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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).

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Pushing the Limits of Conversational AI for CX Automation

TechSee

Chatbots are typically rule-based systems that follow predefined scripts to interact with customers. By their nature, chatbots are limited to their programming, and they may struggle with complex requests or conversations that deviate from their script. Is this the right Conversational AI provider for my CX needs?

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Build production-ready generative AI applications for enterprise search using Haystack pipelines and Amazon SageMaker JumpStart with LLMs

AWS Machine Learning

Enterprise search is a critical component of organizational efficiency through document digitization and knowledge management. Enterprise search covers storing documents such as digital files, indexing the documents for search, and providing relevant results based on user queries. script to preprocess and index the provided demo data.

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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

Central model registry – Amazon SageMaker Model Registry is set up in a separate AWS account to track model versions generated across the dev and prod environments. Approve the model in SageMaker Model Registry in the central model registry account. Create a pull request to merge the code into the main branch of the GitHub repository.

Scripts 100
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Use Snowflake as a data source to train ML models with Amazon SageMaker

AWS Machine Learning

After the data is downloaded into the training instance, the custom training script performs data preparation tasks and then trains the ML model using the XGBoost Estimator. Store your Snowflake account credentials in AWS Secrets Manager. Ingest the data in a table in your Snowflake account. amazonaws.com/sagemaker-xgboost:1.5-1

Scripts 106
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Call Center Scripts – Live Agent Scripting

Zingtree

After tons of research, we’ve launched what we believe is the ultimate live agent scripting solution, especially suited for call centers of all sizes. From easy deployment to intelligent pricing packages, Zingtree makes it easy to set up scripts for any type of live support! When you run out of credits, we refill your account.

Scripts 48
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Train and deploy ML models in a multicloud environment using Amazon SageMaker

AWS Machine Learning

Prerequisites You should have the following prerequisites: An AWS account. As part of the setup, we define the following: A session object that provides convenience methods within the context of SageMaker and our own account. Our training script uses this location to download and prepare the training data, and then train the model.

Scripts 100