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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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10 Technical Blogs for Data Scientists to Advance AI/ML Skills

DataRobot Blog

With a goal to help data science teams learn about the application of AI and ML, DataRobot shares helpful, educational blogs based on work with the world’s most strategic companies. Explore these 10 popular blogs that help data scientists drive better data decisions. Read the blog. Read the blog.

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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.

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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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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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How to Build ETL Data Pipeline in ML

The MLOps Blog

From data processing to quick insights, robust pipelines are a must for any ML system. Often the Data Team, comprising Data and ML Engineers , needs to build this infrastructure, and this experience can be painful. However, efficient use of ETL pipelines in ML can help make their life much easier.

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Harness the power of AI and ML using Splunk and Amazon SageMaker Canvas

AWS Machine Learning Blog

Instead, organizations are increasingly looking to take advantage of transformative technologies like machine learning (ML) and artificial intelligence (AI) to deliver innovative products, improve outcomes, and gain operational efficiencies at scale. Data is presented to the personas that need access using a unified interface.

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