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. And everybody agrees that in production, this should be automated.” It’s all data driven,” Faruqui explains.
AWS Machine Learning Blog
FEBRUARY 27, 2024
Automation of building new projects based on the template is streamlined through AWS Service Catalog , where a portfolio is created, serving as an abstraction for multiple products. The model will be approved by designated data scientists to deploy the model for use in production.
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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.
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.
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. All steps are run in an automated manner after the pipeline has been run. This step produces an expanded report containing the model’s metrics.
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.
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.
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