Evaluating the performance of large language models (LLMs) goes beyond statistical metrics like perplexity or bilingual evaluation understudy (BLEU) scores. For most real-world generative…
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Managing ModelOps workflows can be complex and time-consuming. If you’ve struggled with setting up project templates for your data science team, you know that…
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Building AI applications with Amazon Bedrock presents throughput challenges impacting the scalability of your applications. Globalcross-Region inferencein the af-south-1 AWS Region changes that. You…
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Maia 200 is Microsoft’s new in house AI accelerator designed for inference in Azure datacenters. It targets the cost of token generation for large…
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Enterprises are managing ever-growing volumes of content, ranging from product catalogs and support articles to knowledge bases and technical documentation. Ensuring this information remains…
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Most AI applications still showcase the model as a chat box. That interface is simple, but it hides what agents are actually doing, such…
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Tencent Hunyuan has open sourced HPC-Ops, a production grade operator library for large language model inference architecture devices. HPC-Ops focuses on low level CUDA…
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This post was co-written with Saurabh Gupta and Todd Colbyfrom Pushpay. Pushpayis a market-leading digital giving and engagement platform designed to help churches and…
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Organizations managing hundreds of contracts annually face significant inefficiencies, with fragmented systems and complex workflows that require teams to spend hours on contract review…
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@tool def sql_investigate(query: str) -> dict: try: df = con.execute(query).df() head = df.head(30) return { "rows": int(len(df)), "columns": list(df.columns), "preview": head.to_dict(orient="records") } except Exception…
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