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What You Should Know before Deploying ML in Production
What should you know before deploying machine learning projects to production? There are four aspects of Machine Learning Operations, or MLOps, that everyone should be aware of first. These can help data scientists and engineers overcome limitations in the machine learning lifecycle and actually see them as opportunities.
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Getting Rid of Wastes and Impediments in Software Development Using Data Science
This article presents how to use data science to detect wastes and impediments, and concepts and related information that help teams to figure out the root cause of impediments they struggle to get rid of. The knowledge discovered during research includes an expanded waste classification, and the use of trends to uncover undesired situations like hidden delayed backlog items and defects trends.
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DevOps is Not Enough for Scaling and Evolving Tech-Driven Organizations: a Q&A with Eduardo da Silva
Eduardo Silva from bol.com on the need for sociotechnical systems thinking. DevOps is a good starting point but a wider view of the organization as a sociotechnical system is key for sustained growth.
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The First Wave of GPT-3 Enabled Applications Offer a Preview of Our AI Future
The first wave of GPT-3 powered applications are emerging. After priming of only a few examples, GPT-3 could write essays, answer questions, and even generate computer code! Furthermore, GPT-3 can perform algebraic calculations and language translations despite never being taught such concepts. However, GPT-3 is a black box with unpredictable outcomes. Developers must use it responsively.
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State of the Art in Automated Machine Learning
InfoQ caught up with experts Francesca Lazzeri, machine learning scientist lead at Microsoft; Matthew Tovbin, co-founder of Faros AI; Adrian de Wynter, applied scientist in Alexa AI’s Secure AI Foundations; Leah McGuire, principal member of technical staff at Salesforce; and Marios Michailidis, data scientist at H2O.ai, about the state of the art in automated machine learning (AutoML).
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What’s Next in DevOps?
The DevOps movement continues to grow and gain influence in the IT world and the business world at large. As the organisations become increasingly digital, the agility of our IT systems becomes critical to the life and health of the companies.
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The Data Science Mindset: Six Principles to Build Healthy Data-Driven Organizations
In this article, business and technical leaders will learn methods to assess whether their organization is data-driven and benchmark its data science maturity. They will learn how to use the Healthy Data Science Organization Framework to nurture a data science mindset within the organization.