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How to Build a CI/CD MLOps Pipeline [Case Study]

The MLOps Blog

In the case of our CI/CD-MLOPs system, we stored the model versions and metadata in the data storage services offered by AWS i.e Licensing costs: Oftentimes, we need third-party software libraries to power our solutions. If you aren’t aware already, let’s introduce the concept of ETL. S3 buckets.

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Build an Amazon SageMaker Model Registry approval and promotion workflow with human intervention

AWS Machine Learning Blog

The large machine learning (ML) model development lifecycle requires a scalable model release process similar to that of software development. Model developers often work together in developing ML models and require a robust MLOps platform to work in. ML Engineer at Tiger Analytics.

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Integrate SaaS platforms with Amazon SageMaker to enable ML-powered applications

AWS Machine Learning Blog

The open-source Custom Connector SDK enables the development of a private, shared, or public connector using Python or Java. SaaS platform SDK – If the SaaS platform has an SDK (Software Development Kit), such as a Python SDK, this can be used to access data directly from a SageMaker notebook.

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How SnapLogic built a text-to-pipeline application with Amazon Bedrock to translate business intent into action

Flipboard

This emergent ability in LLMs has compelled software developers to use LLMs as an automation and UX enhancement tool that transforms natural language to a domain-specific language (DSL): system instructions, API requests, code artifacts, and more. We use the following prompt: Human: Your job is to act as an expert on ETL pipelines.

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