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Real value, real time: Production AI with Amazon SageMaker and Tecton

AWS Machine Learning Blog

Businesses are under pressure to show return on investment (ROI) from AI use cases, whether predictive machine learning (ML) or generative AI. Only 54% of ML prototypes make it to production, and only 5% of generative AI use cases make it to production. Using SageMaker, you can build, train and deploy ML models.

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Enhance your Amazon Redshift cloud data warehouse with easier, simpler, and faster machine learning using Amazon SageMaker Canvas

AWS Machine Learning Blog

Machine learning (ML) helps organizations to increase revenue, drive business growth, and reduce costs by optimizing core business functions such as supply and demand forecasting, customer churn prediction, credit risk scoring, pricing, predicting late shipments, and many others. Let’s learn about the services we will use to make this happen.

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Edge Impulse Launches “Bring Your Own Model” for ML Engineers

Towards AI

Last Updated on April 4, 2023 by Editorial Team Introducing a Python SDK that allows enterprises to effortlessly optimize their ML models for edge devices. With their groundbreaking web-based Studio platform, engineers have been able to collect data, develop and tune ML models, and deploy them to devices.

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Pioneering computer vision: Aleksandr Timashov, ML developer

Dataconomy

Aleksandr Timashov is an ML Engineer with over a decade of experience in AI and Machine Learning. In this interview, Aleksandr shares his unique experiences of leading groundbreaking projects in Computer Vision and Data Science at the Petronas global energy group (Malaysia). Did the pandemic and isolation complicate your work?

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The 2021 Executive Guide To Data Science and AI

Applied Data Science

Automation Automating data pipelines and models ➡️ 6. Big Ideas What to look out for in 2022 1. Team Building the right data science team is complex. With a range of role types available, how do you find the perfect balance of Data Scientists , Data Engineers and Data Analysts to include in your team?

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AWS Machine Learning: A Beginner’s Guide

How to Learn Machine Learning

You can easily: Store and process data using S3 and RedShift Create data pipelines with AWS Glue Deploy models through API Gateway Monitor performance with CloudWatch Manage access control with IAM This integrated ecosystem makes it easier to build end-to-end machine learning solutions.

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Streamlining Process Configuration in Machine Learning with Hydra

Pickl AI

It enhances scalability, experimentation, and reproducibility, allowing ML teams to focus on innovation. billion in 2022, is expected to soar to USD 505.42 This blog highlights the importance of organised, flexible configurations in ML workflows and introduces Hydra. As the global Machine Learning market, valued at USD 35.80