AI News

AUGUST 29, 2023

The operationalisation of data projects has been a key factor in helping organisations turn a data deluge into a workable digital transformation strategy, and DataOps carries on from where DevOps started. It’s all data driven,” Faruqui explains. And everybody agrees that in production, this should be automated.”

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Data Ingestion Explainability Big Data ETL 165

AWS Machine Learning Blog

FEBRUARY 27, 2024

The model will be approved by designated data scientists to deploy the model for use in production. For production environments, data ingestion and trigger mechanisms are managed via a primary Airflow orchestration. Pavel Maslov is a Senior DevOps and ML engineer in the Analytic Platforms team.

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Machine Learning DevOps Data Scientist Data Quality 134

IBM Journey to AI blog

SEPTEMBER 20, 2023

These include data ingestion, data selection, data pre-processing, FM pre-training, model tuning to one or more downstream tasks, inference serving, and data and AI model governance and lifecycle management—all of which can be described as FMOps.

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Large Language Models DevOps AI Modeling Automation 240

AWS Machine Learning Blog

JUNE 27, 2023

That is where Provectus , an AWS Premier Consulting Partner with competencies in Machine Learning, Data & Analytics, and DevOps, stepped in. They needed a cloud platform and a strategic partner with proven expertise in delivering production-ready AI/ML solutions, to quickly bring EarthSnap to the market.

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DevOps ML Machine Learning ML Engineer 120

AWS Machine Learning Blog

APRIL 26, 2023

Data engineering – Identifies the data sources, sets up data ingestion and pipelines, and prepares data using Data Wrangler. Data science – The heart of ML EBA and focuses on feature engineering, model training, hyperparameter tuning, and model validation.

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ML Machine Learning Data Science Data Drift 116

TensorFlow

JANUARY 18, 2023

The first part is all about the core TFX pipeline handling all the steps from data ingestion to model deployment. We built a simple yet complete ML pipeline with support for automatic data ingestion, data preprocessing, model training, model evaluation, and model deployment in TFX. Hub service.

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Data Ingestion ML DevOps Automation 52

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.

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Metadata Machine Learning Data Scientist Data Quality 134