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The ability to Interpret image data using software is advancing fast! The two images above are captioned with program generated text. Here's an article that describes concepts and an approach to generate a caption for an image. The code is written in Python and uses TensorFlow.
How to build and train an image caption generator using a TensorFlo...
"TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well." Here's a TensorFlow tutorial.
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Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.
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Grab updated its internal platform to monitor Apache Kafka data quality in real time. The system uses FlinkSQL and an LLM to detect syntactic and semantic errors. It currently tracks 100+ topics, preventing invalid data from reaching downstream users. This proactive strategy aligns with industry trends to treat data streams as reliable products.
By Patrick Farry
Serving Large Language Models (LLMs) at scale is complex. Modern LLMs now exceed the memory and compute capacity of a single GPU or even a single multi-GPU node. As a result, inference workloads for 70B+, 120B+ parameter models, or pipelines with large context windows, require multi-node, distributed GPU deployments.
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Karrot replaced its legacy recommendation system with a scalable architecture that leverages various AWS services. The company sought to address challenges related to tight coupling, limited scalability, and poor reliability in its previous solution, opting instead for a distributed, event-driven architecture built on top of scalable cloud services.
By Rafal Gancarz
Sharing your work as a software engineer inspires others, invites feedback, and fosters personal growth, Suhail Patel said at QCon London. Normalizing and owning incidents builds trust, and it supports understanding the complexities. AI enables automation but needs proper guidance, context, and security guardrails.
By Ben LindersThe article shares goals and strategies for scaling cloud and distributed applications, focusing on lessons learned from cloud migration at Chase.com at JP Morgan Chase. The discussion centers on three primary goals and the strategies addressing the goals, concluding how these approaches were achieved in practice. For those managing large-scale systems, these lessons provide valuable guidance!
By Durai Arasan
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