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Be Warned: You Can’t Rely On Big Data!

Beyond Philosophy

The concept of a customer’s journey is nothing new – we have been offering journey mapping in our customer experience consultancy for years. And linking data points throughout a journey is a step in the right direction. But I have a big problem with Big Data. Have you used Big Data in your business?

Big data 284
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Use Amazon SageMaker pipeline sharing to view or manage pipelines across AWS accounts

AWS Machine Learning

On August 9, 2022, we announced the general availability of cross-account sharing of Amazon SageMaker Pipelines entities. You can now use cross-account support for Amazon SageMaker Pipelines to share pipeline entities across AWS accounts and access shared pipelines directly through Amazon SageMaker API calls. Solution overview.

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Shocking! Yahoo’s data breach

Beyond Philosophy

If you aren’t sure this is true, then ask yourself: would I open a Yahoo email account today? It’s because 500 million of Yahoo’s account users’ names, email addresses, telephone numbers, birth dates, scrambled passwords, and security questions are in the wind. Yahoo’s new email account set up is also quiet today.

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Top Customer Success Courses and Training that every CSM needs in 2022

CustomerSuccessBox

We have curated the best courses and training handpicked for you to select the best out of the best. Creator: Nils Vinje , Founder & CEO, Glide Consulting. Customer Success Training to do in 2022. Cisco Training. Customer Success Courses to pursue in 2022. Customer Success Manager: Fundamentals to your CSM Career.

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Build and train computer vision models to detect car positions in images using Amazon SageMaker and Amazon Rekognition

AWS Machine Learning

Training ML algorithms for pose estimation requires a lot of expertise and custom training data. Therefore, we present two options: one that doesn’t require any ML expertise and uses Amazon Rekognition, and another that uses Amazon SageMaker to train and deploy a custom ML model.

APIs 62
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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

We build a model to predict the severity (benign or malignant) of a mammographic mass lesion trained with the XGBoost algorithm using the publicly available UCI Mammography Mass dataset and deploy it using the MLOps framework. After it’s trained, the model is registered into the central model registry to be approved by a model approver.

Scripts 96
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Accelerate data preparation for ML in Amazon SageMaker Canvas

AWS Machine Learning

The no-code environment of SageMaker Canvas allows us to quickly prepare the data, engineer features, train an ML model, and deploy the model in an end-to-end workflow, without the need for coding. From the Import data page, select Snowflake from the list and choose Add connection.