AWS Machine Learning Blog

DECEMBER 19, 2023

Machine learning operations (MLOps) applies DevOps principles to ML systems. Just like DevOps combines development and operations for software engineering, MLOps combines ML engineering and IT operations. PwC MLOps Accelerator is designed to be agnostic to ML models, ML frameworks, and runtime environments.

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Machine Learning ML Engineer DevOps ML 130

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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Unite.AI

JUNE 24, 2024

It combines principles from DevOps, such as continuous integration, continuous delivery, and continuous monitoring, with the unique challenges of managing machine learning models and datasets. BentoML : BentoML is a Python-first tool for deploying and maintaining machine learning models in production. What is MLOps?

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Data Drift Machine Learning Data Scientist ML 124

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.

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Automation DevOps Machine Learning ML 119

AWS Machine Learning Blog

APRIL 21, 2023

The repository also includes additional Python source code with helper functions, used in the setup notebook, to set up required permissions. model.create() creates a model entity, which will be included in the custom metadata registered for this model version and later used in the second pipeline for batch inference and model monitoring.

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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. The model registry maintains records of model versions, their associated artifacts, lineage, and metadata. Building a robust MLOps pipeline demands cross-functional collaboration.

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Marktechpost

JULY 24, 2023

This data version is frequently recorded into your metadata management solution to ensure that your model training is versioned and repeatable. In addition to supporting batch and streaming data processing, Delta Lake also offers scalable metadata management. Neptune serves as a consolidated metadata store for each MLOps workflow.

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Machine Learning Metadata Data Science DevOps 52

AWS Machine Learning Blog

NOVEMBER 14, 2023

Python 3.10 Experiments plus callback integration Amazon SageMaker Experiments lets you organize, track, compare and evaluate machine learning (ML) experiments and model versions from any integrated development environment (IDE), including local Jupyter Notebooks, using the SageMaker Python SDK or boto3.

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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.

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ML Machine Learning Deep Learning Metadata 69

Viso.ai

MARCH 29, 2024

MLflow can be seen as a tool that fits within the MLOps (synonymous with DevOps) framework. You can use the API using Python, REST, R, and Java. Local Tracking with Database: You can use a local database to manage experiment metadata for a cleaner setup compared to local files.

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Machine Learning ML Automation Data Scientist 52

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.

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Metadata Data Scientist Explainability ML 52

AWS Machine Learning Blog

JULY 8, 2024

Choose your runtime kernel (it’s set to use Python 3 by default). The Details tab displays metadata, logs, and the associated training job. He currently serves media and entertainment customers, and has expertise in software engineering, DevOps, security, and AI/ML. Open the Jupyter notebook named kohya-ss-fine-tuning.ipynb.

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ML Automation DevOps Software Engineer 123

AWS Machine Learning Blog

JUNE 8, 2023

This is your Custom Python Hook speaking!" A session stores metadata and application-specific data known as session attributes. Solutions Architect at Amazon Web Services with specialization in DevOps and Observability. A session persists over time unless manually stopped or timed out. Mahesh Birardar is a Sr.

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The MLOps Blog

AUGUST 3, 2023

What we’re targeting first is helping you replace that procedural Python code with Hamilton code that you describe, which I can go into detail a little bit more. You could almost think of Hamilton as DBT for Python functions. It gives a very opinionary way of writing Python. Piotr: This is procedural Python code.

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ML Data Scientist Software Engineer Machine Learning 52

The MLOps Blog

OCTOBER 3, 2023

I actually did not pick up Python until about a year before I made the transition to a data scientist role. For me, it was a little bit of a longer journey because I kind of had data engineering and cloud engineering and DevOps engineering in between. There’s no component that stores metadata about this feature store?

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ML Data Scientist Machine Learning Data Science 52

The MLOps Blog

MAY 9, 2023

Here, the component will also return statistics and metadata that help you understand if the model suits the target deployment environment. Model deployment You can deploy the packaged and registered model to a staging environment (as traditional software with DevOps) or the production environment. Implementing system governance.

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AWS Machine Learning Blog

JUNE 14, 2023

SageMaker provides a set of templates for organizations that want to quickly get started with ML workflows and DevOps continuous integration and continuous delivery (CI/CD) pipelines. You can also add your own Python scripts and transformations to customize workflows. Python code file. Choose the file browser icon view the path.

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AWS Machine Learning Blog

JULY 16, 2024

Eliuth Triana Isaza is a Developer Relations Manager at NVIDIA empowering Amazon’s AI MLOps, DevOps, Scientists and AWS technical experts to master the NVIDIA computing stack for accelerating and optimizing Generative AI Foundation models spanning from data curation, GPU training, model inference and production deployment on AWS GPU instances.

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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. Collaboration The principles you have learned in this guide are mostly born out of DevOps principles.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

The Amazon SageMaker Pipeline consists of the following steps: data pre-processing, parallel evaluation of multiple FMs, models comparison, and selection based on accuracy and other properties like cost or latency, registration of selected model artifacts, and metadata. The following diagram illustrates this architecture.

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LLM Data Scientist Algorithm Generative AI 110