Flipboard

DECEMBER 11, 2024

Many of these applications are complex to build because they require collaboration across teams and the integration of data, tools, and services. Data engineers use data warehouses, data lakes, and analytics tools to load, transform, clean, and aggregate data.

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SQL AWS Data Lakes AI 160

AWS Machine Learning Blog

SEPTEMBER 27, 2024

Solution overview Amazon SageMaker is a fully managed service that helps developers and data scientists build, train, and deploy machine learning (ML) models. About the authors Scott Patterson is a Senior Solutions Architect at AWS. The sunburst graph below is a visualization of this classification.

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AWS Machine Learning Blog

AUGUST 26, 2024

At AWS, we are transforming our seller and customer journeys by using generative artificial intelligence (AI) across the sales lifecycle. It will be able to answer questions, generate content, and facilitate bidirectional interactions, all while continuously using internal AWS and external data to deliver timely, personalized insights.

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AWS Machine Learning Blog

AUGUST 21, 2023

You can streamline the process of feature engineering and data preparation with SageMaker Data Wrangler and finish each stage of the data preparation workflow (including data selection, purification, exploration, visualization, and processing at scale) within a single visual interface.

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Precisely

MARCH 9, 2023

Data warehouse vs. data lake, each has their own unique advantages and disadvantages; it’s helpful to understand their similarities and differences. In this article, we’ll focus on a data lake vs. data warehouse. It is often used as a foundation for enterprise data lakes.

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AWS Machine Learning Blog

AUGUST 21, 2024

Amazon DataZone is a data management service that makes it quick and convenient to catalog, discover, share, and govern data stored in AWS, on-premises, and third-party sources. Solution overview In this section, we provide an overview of three personas: the data admin, data publisher, and data scientist.

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AWS Machine Learning Blog

SEPTEMBER 5, 2023

In this post, we show how the Carrier and AWS teams applied ML to predict faults across large fleets of equipment using a single model. We first highlight how we use AWS Glue for highly parallel data processing. This dramatically reduces the size of data while capturing features that characterize the equipment’s behavior.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

They are processing data across channels, including recorded contact center interactions, emails, chat and other digital channels. Solution requirements Principal provides investment services through Genesys Cloud CX, a cloud-based contact center that provides powerful, native integrations with AWS.

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Flipboard

NOVEMBER 24, 2023

In an increasingly digital and rapidly changing world, BMW Group’s business and product development strategies rely heavily on data-driven decision-making. With that, the need for data scientists and machine learning (ML) engineers has grown significantly.

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IBM Journey to AI blog

MARCH 14, 2024

The solution: IBM databases on AWS To solve for these challenges, IBM’s portfolio of SaaS database solutions on Amazon Web Services (AWS), enables enterprises to scale applications, analytics and AI across the hybrid cloud landscape. Let’s delve into the database portfolio from IBM available on AWS. 

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AWS Machine Learning Blog

MARCH 8, 2023

This solution helps market analysts design and perform data-driven bidding strategies optimized for power asset profitability. In this post, you will learn how Marubeni is optimizing market decisions by using the broad set of AWS analytics and ML services, to build a robust and cost-effective Power Bid Optimization solution.

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phData

SEPTEMBER 19, 2023

With the amount of data companies are using growing to unprecedented levels, organizations are grappling with the challenge of efficiently managing and deriving insights from these vast volumes of structured and unstructured data. What is a Data Lake? Consistency of data throughout the data lake.

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AWS Machine Learning Blog

JUNE 22, 2023

Working with AWS, Light & Wonder recently developed an industry-first secure solution, Light & Wonder Connect (LnW Connect), to stream telemetry and machine health data from roughly half a million electronic gaming machines distributed across its casino customer base globally when LnW Connect reaches its full potential.

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ODSC - Open Data Science

JUNE 7, 2023

To make your data management processes easier, here’s a primer on data lakes, and our picks for a few data lake vendors worth considering. What is a data lake? First, a data lake is a centralized repository that allows users or an organization to store and analyze large volumes of data.

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AWS Machine Learning Blog

FEBRUARY 13, 2024

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). Their task is to construct and oversee efficient data pipelines.

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AWS Machine Learning Blog

JANUARY 13, 2023

To fulfill these requirements, TR built the Enterprise AI platform around the following five pillars: a data service, experimentation workspace, central model registry, model deployment service, and model monitoring. Historically at TR, ML has been a capability for teams with advanced data scientists and engineers.

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phData

NOVEMBER 8, 2024

Versioning also ensures a safer experimentation environment, where data scientists can test new models or hypotheses on historical data snapshots without impacting live data. Note : Cloud Data warehouses like Snowflake and Big Query already have a default time travel feature. FAQs What is a Data Lakehouse?

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Flipboard

AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. If you’re familiar with SageMaker and writing Spark code, option B could be your choice.

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O'Reilly Media

OCTOBER 19, 2021

ML use cases rarely dictate the master data management solution, so the ML stack needs to integrate with existing data warehouses. Today, a number of cloud-based, auto-scaling systems are easily available, such as AWS Batch. Data Science Layers. Software Development Layers. Software Architecture.

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AWS Machine Learning Blog

JULY 29, 2024

In Part 3 , we demonstrate how business analysts and citizen data scientists can create machine learning (ML) models, without code, in Amazon SageMaker Canvas and deploy trained models for integration with Salesforce Einstein Studio to create powerful business applications. For this post, we use the Anthropic Claude 3 Sonnet model.

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Data Science 101

NOVEMBER 7, 2019

Azure Synapse Analytics can be seen as a merge of Azure SQL Data Warehouse and Azure Data Lake. Synapse allows one to use SQL to query petabytes of data, both relational and non-relational, with amazing speed. I have not gotten a chance to try it out yet, so I am not sure its usecase for data science yet.

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ODSC - Open Data Science

FEBRUARY 2, 2023

The role of a data scientist is in demand and 2023 will be no exception. To get a better grip on those changes we reviewed over 25,000 data scientist job descriptions from that past year to find out what employers are looking for in 2023. Data Science Of course, a data scientist should know data science!

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AWS Machine Learning Blog

AUGUST 2, 2024

This is a guest post co-written with Vicente Cruz Mínguez, Head of Data and Advanced Analytics at Cepsa Química, and Marcos Fernández Díaz, Senior Data Scientist at Keepler. After data is extracted, the job performs document chunking, data cleanup, and postprocessing.

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AWS Machine Learning Blog

FEBRUARY 7, 2025

In this post, we assign the functions in terms of the ML lifecycle to each role as follows: Lead data scientist Provision accounts for ML development teams, govern access to the accounts and resources, and promote standardized model development and approval process to eliminate repeated engineering effort.

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

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AWS Machine Learning Blog

DECEMBER 18, 2023

By using these capabilities, businesses can efficiently store, manage, and analyze time-series data, enabling data-driven decisions and gaining a competitive edge. sales-train-data is used to store data extracted from MongoDB Atlas, while sales-forecast-output contains predictions from Canvas. Note we have two folders.

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AWS Machine Learning Blog

NOVEMBER 9, 2023

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. with administrative privileges installed on AWS Terraform version 1.5.5 After the key is provisioned, it should be visible on the AWS KMS console.

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Pickl AI

APRIL 21, 2025

This crucial step involves handling missing values, correcting errors (addressing Veracity issues from Big Data), transforming data into a usable format, and structuring it for analysis. This often takes up a significant chunk of a data scientist’s time. It turns the raw ocean of data into actionable intelligence.

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AWS Machine Learning Blog

APRIL 12, 2023

In this post, we describe how AWS Partner Airis Solutions used Amazon Lookout for Equipment , AWS Internet of Things (IoT) services, and CloudRail sensor technologies to provide a state-of-the-art solution to address these challenges. It’s an easy way to run analytics on IoT data to gain accurate insights.

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AWS Machine Learning Blog

MAY 15, 2023

We outline how we built an automated demand forecasting pipeline using Forecast and orchestrated by AWS Step Functions to predict daily demand for SKUs. On an ongoing basis, we calculate mean absolute percentage error (MAPE) ratios with product-based data, and optimize model and feature ingestion processes.

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AWS Machine Learning Blog

JUNE 20, 2024

Our goal was to improve the user experience of an existing application used to explore the counters and insights data. The data is stored in a data lake and retrieved by SQL using Amazon Athena. Eitan Sela is a Generative AI and Machine Learning Specialist Solutions Architect at AWS.

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AWS Machine Learning Blog

AUGUST 2, 2023

We demonstrate CDE using simple examples and provide a step-by-step guide for you to experience CDE in an Amazon Kendra index in your own AWS account. Marketing firms store vast amounts of digital data that needs to be centralized, easily searchable, and scalable enabled by data catalogs.

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AWS Machine Learning Blog

APRIL 18, 2023

It includes sensor devices to capture vibration and temperature data, a gateway device to securely transfer data to the AWS Cloud, the Amazon Monitron service that analyzes the data for anomalies with ML, and a companion mobile app to track potential failures in your machinery.

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AWS Machine Learning Blog

SEPTEMBER 12, 2024

Organizations that want to build their own models or want granular control are choosing Amazon Web Services (AWS) because we are helping customers use the cloud more efficiently and leverage more powerful, price-performant AWS capabilities such as petabyte-scale networking capability, hyperscale clustering, and the right tools to help you build.

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Dataconomy

SEPTEMBER 4, 2023

Downtime, like the AWS outage in 2017 that affected several high-profile websites, can disrupt business operations. Data integration: Integrate data from various sources into a centralized cloud data warehouse or data lake. Ensure that data is clean, consistent, and up-to-date.

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AWS Machine Learning Blog

NOVEMBER 27, 2023

SageMaker endpoints can be registered to the Salesforce Data Cloud to activate predictions in Salesforce. Salesforce Data Cloud and Einstein Studio Salesforce Data Cloud is a data platform that provides businesses with real-time updates of their customer data from any touch point.

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The MLOps Blog

JUNE 27, 2023

For example, if you use AWS, you may prefer Amazon SageMaker as an MLOps platform that integrates with other AWS services. SageMaker Studio offers built-in algorithms, automated model tuning, and seamless integration with AWS services, making it a powerful platform for developing and deploying machine learning solutions at scale.

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