Remove Data Lakes Remove Deep Learning Remove SQL
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Generate financial industry-specific insights using generative AI and in-context fine-tuning

AWS Machine Learning Blog

NOTE : Since we used an SQL query engine to query the dataset for this demonstration, the prompts and generated outputs mention SQL below. The question in the preceding example doesn’t require a lot of complex analysis on the data returned from the ETF dataset. A user can ask a business- or industry-related question for ETFs.

SQL 113
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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

Many teams are turning to Athena to enable interactive querying and analyze their data in the respective data stores without creating multiple data copies. Athena allows applications to use standard SQL to query massive amounts of data on an S3 data lake. Create a data lake with Lake Formation.

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40 Must-Know Data Science Skills and Frameworks for 2023

ODSC - Open Data Science

Computer Science and Computer Engineering Similar to knowing statistics and math, a data scientist should know the fundamentals of computer science as well. While knowing Python, R, and SQL are expected, you’ll need to go beyond that. This will lead to algorithm development for any machine or deep learning processes.

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

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Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. You can use query_string to filter your dataset by SQL and unload it to Amazon S3.

ML 123
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Azure Machine Learning – Empowering Your Data Science Journey

How to Learn Machine Learning

Advanced Capabilities and Use Cases of Azure Machine Learning Handling Different Data Types Azure Machine Learning excels at working with various data types: Structured Data : Traditional tabular data can be processed using AutoML or custom models with frameworks like scikit-learn or XGBoost.

Azure 52
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Harmonize data using AWS Glue and AWS Lake Formation FindMatches ML to build a customer 360 view

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Companies are faced with the daunting task of ingesting all this data, cleansing it, and using it to provide outstanding customer experience. Typically, companies ingest data from multiple sources into their data lake to derive valuable insights from the data.

AWS 123
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Top Data Analytics Skills and Platforms for 2023

ODSC - Open Data Science

Data analysts often must go out and find their data, process it, clean it, and get it ready for analysis. This pushes into Big Data as well, as many companies now have significant amounts of data and large data lakes that need analyzing. Cloud Services: Google Cloud Platform, AWS, Azure.