Data Ingestion and DevOps - Artificial Intelligence Zone

Data Ingestion

DevOps

Basil Faruqui, BMC: Why DataOps needs orchestration to make it work

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.”

Data Ingestion

Data Ingestion Explainability Big Data ETL

How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

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.

Machine Learning

Machine Learning DevOps Data Quality Data Scientist

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Webinars

From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

Understanding User Needs and Satisfying Them

How To Get Promoted In Product Management

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Trending Sources

Splunk Tutorial For Beginners: It’s Application & Features

Pickl AI

JUNE 29, 2023

Furthermore, The platform’s versatility extends beyond data analysis. The pricing structure is based on the volume of data ingested, which can add up quickly for large-scale deployments. Resource Requirements Splunk’s data processing and indexing can consume significant system resources.

Big Data

Big Data DevOps Data Analysis Machine Learning

Webinars

From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

Understanding User Needs and Satisfying Them

How To Get Promoted In Product Management

MORE WEBINARS

Boost employee productivity with automated meeting summaries using Amazon Transcribe, Amazon SageMaker, and LLMs from Hugging Face

AWS Machine Learning Blog

MAY 7, 2024

The service allows for simple audio data ingestion, easy-to-read transcript creation, and accuracy improvement through custom vocabularies. Mateusz Zaremba is a DevOps Architect at AWS Professional Services. Amazon Transcribe’s new ASR foundation model supports 100+ language variants.

Automation

Automation Auto-complete DevOps UX Design

Foundational models at the edge

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.

Large Language Models

Large Language Models DevOps AI Modeling Automation

How Earth.com and Provectus implemented their MLOps Infrastructure with Amazon SageMaker

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.

DevOps

DevOps ML Machine Learning Automation

Introducing the Amazon Comprehend flywheel for MLOps

AWS Machine Learning Blog

MARCH 1, 2023

MLOps focuses on the intersection of data science and data engineering in combination with existing DevOps practices to streamline model delivery across the ML development lifecycle. An Amazon Comprehend flywheel automates this ML process, from data ingestion to deploying the model in production.

Data Ingestion

Data Ingestion DevOps Automation ML

End-to-End Pipeline for Segmentation with TFX, Google Cloud, and Hugging Face

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.

Data Ingestion

Data Ingestion ML DevOps Automation

Deliver your first ML use case in 8–12 weeks

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.

ML Machine Learning Data Science Data Drift

Machine Learning Operations (MLOPs) with Azure Machine Learning

ODSC - Open Data Science

JULY 19, 2023

Personas associated with this phase may be primarily Infrastructure Team but may also include all of Data Engineers, Machine Learning Engineers, and Data Scientists. Model Development (Inner Loop): The inner loop element consists of your iterative data science workflow.

Machine Learning

Machine Learning Data Drift Data Science ML Engineer

MLOps Landscape in 2023: Top Tools and Platforms

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.

Machine Learning

Machine Learning Metadata Data Quality Data Scientist

How to Build an End-To-End ML Pipeline

The MLOps Blog

MAY 9, 2023

The components comprise implementations of the manual workflow process you engage in for automatable steps, including: Data ingestion (extraction and versioning). Data validation (writing tests to check for data quality). Data preprocessing. Let’s briefly go over each of the components below. CSV, Parquet, etc.)

ML Machine Learning Metadata Automation

Definite Guide to Building a Machine Learning Platform

The MLOps Blog

MARCH 21, 2023

” — Isaac Vidas , Shopify’s ML Platform Lead, at Ray Summit 2022 Monitoring Monitoring is an essential DevOps practice, and MLOps should be no different. Collaboration The principles you have learned in this guide are mostly born out of DevOps principles. My Story DevOps Engineers Who they are?

Machine Learning

Machine Learning Data Scientist ML Metadata

Basil Faruqui, BMC: Why DataOps needs orchestration to make it work

How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

Webinars

Trending Sources

Splunk Tutorial For Beginners: It’s Application & Features

Webinars

Boost employee productivity with automated meeting summaries using Amazon Transcribe, Amazon SageMaker, and LLMs from Hugging Face

Foundational models at the edge

How Earth.com and Provectus implemented their MLOps Infrastructure with Amazon SageMaker

Introducing the Amazon Comprehend flywheel for MLOps

End-to-End Pipeline for Segmentation with TFX, Google Cloud, and Hugging Face

Deliver your first ML use case in 8–12 weeks

Machine Learning Operations (MLOPs) with Azure Machine Learning

MLOps Landscape in 2023: Top Tools and Platforms

How to Build an End-To-End ML Pipeline

Definite Guide to Building a Machine Learning Platform

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