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Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

Flipboard

Amazon Redshift is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. You can use query_string to filter your dataset by SQL and unload it to Amazon S3. If you’re familiar with SageMaker and writing Spark code, option B could be your choice.

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How Thomson Reuters delivers personalized content subscription plans at scale using Amazon Personalize

AWS Machine Learning Blog

The rules in this engine were predefined and written in SQL, which aside from posing a challenge to manage, also struggled to cope with the proliferation of data from TR’s various integrated data source. TR customer data is changing at a faster rate than the business rules can evolve to reflect changing customer needs.

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Building an efficient MLOps platform with OSS tools on Amazon ECS with AWS Fargate

AWS Machine Learning Blog

Zeta’s AI innovations over the past few years span 30 pending and issued patents, primarily related to the application of deep learning and generative AI to marketing technology. Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly. He holds a Ph.D.

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Data science vs. machine learning: What’s the difference?

IBM Journey to AI blog

Data from various sources, collected in different forms, require data entry and compilation. That can be made easier today with virtual data warehouses that have a centralized platform where data from different sources can be stored. One challenge in applying data science is to identify pertinent business issues.

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The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

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Use Amazon SageMaker Canvas to build machine learning models using Parquet data from Amazon Athena and AWS Lake Formation

AWS Machine Learning Blog

One of the most common formats for storing large amounts of data is Apache Parquet due to its compact and highly efficient format. This means that business analysts who want to extract insights from the large volumes of data in their data warehouse must frequently use data stored in Parquet. Choose Join data.

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How to Build Machine Learning Systems With a Feature Store

The MLOps Blog

Most of them were built by people who took my free online serverless machine learning course or my Scalable Machine Learning and Deep Learning course at KTH Royal Institute of Technology in Stockholm. Some ML systems use deep learning, while others utilize more classical models like decision trees or XGBoost.