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Recapping the Cloud Amplifier and Snowflake Demo

Towards AI

Recapping the Cloud Amplifier and Snowflake Demo The combined power of Snowflake and Domo’s Cloud Amplifier is the best-kept secret in data management right now — and we’re reaching new heights every day. If you missed our demo, we dive into the technical intricacies of architecting it below.

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No-code data preparation for time series forecasting using Amazon SageMaker Canvas

AWS Machine Learning Blog

In this post, we explore how SageMaker Canvas and SageMaker Data Wrangler provide no-code data preparation techniques that empower users of all backgrounds to prepare data and build time series forecasting models in a single interface with confidence. Select the consumer_electronics.csv file from the prerequisites.

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Improve governance of models with Amazon SageMaker unified Model Cards and Model Registry

AWS Machine Learning Blog

Several activities are performed in this phase, such as creating the model, data preparation, model training, evaluation, and model registration. Model lineage tracking captures and retains information about the stages of an ML workflow, from data preparation and training to model registration and deployment.

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Deploying Gen AI in Production with NVIDIA NIM & MLRun

Iguazio

Then, we show how to use NVIDIA NIM with MLRun to productize gen AI applications at scale and reduce risks , including a demo of a multi-agent banking chatbot. A gen AI factory allows developers and users to quickly demo, build, deploy, and scale new gen Al applications, accessible through a portal. What is a Gen AI Factory?

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Enhance your Amazon Redshift cloud data warehouse with easier, simpler, and faster machine learning using Amazon SageMaker Canvas

AWS Machine Learning Blog

Conventional ML development cycles take weeks to many months and requires sparse data science understanding and ML development skills. Business analysts’ ideas to use ML models often sit in prolonged backlogs because of data engineering and data science team’s bandwidth and data preparation activities.

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Fine-tune large language models with Amazon SageMaker Autopilot

Flipboard

We use Amazon SageMaker Pipelines , which helps automate the different steps, including data preparation, fine-tuning, and creating the model. We demonstrated an end-to-end solution that uses SageMaker Pipelines to orchestrate the steps of data preparation, model training, evaluation, and deployment.

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End-to-End model training and deployment with Amazon SageMaker Unified Studio

Flipboard

Organizations need a unified, streamlined approach that simplifies the entire process from data preparation to model deployment. To address these challenges, AWS has expanded Amazon SageMaker with a comprehensive set of data, analytics, and generative AI capabilities. For Project name , enter a name (for example, demo ).

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