Remove 2012 Remove Analytics Remove Big data
article thumbnail

Use LangChain with PySpark to process documents at massive scale with Amazon SageMaker Studio and Amazon EMR Serverless

AWS Machine Learning

Harnessing the power of big data has become increasingly critical for businesses looking to gain a competitive edge. However, managing the complex infrastructure required for big data workloads has traditionally been a significant challenge, often requiring specialized expertise. elasticmapreduce", "arn:aws:s3:::*.elasticmapreduce/*"

Big data 125
article thumbnail

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. Create IAM users called data-engineer and data-scientist under the IAM group data-platform-group.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Achieve enterprise-grade monitoring for your Amazon SageMaker models using Fiddler

AWS Machine Learning

Through model monitoring, model explainability, analytics, and bias detection, Fiddler provides your company with an easy-to-use single pane of glass to ensure your models are behaving as they should. Danny is long tenured in the analytics and ML space, running presales and post-sales teams for startups like Endeca and Incorta.

article thumbnail

Buddying Up – Putting Virtual Employee Assistants at the Heart of Agent Development

TechSee

Current approaches to automation in contact centers are mainly focused on structured data, text and voice. With speech analytics, agents are equipped with a wide range of voice-based tools, enabling auto-recognition of accent, gender, and emotion. These systems also power conversational IVRs and voice-based virtual assistants.

article thumbnail

A review of purpose-built accelerators for financial services

AWS Machine Learning

The financial services industry (FSI) is no exception to this, and is a well-established producer and consumer of data and analytics. These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). in 2012 is now widely referred to as ML’s “Cambrian Explosion.”

Benchmark 113
article thumbnail

Machine learning with decentralized training data using federated learning on Amazon SageMaker

AWS Machine Learning

He works with government, non-profit, and education customers on big data, analytical, and AI/ML projects, helping them build solutions using AWS. In the client account, we create an IAM role called FL-kickoff-client-job with the policy FL-sagemaker-actions attached to the role.

Scripts 92
article thumbnail

Publish predictive dashboards in Amazon QuickSight using ML predictions from Amazon SageMaker Canvas

AWS Machine Learning

His knowledge ranges from application architecture to big data, analytics, and machine learning. Create a new IAM policy for QuickSight access To create an IAM policy, complete the following steps: On the IAM console, choose Policies in the navigation pane. Choose Create policy. Varun Mehta is a Solutions Architect at AWS.