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Innovation Systems: Advancing Practices to Create New Value

As technology transforms the global business landscape, companies need to examine and update their internal processes for innovation to keep pace. Ultimately, organizations will have to improve the velocity of innovation by creating repeatable processes that support ideation, exploration, and incubation, essential to capturing an idea’s full value.

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Trusted AI 102: A Guide to Building Fair and Unbiased AI Systems

The risk of bias in artificial intelligence (AI) has been the source of much concern and debate. Numerous high-profile examples demonstrate the reality that AI is not a default “neutral” technology and can come to reflect or exacerbate bias encoded in human data.

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10 Rules to More Streamlined Data Modeling

Learn 10 rules that will help you perfect your Kafka system to get ahead. Apache Kafka is a powerful piece of software that can solve a lot of problems. Like most libraries and frameworks, you get out of it what you put into it.

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Resilient Machine Learning with MLOps

But we can take the right actions to prevent failure and ensure that AI systems perform to predictably high standards, meet business needs, unlock additional resources for financial sustainability, and reflect the real patterns observed in the outside world.

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LLMs in Production: Tooling, Process, and Team Structure

Speaker: Dr. Greg Loughnane and Chris Alexiuk

However, during development – and even more so once deployed to production – best practices for operating and improving generative AI applications are less understood.

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Build Trustworthy AI With MLOps

How MLOps helps bridge the production gap between systems and teams. AI operations, including compliance, security, and governance. AI ethics, including privacy, bias and fairness, and explainability.

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10 Rules for Managing Apache Kafka

Learn ten rules that will help you perfect your Kafka system to get ahead. Kafka is a powerful piece of software that can solve a lot of problems. Like most libraries and frameworks, you get out of it what you put into it.

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The Pro’s Guide To Getting The Best ROI From Your New LMS

You need a Learning Management System when your courses and training programs need to be accessible online. Quickly build the perfect business case and easily determine which LMS will provide the best return on investment you need with this how-to eBook!