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
NOVEMBER 20, 2024
In the future, high automation will play a crucial role in this domain. Using generative AI allows businesses to improve accuracy and efficiency in email management and automation. The combination of retrieval augmented generation (RAG) and knowledge bases enhances automated response accuracy.
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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.
AWS Machine Learning Blog
NOVEMBER 7, 2024
Regardless of the models used, they all include data preprocessing, training, and inference over several billions of records containing weekly data spanning multiple years and markets to produce forecasts. A fully automated production workflow The MLOps lifecycle starts with ingesting the training data in the S3 buckets.
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.
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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