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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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State of Machine Learning Survey Results Part Two

ODSC - Open Data Science

First, there’s a need for preparing the data, aka data engineering basics. Machine learning practitioners are often working with data at the beginning and during the full stack of things, so they see a lot of workflow/pipeline development, data wrangling, and data preparation.

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An integrated experience for all your data and AI with Amazon SageMaker Unified Studio (preview)

Flipboard

Organizations are building data-driven applications to guide business decisions, improve agility, and drive innovation. Many of these applications are complex to build because they require collaboration across teams and the integration of data, tools, and services. For Project name , enter a name (for example, demo).

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

AWS Machine Learning Blog

With the integration of SageMaker and Amazon DataZone, it enables collaboration between ML builders and data engineers for building ML use cases. ML builders can request access to data published by data engineers. Additionally, this solution uses Amazon DataZone. intended_uses="Not used except this test.",

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How OLAP and AI can enable better business

IBM Journey to AI blog

Increased operational efficiency benefits Reduced data preparation time : OLAP data preparation capabilities streamline data analysis processes, saving time and resources. IBM watsonx.data is the next generation OLAP system that can help you make the most of your data.

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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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Tackling AI’s data challenges with IBM databases on AWS

IBM Journey to AI blog

  Request a live demo or start a proof of concept with Amazon RDS for Db2 Db2 Warehouse SaaS on AWS The cloud-native Db2 Warehouse fulfills your price and performance objectives for mission-critical operational analytics, business intelligence (BI) and mixed workloads. . Netezza

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