Remove infrastructure-as-code-iac-services
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Accelerate machine learning time to value with Amazon SageMaker JumpStart and PwC’s MLOps accelerator

AWS Machine Learning

Solution overview AWS offers a comprehensive portfolio of cloud-native services for developing and running MLOps pipelines in a scalable and sustainable manner. Additionally, JumpStart provides solution templates designed to tackle common use cases, as well as example Jupyter notebooks with prewritten starter code.

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Driving advanced analytics outcomes at scale using Amazon SageMaker powered PwC’s Machine Learning Ops Accelerator

AWS Machine Learning

PwC Machine Learning Ops Accelerator, built on top of AWS native services, delivers a fit-for-purpose solution that easily integrates into the ML use cases with ease for customers across all industries. Prediction service capability starts the deployed model to provide prediction through online, batch, or streaming patterns.

Analytics 104
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Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

AWS Machine Learning

Since 2014, the company has been offering customers its Philips HealthSuite Platform, which orchestrates dozens of AWS services that healthcare and life sciences companies use to improve patient care. These environments ranged from individual laptops and desktops to diverse on-premises computational clusters and cloud-based infrastructure.

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Configure an AWS DeepRacer environment for training and log analysis using the AWS CDK

AWS Machine Learning

Toolkits to automate the infrastructure become essential for horizontal scaling of AI/ML efforts within a corporation. Solution overview Orchestrating all the necessary services takes a considerable amount of time when it comes to creating a scalable template that can be applied for multiple use cases.

Scripts 73
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Build well-architected IDP solutions with a custom lens – Part 4: Performance efficiency

AWS Machine Learning

These resources introduce common AWS services for IDP workloads and suggested workflows. Frequent throttling – You may experience throttling by AWS services like Amazon Textract due to request limits. With this knowledge, you’re now ready to learn more about productionizing your workload.

APIs 93
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Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning

The full instructions with code are available in the GitHub repository. Code changes merged to the corresponding environment git branch triggers a CI/CD workflow to make appropriate changes to the given target environment. Create a pull request to merge the code into the main branch of the GitHub repository.

Scripts 99
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Boomi uses BYOC on Amazon SageMaker Studio to scale custom Markov chain implementation

AWS Machine Learning

Boomi is an enterprise-level software as a service (SaaS) independent software vendor (ISV) that creates developer enablement tooling for software engineers. These tools integrate via API into Boomi’s core service offering. This post is co-written with Swagata Ashwani, Senior Data Scientist at Boomi.