Data Platform ML ML Engineer 
AWS Machine Learning Blog
OCTOBER 20, 2023
Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries.
NOVEMBER 24, 2023
With that, the need for data scientists and machine learning (ML) engineers has grown significantly. Data scientists and ML engineers require capable tooling and sufficient compute for their work. Data scientists and ML engineers require capable tooling and sufficient compute for their work.
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AWS Machine Learning Blog
APRIL 8, 2025
With the support of AWS, iFood has developed a robust machine learning (ML) inference infrastructure, using services such as Amazon SageMaker to efficiently create and deploy ML models. In this post, we show how iFood uses SageMaker to revolutionize its ML operations.
AWS Machine Learning Blog
FEBRUARY 21, 2025
Data exploration and model development were conducted using well-known machine learning (ML) tools such as Jupyter or Apache Zeppelin notebooks. Apache Hive was used to provide a tabular interface to data stored in HDFS, and to integrate with Apache Spark SQL. This created a challenge for data scientists to become productive.
Unite.AI
JUNE 28, 2023
It became apparent to both Razi and me that we had the opportunity to make a significant impact by radically simplifying the feature engineering process and providing data scientists and ML engineers with the right tools and user experience for seamless feature experimentation and feature serving.
AWS Machine Learning Blog
FEBRUARY 27, 2024
In this post, we share how Axfood, a large Swedish food retailer, improved operations and scalability of their existing artificial intelligence (AI) and machine learning (ML) operations by prototyping in close collaboration with AWS experts and using Amazon SageMaker. This is a guest post written by Axfood AB.
AWS Machine Learning Blog
AUGUST 4, 2023
As a result, businesses can accelerate time to market while maintaining data integrity and security, and reduce the operational burden of moving data from one location to another. With Einstein Studio, a gateway to AI tools on the data platform, admins and data scientists can effortlessly create models with a few clicks or using code.
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