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Some Thoughts on Operationalizing LLM Applications | by Matthew Harris | Jan, 2024

A few personal lessons learned from developing LLM applications Source DALL·E 3 prompted with “Operationalizing LLMs, watercolor”It’s been fun posting articles exploring new Large Language Model (LLM) techniques and libraries as they emerge, but most of the time has been spent behind the scenes working on the operationalization of LLM solutions. Many organizations are working…

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Exploring Public Storage Traces. What are they, where are they, and are… | by Raluca Diaconu | Jan, 2024

What are they, where are they, and are they right for you? Photo by Hongwei FAN on UnsplashInput and output (I/O) operations refer to the transfer of data between a computer’s main memory and various peripherals. Storage peripherals such as HDDs and SSDs have particular performance characteristics in terms of latency, throughput, and rate which…

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Introduction to Causal Inference with Machine Learning in Python | by Marco Peixeiro | Jan, 2024

Discover the concepts and basic methods of causal machine learning applied in Python Photo by David Clode on UnsplashCausal inference has many tangible applications in a wide variety of scenarios, but in my experience, it is a subject that is rarely talked about among data scientists. In this article, we define causal inference and motivate…

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5 Steps to Transform Messy Functions into Production-Ready Code | by Khuyen Tran | Jan, 2024

Image by AuthorFunctions are essential in a data science project because they make the code more modular, reusable, readable, and testable. However, writing a messy function that tries to do too much can introduce maintenance hurdles and diminish the code’s readability. In the following code, the function impute_missing_values is long, messy, and tries to do…

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No Code GenAI Agents Workflow Orchestration: AutoGen Studio with Local Mistral AI model | by Han HELOIR, Ph.D. ☕️ | Jan, 2024

Introduction to AutoGen and Mistral AI: AutoGen is a framework developed by Microsoft and designed to simplify the development of multi-agent applications, particularly in orchestrating LLM agents. Multi-agent applications involve systems where multiple LLM or multi-modal agents or entities interact with each other in the whole workflow to achieve specific goals or tasks. These agents…

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