Remove DevOps Remove Download Remove ML Engineer
article thumbnail

Supercharge your AI team with Amazon SageMaker Studio: A comprehensive view of Deutsche Bahn’s AI platform transformation

AWS Machine Learning Blog

The AI platform team’s key objective is to ensure seamless access to Workbench services and SageMaker Studio for all Deutsche Bahn teams and projects, with a primary focus on data scientists and ML engineers. Download the source code from the GitHub repo. Bootstrap the AWS account.

article thumbnail

Fine tune a generative AI application for Amazon Bedrock using Amazon SageMaker Pipeline decorators

AWS Machine Learning Blog

As you move from pilot and test phases to deploying generative AI models at scale, you will need to apply DevOps practices to ML workloads. Use Python to preprocess, train, and test an LLM in Amazon Bedrock To begin, we need to download data and prepare an LLM in Amazon Bedrock. We use Python to do this.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning Blog

After the completion of the research phase, the data scientists need to collaborate with ML engineers to create automations for building (ML pipelines) and deploying models into production using CI/CD pipelines. Security SMEs review the architecture based on business security policies and needs.

article thumbnail

MLOps Without Magic

Mlearning.ai

My interpretation to MLOps is similar to my interpretation of DevOps. As a software engineer your role is to write code for a certain cause. DevOps cover all of the rest, like deployment, scheduling of automatic tests on code change, scaling machines to demanding load, cloud permissions, db configuration and much more.

DevOps 52
article thumbnail

Automate fine-tuning of Llama 3.x models with the new visual designer for Amazon SageMaker Pipelines

AWS Machine Learning Blog

Data scientists and machine learning (ML) engineers use pipelines for tasks such as continuous fine-tuning of large language models (LLMs) and scheduled notebook job workflows. Download the pipeline definition as a JSON file to your local environment by choosing Export at the bottom of the visual editor.

article thumbnail

Build an end-to-end MLOps pipeline using Amazon SageMaker Pipelines, GitHub, and GitHub Actions

AWS Machine Learning Blog

ML operations, known as MLOps, focus on streamlining, automating, and monitoring ML models throughout their lifecycle. Data scientists, ML engineers, IT staff, and DevOps teams must work together to operationalize models from research to deployment and maintenance. Download the template.yml file to your computer.

ML 117
article thumbnail

Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

Flipboard

There are also limited options for ad hoc script customization by users, such as data scientists or ML engineers, due to permissions of the user profile execution role. Depending on how many packages are installed and how large they are, the lifecycle script might even timeout.

Python 123