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Build a Serverless News Data Pipeline using ML on AWS Cloud

KDnuggets

This is the guide on how to build a serverless data pipeline on AWS with a Machine Learning model deployed as a Sagemaker endpoint.

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Build a Serverless News Data Pipeline using ML on AWS Cloud

KDnuggets

This is the guide on how to build a serverless data pipeline on AWS with a Machine Learning model deployed as a Sagemaker endpoint.

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Lakehouse Monitoring: A Unified Solution for Quality of Data and AI

databricks

Introduction Databricks Lakehouse Monitoring allows you to monitor all your data pipelines – from data to features to ML models – without additional too.

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Enhanced observability for AWS Trainium and AWS Inferentia with Datadog

AWS Machine Learning Blog

With the increasing use of large models, requiring a large number of accelerated compute instances, observability plays a critical role in ML operations, empowering you to improve performance, diagnose and fix failures, and optimize resource utilization. Anjali Thatte is a Product Manager at Datadog.

AWS 110
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Five Important Trends in Big Data Analytics

Flipboard

Over the last few years, with the rapid growth of data, pipeline, AI/ML, and analytics, DataOps has become a noteworthy piece of day-to-day business New-age technologies are almost entirely running the world today. Among these technologies, big data has gained significant traction. This concept is …

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Streaming Data Pipelines: What Are They and How to Build One

Precisely

The concept of streaming data was born of necessity. More than ever, advanced analytics, ML, and AI are providing the foundation for innovation, efficiency, and profitability. But insights derived from day-old data don’t cut it. Business success is based on how we use continuously changing data.

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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.

ML 101