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Vijay Gabale

Co-founder

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Vijay Gabale is the Co-founder and Chief Technology and Product Officer at Infilect Inc., a pioneering force in global retail visual intelligence. Leveraging cutting-edge Image Recognition and AI technologies, Infilect aims to revolutionize the retail landscape by addressing CPG brands' complex challenges in real-time.

Vijay Gabale earned his PhD from IIT Bombay, specializing in wireless platforms and AI systems. He has held critical roles in prominent global technology firms such as IBM Research, distinguishing himself as both a respected technocrat and a forward-thinking leader. Among his notable accomplishments are pioneering demonstrations of the world's inaugural voice network on Zigbee in the USA, groundbreaking contributions to Deep Neural Network architecture for multi-object detection, and his keynote address at the Heidelberg Laureate Forum in Germany, advocating for the societal advantages of AI. With over 6 patents granted and 15 research publications, he continues to drive innovation at the forefront of technology.

With a wealth of experience in product development and partnerships, complemented by deep expertise in Image Recognition Technology and Artificial Intelligence, Vijay Gabale spearheads transformative innovation for CPG brands, retailers, and global distributors and merchandising firms on a large scale.

Generative image recognition leverages generative AI to analyze images, automatically creating descriptive labels and extracting comprehensive information. This technology can interpret and understand visual content, generating insights and answering queries about the image. By combining advanced machine learning techniques with image analysis, it enhances the accuracy and depth of image recognition, making it possible to identify objects, lucid details about the object, and categories, and even infer context within the visual data. This deeper understanding allows for more nuanced insights and more accurate answers to questions about the image.

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

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