Jan 19

Building, automating, managing, and scaling ML workflows using Amazon SageMaker Pipelines

We recently announced Amazon SageMaker Pipelines, the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). SageMaker Pipelines is a native workflow orchestration tool for building ML pipelines that take advantage of direct Amazon SageMaker integration. Three components improve the operational resilience and reproducibility of your ML workflows: pipelines, model […]

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Mar 03

Utilizing XGBoost training reports to improve your models

In 2019, AWS unveiled Amazon SageMaker Debugger, a SageMaker capability that enables you to automatically detect a variety of issues that may arise while a model is being trained. SageMaker Debugger captures model state data at specified intervals during a training job. With this data, SageMaker Debugger can detect training issues or anomalies by leveraging […]

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Mar 03

Integrating Amazon Polly with legacy IVR systems by converting output to WAV format

Amazon Web Services (AWS) offers a rich stack of artificial intelligence (AI) and machine learning (ML) services that help automate several components of the customer service industry. Amazon Polly, an AI generated text-to-speech service, enables you to automate and scale your interactive voice solutions, helping to improve productivity and reduce costs. You might face common […]

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Mar 03

Introducing Amazon SageMaker Reinforcement Learning Components for open-source Kubeflow pipelines

This blog post was co-authored by AWS and Max Kelsen. Max Kelsen is one of Australia’s leading Artificial Intelligence (AI) and Machine Learning (ML) solutions businesses. The company delivers innovation, directly linked to the generation of business value and competitive advantage to customers in Australia and globally, including Fortune 500 companies. Max Kelsen is also […]

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