DevOps Download ML Engineer 
AWS Machine Learning Blog
AUGUST 22, 2024
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.
AWS Machine Learning Blog
SEPTEMBER 1, 2023
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.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
AWS Machine Learning Blog
DECEMBER 13, 2023
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.
AWS Machine Learning Blog
FEBRUARY 29, 2024
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.
AWS Machine Learning Blog
NOVEMBER 9, 2023
Download and save the publicly available UCI Mammography Mass dataset to the S3 bucket you created earlier in the dev account. She is passionate about developing, deploying, and explaining AI/ ML solutions across various domains. Saswata Dash is a DevOps Consultant with AWS Professional Services.
Mlearning.ai
AUGUST 18, 2023
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.
AWS Machine Learning Blog
SEPTEMBER 23, 2024
Throughout this exercise, you use Amazon Q Developer in SageMaker Studio for various stages of the development lifecycle and experience firsthand how this natural language assistant can help even the most experienced data scientists or ML engineers streamline the development process and accelerate time-to-value.
Expert insights. Personalized for you.
106 
Let's personalize your content