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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.

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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

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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Learnings From Building the ML Platform at Stitch Fix

The MLOps Blog

This is Piotr Niedźwiedź and Aurimas Griciūnas from neptune.ai , and you’re listening to ML Platform Podcast. Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. We thought, “how can we lower the software engineering bar?”

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Deliver your first ML use case in 8–12 weeks

AWS Machine Learning Blog

You may have gaps in skills and technologies, including operationalizing ML solutions, implementing ML services, and managing ML projects for rapid iterations. Ensuring data quality, governance, and security may slow down or stall ML projects. He has a background in software engineering and AI research.

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Deploying Conversational AI Products to Production With Jason Flaks

The MLOps Blog

You have a bit of education in music composition, math, and science before you get more into the software engineering side of things. But you have started out in software design engineering, is that correct? How do you ensure data quality when building NLP products? Data quality is critical.