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Driving advanced analytics outcomes at scale using Amazon SageMaker powered PwC’s Machine Learning Ops Accelerator

AWS Machine Learning Blog

Artificial intelligence (AI) and machine learning (ML) are becoming an integral part of systems and processes, enabling decisions in real time, thereby driving top and bottom-line improvements across organizations. However, putting an ML model into production at scale is challenging and requires a set of best practices.

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Set up Amazon SageMaker Studio with Jupyter Lab 3 using the AWS CDK

AWS Machine Learning Blog

This development approach can be used in combination with other common software engineering best practices such as automated code deployments, tests, and CI/CD pipelines. cdk.json – Contains metadata, and feature flags. Cory Hairston is a Software Engineer at the Amazon ML Solutions Lab. AWS CDK scripts.

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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning Blog

Let’s demystify this using the following personas and a real-world analogy: Data and ML engineers (owners and producers) – They lay the groundwork by feeding data into the feature store Data scientists (consumers) – They extract and utilize this data to craft their models Data engineers serve as architects sketching the initial blueprint.

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First ODSC Europe 2023 Sessions Announced

ODSC - Open Data Science

ML Governance: A Lean Approach Ryan Dawson | Principal Data Engineer | Thoughtworks Meissane Chami | Senior ML Engineer | Thoughtworks During this session, you’ll discuss the day-to-day realities of ML Governance. Scaling AI/ML Workloads with Ray Kai Fricke | Senior Software Engineer | Anyscale Inc.

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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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MLOps Is an Extension of DevOps. Not a Fork — My Thoughts on THE MLOPS Paper as an MLOps Startup CEO

The MLOps Blog

Just so you know where I am coming from: I have a heavy software development background (15+ years in software). Came to ML from software. Founded two successful software services companies. Founded neptune.ai , a modular MLOps component for ML metadata store , aka “experiment tracker + model registry”.

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Build a crop segmentation machine learning model with Planet data and Amazon SageMaker geospatial capabilities

AWS Machine Learning Blog

This guest post is co-written by Lydia Lihui Zhang, Business Development Specialist, and Mansi Shah, Software Engineer/Data Scientist, at Planet Labs. Planet and AWS’s partnership on geospatial ML SageMaker geospatial capabilities empower data scientists and ML engineers to build, train, and deploy models using geospatial data.