9/17/2023 0 Comments Neo4j collectGiskard allows for quality assurance for AI models, allowing designers to detect bias and other negative facets of models, which aligns with our encouragement to tread ethical waters carefully when developing solutions with AI. Deepchecks allows for the intersection of continuous integration and model validation, an important step in incorporating good engineering practices in analytics settings. For example, Soda Core, a data quality tool, allows the validation of data as it arrives in the system and automated monitoring checks for anomalies. The corresponding tooling ecosystem has continued to grow and mature. By integrating these practices, businesses become better positioned to leverage AI and machine learning and forge responsible, data-driven solutions that cater to a diverse user base. Model validation and quality assurance are crucial in tackling biases and ensuring ethical ML systems with equitable outcomes. Test-driven transformations, data sanity tests and data model testing strengthen the data pipelines that power analytical systems. We've long viewed "building in quality" as a vital aspect of developing reliable analytics and machine learning models. Like all technology solutions, serverless has suitable applications but many of its features include trade-offs that become more acute as the solution evolves. If you need a tool to manage code sharing and independent deployment across a collection of serverless functions, then perhaps it’s time to rethink the suitability of the approach. For example, tools that facilitate sharing code between Lambdas or orchestrate complex interactions might solve a common simple problem but are then at risk of recreating some terrible architecture antipatterns with new building blocks. We also see more tools that appear to solve problems but are prone to wide misuse. While we see many successful applications of serverless-style solutions, we also hear many cautionary tales from our projects, such as the Lambda pinball antipattern. However, like many useful things, solutions sometimes start suitably simple but then, from relentless gradual success, keep evolving until they reach beyond the limitations inherent in the paradigm and sink into the sand under their own weight. Serverless functions - AWS Lambdas - increasingly appear in the toolboxes of architects and developers, and are used for a wide variety of useful tasks that realize the benefits of cloud-based infrastructure. Make sure it's accurate and appropriate before using it." Even some product demos caution users, "AI-generated content can contain mistakes. If these precautions are ignored, the results can lead to reputational and security risks to organizations and users. But the generated content always needs to be monitored by a human who can validate, moderate and use it responsibly. Right now, the AI models are capable of generating a good first draft. However, we caution against over- or inappropriate uses. Similar to how spreadsheets allowed accountants to stop using adding machines to recalculate complex spreadsheets by hand, the next generation of AI will take on chores to relieve technology workers, including developers, by replacing tedious tasks that require knowledge (but not wisdom). Several blips in this edition of the Radar touch on practical uses for AI for projects beyond suggesting code that requires tweaking: AI-aided test-first development, using AI to help build analysis models, and many more. However, over the last few months, tools like ChatGPT have completely reoriented everyone to what’s possible and made the tools widely available. Artificial intelligence has been quietly bubbling away in specialized areas for decades, and tools like GitHub Copilot have been around (and gradually seeing adoption) for a few years. No, this theme text wasn't written by ChatGPT.
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