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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. He holds a PhD in Telecommunications Engineering and has experience in software engineering.

LLM 95
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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning Blog

Model governance involves overseeing the development, deployment, and maintenance of ML models to help ensure that they meet business objectives and are accurate, fair, and compliant with regulations. He is focused on AI/ML technology, ML model management, ML governance, and MLOps to improve overall organizational efficiency and productivity.

ML 89
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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

Data quality control: Robust dataset labeling and annotation tools incorporate quality control mechanisms such as inter-annotator agreement analysis, review workflows, and data validation checks to ensure the accuracy and reliability of annotations. Data monitoring tools help monitor the quality of the data.

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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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Must-Have Skills for a Machine Learning Engineer

Pickl AI

Fundamental Programming Skills Strong programming skills are essential for success in ML. This section will highlight the critical programming languages and concepts ML engineers should master, including Python, R , and C++, and an understanding of data structures and algorithms. during the forecast period.

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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.