Building Production-Ready AI Pipelines: Lessons from Running 10K+ Generations

Ali Nemati1 day ago29 sec read18 views

Starting from scratch, it's recommended to use managed APIs for language models due to operational costs associated with self-hosting. Prioritize error handling over observability by distinguishing between retryable and non-retryable errors. Implement a dead letter queue early on to handle responses that don't fit the expected format, ensuring failures are not silently accepted. Logging a random sample of prompts and responses (1%) separately aids in identifying issues before they become significant problems.

Read the full article at DEV Community

Want to create content about this topic? Use Nemati AI tools to generate articles, social posts, and more.

Comments

The LLM Speed Hack Nobody Is Talking About

The article discusses recent advancements in Large Language Model (LLM) technology that significantly improve output speed without compromising accura...The article discusses recent advancements in Large Language Model (LLM) technology that significantly improve output speed without compromising accuracy. These innovations include speculative decoding, TIDE for continuous draft model adaptation, hier...

Ali Nemati

AI & Machine LearningFeb 2725 sec read

InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models

InnerQ is a new hardware-aware quantization method for large language model KV caches that reduces decode latency by up to 22% without sacrificing acc...InnerQ is a new hardware-aware quantization method for large language model KV caches that reduces decode latency by up to 22% without sacrificing accuracy; it matters because it enables more efficient long-sequence generation and memory usage. Conte...

Ali Nemati

AI & Machine LearningFeb 2527 sec read

Diffusion Generative Recommendation with Continuous Tokens

Researchers introduced ContRec, a new framework that integrates continuous tokens into large language model-based recommendation systems to improve us...Researchers introduced ContRec, a new framework that integrates continuous tokens into large language model-based recommendation systems to improve user preference capture and item retrieval accuracy. This approach avoids the limitations of discrete ...

Ali Nemati

AI & Machine LearningFeb 2325 sec read

SPQ: An Ensemble Technique for Large Language Model Compression

Researchers introduced SPQ, an ensemble technique for compressing large language models that combines SVD, pruning, and quantization to reduce memory ...Researchers introduced SPQ, an ensemble technique for compressing large language models that combines SVD, pruning, and quantization to reduce memory usage by up to 75% while maintaining or improving model performance. This method is particularly ben...

Ali Nemati

AI & Machine LearningFeb 2221 sec read

We Asked AI to Be Predictable and It Laughed At Us

The article discusses how integrating large language models into systems reveals unpredictability in AI outputs, challenging traditional assumptions o...The article discusses how integrating large language models into systems reveals unpredictability in AI outputs, challenging traditional assumptions of determinism in software engineering. Content creators must focus on output-level observability and...

Ali Nemati

Building Production-Ready AI Pipelines: Lessons from Running 10K+ Generations

Related Articles

The LLM Speed Hack Nobody Is Talking About

InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models

Diffusion Generative Recommendation with Continuous Tokens

SPQ: An Ensemble Technique for Large Language Model Compression

We Asked AI to Be Predictable and It Laughed At Us