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Stas Tushinskiy, CEO & Co-Founder of Instreamatic – Interview Series

Unite.AI

Originally, Instreamatic was born from a vision to transform how audio publishers monetize their content. Our CI/CD pipelines are optimized for machine learning workflows. This led to the idea of an AI-driven marketing platform, which would revolutionize how we interact with ads.

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

AWS Machine Learning Blog

Although the requirements of continuous integration and continuous delivery (CI/CD) pipelines can be unique and reflect each organization’s needs, scaling MLOps practices across teams can be simplified by using managed orchestrations and tools that can accelerate the development process and remove the undifferentiated heavy lifting.

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MLflow: Simplifying Machine Learning Experimentation

Viso.ai

Moreover, maintaining CI/CD (continuous integration and continuous delivery) is even more challenging. Can have tags for tracking attributes (e.g., Example: “task” tag for identifying question-answering models. Personalized Learning Platform : A company tailors educational content for individual students.

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Build an end-to-end MLOps pipeline for visual quality inspection at the edge – Part 3

AWS Machine Learning Blog

The sample use case used for this series is a visual quality inspection solution that can detect defects on metal tags, which you can deploy as part of a manufacturing process. In a production setting, you can decide to automatically notify another system that takes care of removing faulty metal tags from the production line.

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The most valuable AI use cases for business

IBM Journey to AI blog

YouTube will deliver a curated feed of content suited to customer interests. Creative AI use cases Create with generative AI Generative AI tools such as ChatGPT, Bard and DeepAI rely on limited memory AI capabilities to predict the next word, phrase or visual element within the content it’s generating.

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Automate Amazon SageMaker Pipelines DAG creation

AWS Machine Learning Blog

This enables data scientists to quickly build and iterate on ML models, and empowers ML engineers to run through continuous integration and continuous delivery (CI/CD) ML pipelines faster, decreasing time to production for models. During operationalization, these environment variables should be set by the CI pipeline.

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Organizing ML Monorepo With Pants

The MLOps Blog

Finally, we’ll see how to harness the power of the Pants build system to organize your machine learning monorepo into a robust CI/CD build system. This is relative to the build file’s location, that is: even if we had Python files outside of the mnist/src directory, these sources only capture the contents of the mnist/src folder.

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