Deploying Large Language Models in a Serverless Environment Challenges and Solutions

Deploying Large Language Models in a Serverless Environment: Challenges and Solutions

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Ah, serverless computing. That magical place where developers can scale infinitely, pay only for what they use, and never have to think about infrastructure again. At least, that’s the sales pitch. In reality, serverless is a fantastic option for lightweight, ephemeral workloads—not for a behemoth like a large language model (LLM) that devours CPU cycles …

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The Future of Coding Is No Coding at All And Why Coders Need to Adapt

The Future of Coding Is No Coding at All (And Why Coders Need to Adapt)

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For decades, high-level coding skills have been the golden ticket to job security in the development space, but that’s changing. Now we have tools to build complex websites and applications that don’t require writing endless lines of code by hand. We have officially entered a world where no-code and low-code platforms like Webflow and Bubble.io …

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Data Drift Detection in AI Systems Implementing Online Monitoring Pipelines

Data Drift Detection in AI Systems: Implementing Online Monitoring Pipelines

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If you’ve ever deployed a machine learning model in a real-world environment, you might have run into a puzzling scenario: the model worked beautifully during testing, but its performance gradually faltered once it went live. You’re not alone—countless data scientists and software engineers have had that “uh-oh” moment. Very often, it turns out the model …

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Streaming Machine Learning Inference with Kafka and TensorFlow Serving

Streaming Machine Learning Inference With Kafka and TensorFlow Serving

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Batch processing had its time in the sun, back when data scientists had the patience of monks and businesses thought waiting an hour for insights was acceptable. But in today’s world, where your fridge knows you’re out of milk before you do, real-time machine learning inference is king. The need for instant insights, whether for …

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Using Reinforcement Learning to Optimize Microservices Scaling

Using Reinforcement Learning to Optimize Microservices Scaling

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Welcome to the bleeding edge of overengineering, where we’ve decided that traditional scaling strategies aren’t nearly painful enough and have opted to sprinkle some reinforcement learning (RL) into our microservices architecture. Why settle for boring CPU thresholds and reactive autoscalers when you can unleash an AI that learns, adapts, and, on occasion, takes your production …

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Implementing Multi-Agent Collaboration with OpenAI’s AutoGPT Framework

Implementing Multi-Agent Collaboration With OpenAI’s AutoGPT Framework

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Picture this: you’ve just gotten one AutoGPT agent up and running. It’s doing moderately useful things, occasionally throwing errors like a toddler throwing tantrums, but hey—it works. But then a wild idea hits: what if instead of one agent, you had several? Coordinating. Collaborating. Delegating. Maybe even arguing. What could possibly go wrong?   Welcome …

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Efficient Vector Search Implementing HNSW with FAISS for Scalable AI Applications

Efficient Vector Search: Implementing HNSW With FAISS for Scalable AI Applications

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Ah, vector search—everyone’s favorite topic at networking events, right? Nothing quite sparks joy like discussing how to efficiently find the nearest neighbor among a few hundred million vectors. If you’ve been blissfully relying on brute-force search, congratulations: you’ve been lighting your compute budget on fire for no good reason.   For those of us living …

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How to Fine-Tune LLaMA 3 on a Custom Dataset Using LoRA

How To Fine-Tune LLaMA 3 on a Custom Dataset Using LoRA

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Fine-tuning large language models is the machine learning equivalent of customizing a sports car—except instead of just swapping out tires and adding a spoiler, you’re tweaking hyperparameters and feeding it mountains of data while praying your GPU doesn’t burst into flames. LLaMA 3 is Meta’s latest masterpiece, and while it’s already an impressive model, sometimes …

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Optimizing Transformer Models for Low-Latency Inference in Production

Optimizing Transformer Models for Low-Latency Inference in Production

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Congratulations! You’ve built a transformer model that can rival GPT-4 in accuracy. There’s just one tiny problem—it takes forever to generate a single response, and your users are starting to reminisce about the glory days of dial-up internet. That’s right. Your cutting-edge AI is slower than a 1998 AOL login.   Transformer models are computationally …

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