InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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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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How to Get Hired as a Machine Learning Engineer
To become a machine learning engineer, you have to interview. You have to gain relevant skills from books, courses, conferences, and projects. Include technologies, frameworks, and projects on your CV. In an interview, expect that you will be asked technical questions, insight questions, and programming questions. When given a technical task, demonstrate your skills as if you already had the job.
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Data Leadership Book Review and Interview
Data Leadership book, authored by Anthony Algmin, covers the data leadership topic and how data leaders should manage and govern the data management programs in their organizations. Data Leadership is how organizations choose to apply their energy and resources toward creating data capabilities to influence their business.
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Innovation Startups Modeling Agile Culture
Innovation is not only about the most advanced technology; management and processes are the new era of startups' innovation. To mix the power of the data and the importance of people to offer business intelligence is a key point nowadays. The result is not only the most important thing; the way you do it is more important. To be agile is to adapt to today's market.
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Applied Probability - Counting Large Set of Unstructured Events with Theta Sketches
In this article, author Ronen Cohen discusses the solution to processing the event data using Theta Sketches and technologies like HBase and Kafka.
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TornadoVM: Accelerating Java with GPUs and FPGAs
The proliferation of heterogeneous hardware represents a problem for programming languages such as Java that target CPUs. TornadoVM extends the Graal JIT compiler to take advantage of GPUs & FPGAs and provides a flexible, high-level model whilst still enabling high performance and features such as live task migration.
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Machine Learning in Java with Amazon Deep Java Library
In this article, we demonstrate how Java developers can use the JSR-381 VisRec API to implement image classification or object detection with DJL’s pre-trained models in less than 10 lines of code.
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Q&A on the Book Hands-On Genetic Algorithms with Python
Hands-On Genetic Algorithms with Python by Eyal Wirsansky is a new book which explores the world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models. InfoQ interviewed Eyal Wirsansky about how genetic algorithms work and what they can be used for.
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The Road to Artificial Intelligence: a Tale of Two Advertising Approaches
Artificial Intelligence startups received a record $26.6bn in funding last year, yet a litany of stakeholders continue to demonstrate a lack understanding and education around the discipline. It is critical that entrepreneurs, investors, regulators, and consumers all remain vigilant in properly assessing advertising claims as relates to powerful, constantly-evolving technology.
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Edge Computing and Flow Evolution
Edge computing echoes science from the field of complex adaptive systems that explains scaling patterns. Understand this science to make better decisions about what to run "on the edge."
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What Do AI and Test Automation Have in Common?
These days AI is a big buzzword. While it rises in popularity, the controversy surrounding it flourishes as well. We will demystify AI, and see how it is already embedded in our everyday life, and then you are going to learn about how we (the folks at Testim.io) utilised this kind of groundbreaking technology to bring test automation to the next level.
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Reinforcement Machine Learning for Effective Clinical Trials
In this article, author Dattaraj Jagdish Rao explores the reinforcement machine learning technique called Multi-armed Bandits and discusses how it can be applied to areas like website design and clinical trials.