Data Ingestion Machine Learning ML Engineer 
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.
Marktechpost
AUGUST 8, 2023
In the ever-evolving landscape of machine learning, feature management has emerged as a key pain point for ML Engineers at Airbnb. Airbnb recognized the need for a solution that could streamline feature data management, provide real-time updates, and ensure consistency between training and production environments.
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AWS Machine Learning Blog
SEPTEMBER 18, 2023
Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model. Ingest the prepared data into the feature group by using the Boto3 SDK.
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.
The MLOps Blog
JANUARY 26, 2024
Training and evaluating models is just the first step toward machine-learning success. For this, we have to build an entire machine-learning system around our models that manages their lifecycle, feeds properly prepared data into them, and sends their output to downstream systems. But what is an ML pipeline?
Towards AI
NOVEMBER 8, 2024
Data preparation isn’t just a part of the ML engineering process — it’s the heart of it. Photo by Myriam Jessier on Unsplash To set the stage, let’s examine the nuances between research-phase data and production-phase data. This post dives into key steps for preparing data to build real-world ML systems.
ODSC - Open Data Science
JULY 19, 2023
Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. The data science team is now expected to be equipped with CI/CD skills to sustain ongoing inference with retraining cycles and automated redeployments of models.
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