   ​ Active learning aims to optimize the training process of an ML model using minimum but important and impactful labeled samples by actively selecting them from a large pool of unlabeled data and querying a human annotator to label them.​ ​Photo by Scott Graham / Unsplash Introduction and Background Creating an efficient machine learning model …
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   ​ An interview with Ljubomir Buturovic, VP of ML at Inflammatix, about using machine learning for diagnosing infections and blood tests in the ER. Exploring challenges in diagnostic and prognostic models and offering insights on building datasets for production.​ ​ In this episode, Dean chats with Ljubomir Buturovic, VP of ML and Informatics at Inflammatix. …
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   ​ Examine how you can improve the overall accuracy of your machine learning models so that they perform well and make reliable predictions.​ ​ In 2018, there were extensive news reports that an Uber self-driving car made an accident with a pedestrian in Tempe, Arizona. The pedestrian died, and investigators found that there was an …
   ​ Discover the essential key metrics to consider when evaluating data annotation tools, and learn about the best tools available.​ ​Source: Author Introduction Just like having a massive pile of books won’t make you a genius unless you read and understand them, a mountain of data won’t make a powerful AI if it’s not properly …
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   ​ Learn about the most popular tools to validate data. We picked these tools due to their adoption by the biggest companies, and their versatile features.​ ​[[{“value”:”Image generated with Midjourney It was challenging to stop myself from starting this article with some variation of the popular phrase “garbage in, garbage out.” Well, I did it …
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   ​ A chat with Idan Gazit, Senior Director of Research at GitHub Next, where we discuss his work at GitHub Next, the future of AI products and interfaces, and how to get AI products to production​ ​ In this episode, Idan Gazit, Senior Director of Research at GitHub Next, discusses his role in exploring strategic …
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   ​ Get a solid understanding of the benefits of LLM fine-tuning, common tricks, and techniques for fine-tuning LLMs.​ ​ LLMs exist to be fine-tuned. Fine-tuning is the process that transforms a large language model from a generic predictive resource to one that is fully differentiated with respect to an organization’s unique datasets. To make an …
   ​ Explore ML model testing and tools specifically designed for testing ML models, which will significantly enhance your ML pipeline.​ ​[[{“value”:” Machine learning, particularly its subsets, deep learning, and generative ML, is currently in the spotlight. This surge in interest from both businesses and the general public has been fueled by the excitement around latest …
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   ​ MACSO, a leader in AI for edge computing, credits DagsHub for revolutionizing ML operations, enhancing collaboration, and efficiency.​ ​ Intro In the rapidly evolving field of machine learning (ML) and artificial intelligence (AI), startups like MACSO are at the forefront of innovation, pushing the boundaries of what’s possible with AI on edge devices. A …
   ​ Understand common pitfalls, and create strategies to avoid them, and develop robust, scalable, and efficient database infrastructure.​ ​ Introduction The use of vector databases within modern applications is steadily increasing, especially in the era of generative AI. With the increased adoption of vector databases, it becomes even more important for IT leaders and decision-makers …
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