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Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning Blog

Furthermore, evaluation processes are important not only for LLMs, but are becoming essential for assessing prompt template quality, input data quality, and ultimately, the entire application stack. Evaluation algorithm Computes evaluation metrics to model outputs.

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DeepSeek in My Engineer’s Eyes

Towards AI

In this post, I want to shift the conversation to how Deepseek is redefining the future of machine learning engineering. It has already inspired me to set new goals for 2025, and I hope it can do the same for other ML engineers. It is fascinating what Deepseek has achieved with their top noche engineering skill.

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Use a data-centric approach to minimize the amount of data required to train Amazon SageMaker models

AWS Machine Learning Blog

As machine learning (ML) models have improved, data scientists, ML engineers and researchers have shifted more of their attention to defining and bettering data quality. Applying these techniques allows ML practitioners to reduce the amount of data required to train an ML model.

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11 Open Source Data Exploration Tools You Need to Know in 2023

ODSC - Open Data Science

Its goal is to help with a quick analysis of target characteristics, training vs testing data, and other such data characterization tasks. Apache Superset GitHub | Website Apache Superset is a must-try project for any ML engineer, data scientist, or data analyst.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Learn more The Best Tools, Libraries, Frameworks and Methodologies that ML Teams Actually Use – Things We Learned from 41 ML Startups [ROUNDUP] Key use cases and/or user journeys Identify the main business problems and the data scientist’s needs that you want to solve with ML, and choose a tool that can handle them effectively.

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Prioritizing employee well-being: An innovative approach with generative AI and Amazon SageMaker Canvas

AWS Machine Learning Blog

In a single visual interface, you can complete each step of a data preparation workflow: data selection, cleansing, exploration, visualization, and processing. Custom Spark commands can also expand the over 300 built-in data transformations. Other analyses are also available to help you visualize and understand your data.

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Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

AWS Machine Learning Blog

Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.