AWS Machine Learning Blog

OCTOBER 2, 2023

The output of a SageMaker Ground Truth labeling job is a file in JSON-lines format containing the labels and additional metadata. With a passion for automation, Joerg has worked as a software developer, DevOps engineer, and Site Reliability Engineer in his pre-AWS life.

Automation 124

Automation DevOps Machine Learning ML 124

AWS Machine Learning Blog

MARCH 5, 2025

The examples focus on questions on chunk-wise business knowledge while ignoring irrelevant metadata that might be contained in a chunk. He has touched on most aspects of these projects, from infrastructure and DevOps to software development and AI/ML.

Generative AI 78

Generative AI LLM AI AI 78

AWS Machine Learning Blog

DECEMBER 13, 2023

Data scientists, ML engineers, IT staff, and DevOps teams must work together to operationalize models from research to deployment and maintenance. It enables teams to collaborate on software development projects, track changes, and manage code repositories. Building a robust MLOps pipeline demands cross-functional collaboration.

ML 125

ML Automation Metadata Software Development 125

AWS Machine Learning Blog

FEBRUARY 21, 2023

SageMaker deployment guardrails Guardrails are an essential part of software development. Similar to traditional CI/CD systems, we want to automate software tests, integration testing, and production deployments. All of the metadata for these experiments can be tracked using Amazon SageMaker Experiments during development.

ML 96

ML Software Development Automation Metadata 96

AWS Machine Learning Blog

JUNE 11, 2024

Ziwen Ning is a software development engineer at AWS. Anant Sharma is a software engineer at AWS Annapurna Labs specializing in DevOps. His primary focus revolves around building, automating and refining the process of delivering software to AWS Trainium and Inferentia customers.

Deep Learning 126

Deep Learning ML Automation Auto-complete 126

The MLOps Blog

APRIL 17, 2023

Building a tool for managing experiments can help your data scientists; 1 Keep track of experiments across different projects, 2 Save experiment-related metadata, 3 Reproduce and compare results over time, 4 Share results with teammates, 5 Or push experiment outputs to downstream systems.

Metadata 52

Metadata Data Scientist Explainability ML 52

The MLOps Blog

MARCH 21, 2023

To make that possible, your data scientists would need to store enough details about the environment the model was created in and the related metadata so that the model could be recreated with the same or similar outcomes. Version control for code is common in software development, and the problem is mostly solved.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

AWS Machine Learning Blog

JUNE 8, 2023

A session stores metadata and application-specific data known as session attributes. Victor Rojo is a highly experienced technologist who is passionate about the latest in AI, ML, and software development. Solutions Architect at Amazon Web Services with specialization in DevOps and Observability. Mahesh Birardar is a Sr.

Generative AI 97

Generative AI LLM Machine Learning Large Language Models 97

The MLOps Blog

APRIL 5, 2023

Source Model packaging is a process that involves packaging model artifacts, dependencies, configuration files, and metadata into a single format for effortless distribution, installation, and reuse. These teams may include but are not limited to data scientists, software developers, machine learning engineers, and DevOps engineers.

ML 69

ML Machine Learning Deep Learning Metadata 69

Viso.ai

MARCH 29, 2024

MLflow is an open-source platform designed to manage the entire machine learning lifecycle, making it easier for ML Engineers, Data Scientists, Software Developers, and everyone involved in the process. MLflow can be seen as a tool that fits within the MLOps (synonymous with DevOps) framework.

Machine Learning 52

Machine Learning ML Automation Data Scientist 52

AWS Machine Learning Blog

JANUARY 9, 2025

Fine-tuning process and human validation The fine-tuning and validation process consisted of the following steps: Gathering a malware dataset To cover the breadth of malware techniques, families, and threat types, we collected a large dataset of malware samples, each with technical metadata.

Deep Learning 116

Deep Learning Neural Network Explainability AI 116