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Build a multilingual automatic translation pipeline with Amazon Translate Active Custom Translation

AWS Machine Learning

With a background in AI/ML, data science, and analytics, Yunfei helps customers adopt AWS services to deliver business results. He designs AI/ML and data analytics solutions that overcome complex technical challenges and drive strategic objectives. About the authors Yunfei Bai is a Senior Solutions Architect at AWS.

APIs 74
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How VirtuSwap accelerates their pandas-based trading simulations with an Amazon SageMaker Studio custom container and AWS GPU instances

AWS Machine Learning

The challenge The VirtuSwap Minerva engine creates recommendations for optimal distribution of liquidity between different liquidity pools, while taking into account multiple parameters, such as trading volumes, current market liquidity, and volatilities of traded assets, constrained by a total amount of liquidity available for distribution.

APIs 117
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Large-scale feature engineering with sensitive data protection using AWS Glue interactive sessions and Amazon SageMaker Studio

AWS Machine Learning

To achieve that, AWS offers a unified modern data platform that is powered by Amazon Simple Storage Service (Amazon S3) as the data lake with purpose-built tools and processing engines to support analytics and ML workloads. To complete this tutorial, you must have the following prerequisites: Have an AWS account. Prerequisites.

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Brevo (Formely Sendinblue): Features and Services

JivoChat

Brevo Overview In 2012, Sendinblue was launched as a newsletter service, along the years the company has expanded its service considerably, which led to the name change, as well, Brevo. Use the analytics tools to track how each email performs, and check out key performance indicators (KPIs), like open, click-through, and bounce rates.

APIs 75
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Fireside Chat: Natero Shares Tips for Customer Success

Satrix Solutions

Natero helps Customer Success Managers reduce churn, increase expansion, and manage more accounts. Read our interview: Evan Klein: How has the Customer Success industry changed since Natero was first founded in 2012? For example, high-value accounts can command more face time and personal attention. Customer data silos.

SaaS 60
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Apply fine-grained data access controls with AWS Lake Formation and Amazon EMR from Amazon SageMaker Studio

AWS Machine Learning

Before you get started, make sure you have the following prerequisites: An AWS account. Upload both files to an S3 bucket in your account and region. Follow the instructions provided in the Lake Formation guide here , and choose ‘Amazon EMR’ for Session tag values , and enter your AWS account ID under AWS account IDs.

Scripts 78
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The Competitive Dynamics Of Workforce Optimization--A Critical Driver Of Customer Experience--Unpacked

Ian Jacobs

Dimension Data reports that 83% of companies view the contact center as a competitive differentiator, up 30% since 2012. Additionally, in particularly hot areas such as speech, text, and desktop analytics, customer service pros see the ability to not just improve their own team's performance, but also drive broader business transformation.