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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 MLengineers. Download the source code from the GitHub repo. Bootstrap the AWS account.
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.
After the completion of the research phase, the data scientists need to collaborate with MLengineers 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.
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.
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.
ML operations, known as MLOps, focus on streamlining, automating, and monitoring ML models throughout their lifecycle. Data scientists, MLengineers, 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.
There are also limited options for ad hoc script customization by users, such as data scientists or MLengineers, 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.
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 MLengineers streamline the development process and accelerate time-to-value.
Collaborative workflows : Dataset storage and versioning tools should support collaborative workflows, allowing multiple users to access and contribute to datasets simultaneously, ensuring efficient collaboration among MLengineers, data scientists, and other stakeholders.
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.
Amazon SageMaker is a fully managed service to prepare data and build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows. SageMaker Projects helps organizations set up and standardize environments for automating different steps involved in an ML lifecycle.
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