Best of MLOps — November 2024

1. 1
   Article

   SwirlAI·1y

   What is AI Engineering?

   AI Engineering is a rapidly evolving role focused on developing and deploying AI systems that utilize Large Language Models (LLMs) to solve business problems. AI Engineers differ from Software Engineers and Machine Learning Engineers in that they deal extensively with non-deterministic systems and require skills in prompt engineering, infrastructure, and data integration. The field is witnessing the rise of Agentic systems, which are advanced AI systems capable of performing complex tasks with a degree of autonomy. AI Engineering is poised to become one of the most in-demand roles in the tech industry with high salaries and growing opportunities.

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   2
2. 2
   Article

   Machine Learning Mastery·2y

   Building a Robust Machine Learning Pipeline: Best Practices and Common Pitfalls

   A machine learning pipeline is essential for operating models and delivering value. For robustness, it's crucial to structure the pipeline well and maintain reliability at each stage, even with changing environments. Some key pitfalls to avoid include ignoring data quality, overcomplicating models, inadequate monitoring, and not versioning data and models. Best practices involve using appropriate model evaluation metrics, employing MLOps for deployment and monitoring, and preparing comprehensive documentation.

   36
3. 3
   Article

   TensorFlow·1y

   MLSysBook.AI: Principles and Practices of Machine Learning Systems Engineering

   Machine learning (ML) systems engineering is crucial for transforming sophisticated models into robust, scalable, and efficient systems. MLSysBook.ai fills the educational gap by providing practical insights and resources on ML infrastructure, optimization, deployment, and maintenance, with examples tied to the TensorFlow ecosystem. An interactive learning assistant, SocratiQ, enhances this resource by offering personalized guidance. Understanding both ML modeling and system engineering is key to creating impactful AI solutions.

   22
4. 4
   Article

   Towards Data Science·2y

   Running Large Language Models Privately

   Running large language models (LLMs) locally can enhance privacy and reduce dependency on external providers. Smaller models can be run on standard laptops while more powerful models need advanced hardware. The post discusses various frameworks like llama.cpp and Ollama for local deployment, focusing on speed, power consumption, and performance across different quantization levels. The conclusion highlights the cost-effectiveness and customization options of running LLMs privately compared to cloud-based solutions.

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