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Customizing sk-learn Models and Pipelines

Towards AI

One reason for rephrasing a regression problem into a classification problem could be that the user wants to focus on a specific price range and requires a model that can predict this range with high accuracy. Demo In this section, I show how the pricing pipeline is initialized, trained, and used to predict price categories.

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Snorkel Flow Spring 2023: warm starts and foundation models

Snorkel AI

If you want to see Snorkel Flow in action, sign up for a demo. Autosuggest labeling function improvements We’ve improved the Autosuggest feature for sequence tagging and added new suggestion strategies based on embeddings and TF-IDF keyword count for the text classification task type. Autosuggest labeling functions enhancements.

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Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

Flipboard

Deploy the CloudFormation template Complete the following steps to deploy the CloudFormation template: Save the CloudFormation template sm-redshift-demo-vpc-cfn-v1.yaml Enter a stack name, such as Demo-Redshift. You should see a new CloudFormation stack with the name Demo-Redshift being created. yaml locally.

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Modern NLP: A Detailed Overview. Part 3: BERT

Towards AI

Architecture: The authors have used a two-layered Bidirectional LSTM to demo the concept. In the NLP world, there are usually two types of models or tasks broadly, auto-regressive models and auto-encoding models. It has been observed that the bi-directionality of the model, i.e,

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Prioritizing employee well-being: An innovative approach with generative AI and Amazon SageMaker Canvas

AWS Machine Learning Blog

For Problem type , select Classification. In the following example, we drop the columns Timestamp, Country, state, and comments, because these features will have least impact for classification of our model. For Training method , select Auto. This instance configuration is sufficient for the demo. Choose Create.

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How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency

AWS Machine Learning Blog

Then we needed to Dockerize the application, write a deployment YAML file, deploy the gRPC server to our Kubernetes cluster, and make sure it’s reliable and auto scalable. In our case, we chose to use a float[] as the input type and the built-in DJL classifications as the output type.

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Snorkel Flow Summer 2023: faster, easier and more secure

Snorkel AI

classification, information extraction) using programmatic labeling, fine-tuning, and distillation. This is especially helpful for classification across many classes, where users tend to write more LFs. Intelligent Auto-Suggest Strategies for Labeling Functions You can now target specific error hotspots using slice-based suggestions.