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 112

Machine Learning DevOps Data Quality Data Scientist 112

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 225

Large Language Models DevOps AI Modeling Automation 225

AWS Machine Learning Blog

JUNE 27, 2023

This blog post is co-written with Marat Adayev and Dmitrii Evstiukhin from Provectus. That is where Provectus , an AWS Premier Consulting Partner with competencies in Machine Learning, Data & Analytics, and DevOps, stepped in.

DevOps 94

DevOps ML Machine Learning Automation 94

TensorFlow

JANUARY 18, 2023

In this blog post, we discuss the crucial details of building an end-to-end ML pipeline for Semantic Segmentation tasks with TFX and various Google Cloud services such as Dataflow, Vertex Pipelines, Vertex Training, and Vertex Endpoint. TFX Pipeline The ML pipeline is written entirely in TFX, from data ingestion to model deployment.

Data Ingestion 52

Data Ingestion ML DevOps Automation 52

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 88

ML Machine Learning Data Science Data Drift 88

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 134

Machine Learning Metadata Data Quality Data Scientist 134

Pickl AI

MAY 15, 2024

Dreaming of a Data Science career but started as an Analyst? This guide unlocks the path from Data Analyst to Data Scientist Architect. But the allure of tackling large-scale projects, building robust models for complex problems, and orchestrating data pipelines might be pushing you to transition into Data Science architecture.

Data Scientist 52

Data Scientist Data Science Machine Learning Data Quality 52