Automation Data Ingestion Software Engineer 
ODSC - Open Data Science
OCTOBER 7, 2024
One of the key challenges in AI development is building scalable pipelines that can handle the complexities of modern data systems and models. These challenges range from managing large datasets to automating model deployment and monitoring for performance drift. As datasets grow, scalable data ingestion and storage become critical.
AWS Machine Learning Blog
JUNE 18, 2024
The platform, although functional, deals with CSV and JSON files containing hundreds of thousands of rows from various manufacturers, demanding substantial effort for data ingestion. The objective is to automate data integration from various sensor manufacturers for Accra, Ghana, paving the way for scalability across West Africa.
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AWS Machine Learning Blog
NOVEMBER 26, 2023
Amazon Personalize has helped us achieve high levels of automation in content customization. You follow the same process of data ingestion, training, and creating a batch inference job as in the previous use case. Rishabh Agrawal is a Senior Software Engineer working on AI services at AWS.
AWS Machine Learning Blog
APRIL 26, 2023
This includes AWS Identity and Access Management (IAM) or single sign-on (SSO) access, security guardrails, Amazon SageMaker Studio provisioning, automated stop/start to save costs, and Amazon Simple Storage Service (Amazon S3) set up. MLOps engineering – Focuses on automating the DevOps pipelines for operationalizing the ML use case.
The MLOps Blog
JUNE 27, 2023
Core features of end-to-end MLOps platforms End-to-end MLOps platforms combine a wide range of essential capabilities and tools, which should include: Data management and preprocessing : Provide capabilities for data ingestion, storage, and preprocessing, allowing you to efficiently manage and prepare data for training and evaluation.
The MLOps Blog
FEBRUARY 13, 2025
In addition, automated processes allow you to set up monitoring workflows once and reuse them for similar experiments. The solution lies in systems that can handle high-throughput data ingestion while providing accurate, real-time insights. GPU memory leaks, network latency) or software bugs (e.g., Tools like neptune.ai
ODSC - Open Data Science
JULY 19, 2023
A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team. For the customer, this helps them reduce the time it takes to bootstrap a new data science project and get it to production.
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