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Through the Looking Glass: Exploring the Wonderland of Testing AI Systems

Xebia

Artificial Intelligence (AI) systems are becoming ubiquitous: from self-driving cars to risk assessments to large language models (LLMs). As we depend more on these systems, testing should be a top priority during deployment. When a new system version is ready, the tests ensure it still functions correctly.

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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. Demand forecasting is chosen because it’s a very tangible problem and very suitable application for machine learning.

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Why you should care about debugging machine learning models

O'Reilly Media - Data

For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. Model debugging is an emergent discipline focused on finding and fixing problems in ML systems. We’ll review methods for debugging below. Not least is the broadening realization that ML models can fail.

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Bobidi launches to reward developers for testing companies’ AI models

TechCrunch

In the rush to build, test and deploy AI systems, businesses often lack the resources and time to fully validate their systems and ensure they’re bug-free. In a 2018 report , Gartner predicted that 85% of AI projects will deliver erroneous outcomes due to bias in data, algorithms or the teams responsible for managing them.

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Ethics of generative AI: To be innovative, you must first be trustworthy

CIO

Safeguards need to be in place when testing such powerful new tools.” They should also implement verification systems that help detect and stop the spread of fake content and misinformation generated by AI. This means integrating privacy features into the GenAI system from the outset rather than as an afterthought.

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12 data science certifications that will pay off

CIO

The exam tests general knowledge of the platform and applies to multiple roles, including administrator, developer, data analyst, data engineer, data scientist, and system architect. The exam consists of 60 questions and the candidate has 90 minutes to complete it.

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Leveraging Standardization and Automation to Facilitate DevOps Testing in Multi-Code Environments

CIO

To remain resilient to change and deliver innovative experiences and offerings fast, organizations have introduced DevOps testing into their infrastructures. However, introducing DevOps to mainframe infrastructure can be nearly impossible for companies that do not adequately standardize and automate testing processes before implementation.