Flipboard

MAY 28, 2025

Machine learning (ML) has emerged as a powerful tool to help nonprofits expedite manual processes, quickly unlock insights from data, and accelerate mission outcomesfrom personalizing marketing materials for donors to predicting member churn and donation patterns.

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AWS Machine Learning Blog

NOVEMBER 13, 2024

You can now register machine learning (ML) models in Amazon SageMaker Model Registry with Amazon SageMaker Model Cards , making it straightforward to manage governance information for specific model versions directly in SageMaker Model Registry in just a few clicks.

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Data Science Dojo

APRIL 3, 2023

Drag and drop tools have revolutionized the way we approach machine learning (ML) workflows. Gone are the days of manually coding every step of the process – now, with drag-and-drop interfaces, streamlining your ML pipeline has become more accessible and efficient than ever before. H2O.ai H2O.ai

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AWS Machine Learning Blog

OCTOBER 28, 2024

In the modern, cloud-centric business landscape, data is often scattered across numerous clouds and on-site systems. This fragmentation can complicate efforts by organizations to consolidate and analyze data for their machine learning (ML) initiatives.

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Machine Learning Machine Learning ML ML 114

Dataconomy

MARCH 27, 2023

However, while RPA and ML share some similarities, they differ in functionality, purpose, and the level of human intervention required. In this article, we will explore the similarities and differences between RPA and ML and examine their potential use cases in various industries. What is machine learning (ML)?

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AWS Machine Learning Blog

FEBRUARY 1, 2024

It offers industry-leading scalability, data availability, security, and performance. SageMaker Canvas now supports comprehensive data preparation capabilities powered by SageMaker Data Wrangler. We also demonstrate using the chat for data prep feature in SageMaker Canvas to analyze the data and visualize your findings.

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AWS Machine Learning Blog

AUGUST 15, 2024

Starting today, you can interactively prepare large datasets, create end-to-end data flows, and invoke automated machine learning (AutoML) experiments on petabytes of data—a substantial leap from the previous 5 GB limit. Organizations often struggle to extract meaningful insights and value from their ever-growing volume of data.

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AWS Machine Learning Blog

OCTOBER 24, 2024

Machine learning (ML) helps organizations to increase revenue, drive business growth, and reduce costs by optimizing core business functions such as supply and demand forecasting, customer churn prediction, credit risk scoring, pricing, predicting late shipments, and many others. Let’s learn about the services we will use to make this happen.

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Data Warehouse Machine Learning Machine Learning Cloud Data 120

Dataconomy

DECEMBER 20, 2024

With the most recent developments in machine learning , this process has become more accurate, flexible, and fast: algorithms analyze vast amounts of data, glean insights from the data, and find optimal solutions. Image credit: economicsdiscussion.net The Transformation with ML The dynamic pricing landscape is very different now.

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Machine Learning Machine Learning ML ML 103

AWS Machine Learning Blog

AUGUST 4, 2023

Data preparation is a critical step in any data-driven project, and having the right tools can greatly enhance operational efficiency. Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare tabular and image data for machine learning (ML) from weeks to minutes.

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Data Preparation AWS ML ML 96

Dataconomy

MARCH 29, 2025

He suggested that a Feature Store can help manage preprocessed data and facilitate cross-team usage, while a centralized Data Warehouse (DWH) domain can unify data preparation and migration. From the data side, this is resolved through centralized data preparation using a DWH (Data Warehouse) domain, Krotkikh said.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts.

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AWS Machine Learning Blog

FEBRUARY 12, 2024

Sharing in-house resources with other internal teams, the Ranking team machine learning (ML) scientists often encountered long wait times to access resources for model training and experimentation – challenging their ability to rapidly experiment and innovate. If it shows online improvement, it can be deployed to all the users.

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ML ML AWS Machine Learning 138

Dataconomy

MAY 7, 2025

ML model cards represent an innovative step towards transparency and ethical practices in the field of machine learning. What is an ML model card? An ML model card is a standardized document that offers detailed insights into machine learning models. This part encourages transparency about potential weaknesses in the model.

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ML ML Machine Learning Machine Learning 91

AWS Machine Learning Blog

OCTOBER 15, 2024

We’re excited to announce the release of SageMaker Core , a new Python SDK from Amazon SageMaker designed to offer an object-oriented approach for managing the machine learning (ML) lifecycle. With SageMaker Core, managing ML workloads on SageMaker becomes simpler and more efficient. and above.

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IBM Data Science in Practice

JANUARY 9, 2024

Many businesses are in different stages of their MAS AI/ML modernization journey. In this blog, we delve into 4 different “on-ramps” we created in a MAS Accelerator to offer a straightforward path to harnessing the power of AI in MAS, wherever you may be on your MAS AI/ML modernization journey.

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AWS Machine Learning Blog

APRIL 26, 2023

Do you need help to move your organization’s Machine Learning (ML) journey from pilot to production? Most executives think ML can apply to any business decision, but on average only half of the ML projects make it to production. Challenges Customers may face several challenges when implementing machine learning (ML) solutions.

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ML ML AWS Machine Learning 123

Flipboard

NOVEMBER 24, 2023

With that, the need for data scientists and machine learning (ML) engineers has grown significantly. Data scientists and ML engineers require capable tooling and sufficient compute for their work. Data scientists and ML engineers require capable tooling and sufficient compute for their work.

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Dataconomy

MARCH 19, 2025

This structured framework ensures that all necessary stepsfrom data preparation to model monitoringare executed systematically, enhancing efficiency and effectiveness in both business and technology applications. The main components typically include data preparation, model training, deployment, and ongoing monitoring.

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Machine Learning Machine Learning Data Preparation ML 91

AWS Machine Learning Blog

NOVEMBER 22, 2023

Now all you need is some guidance on generative AI and machine learning (ML) sessions to attend at this twelfth edition of re:Invent. In addition to several exciting announcements during keynotes, most of the sessions in our track will feature generative AI in one form or another, so we can truly call our track “Generative AI and ML.”

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AWS Machine Learning Blog

SEPTEMBER 20, 2023

In these scenarios, as you start to embrace generative AI, large language models (LLMs) and machine learning (ML) technologies as a core part of your business, you may be looking for options to take advantage of AWS AI and ML capabilities outside of AWS in a multicloud environment.

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phData

NOVEMBER 4, 2024

With data software pushing the boundaries of what’s possible in order to answer business questions and alleviate operational bottlenecks, data-driven companies are curious how they can go “beyond the dashboard” to find the answers they are looking for. One of the standout features of Dataiku is its focus on collaboration.

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Machine Learning Machine Learning Data Science ML 69

Dataconomy

APRIL 28, 2025

AWS SageMaker is transforming the way organizations approach machine learning by providing a comprehensive, cloud-based platform that standardizes the entire workflow, from data preparation to model deployment. Data preparation This involves annotating datasets of images and videos for machine learning tasks.

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AWS Machine Learning Machine Learning Data Preparation 91

AWS Machine Learning Blog

MARCH 8, 2023

Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. We add this data to Snowflake as a new table.

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ML ML AWS Python 136

Towards AI

NOVEMBER 4, 2024

Predicting the elections, however, presents challenges unique to it, such as the dynamic nature of voter preferences, non-linear interactions, and latent biases in the data. The points to cover in this article are as follows: Generating synthetic data to illustrate ML modelling for election outcomes.

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Dataconomy

MARCH 17, 2025

Augmented analytics is revolutionizing how organizations interact with their data. By harnessing the power of machine learning (ML) and natural language processing (NLP), businesses can streamline their data analysis processes and make more informed decisions. What is augmented analytics?

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IBM Data Science in Practice

MARCH 21, 2023

This article will walk you though how to approach deep learning modeling through the MVI platform from data preparation to your first deployment. The figure above depicts the required steps to build a successful ML model with Maximo Visual Inspection. What are the types of image processing ML models?

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Dataconomy

MAY 9, 2025

The role of machine learning infrastructure Machine Learning infrastructure plays a pivotal role in coordinating and executing essential resources for training and deploying ML models. The benefits of efficient data ingestion include: Optimized data utilization: Allowing organizations to fully leverage existing data assets.

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Machine Learning Machine Learning ML ML 103

Flipboard

AUGUST 17, 2023

Amazon Redshift is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. SageMaker Studio is the first fully integrated development environment (IDE) for ML. The next step is to build ML models using features selected from one or multiple feature groups.

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AWS Machine Learning Blog

DECEMBER 1, 2023

The ability to quickly build and deploy machine learning (ML) models is becoming increasingly important in today’s data-driven world. However, building ML models requires significant time, effort, and specialized expertise. This is where the AWS suite of low-code and no-code ML services becomes an essential tool.

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Machine Learning Machine Learning Data Preparation ML 124

Dataconomy

SEPTEMBER 13, 2023

Data scientists dedicate a significant chunk of their time to data preparation, as revealed by a survey conducted by the data science platform Anaconda. This process involves rectifying or discarding abnormal or non-standard data points and ensuring the accuracy of measurements.

Machine Learning 195

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AWS Machine Learning Blog

MAY 2, 2023

Machine learning (ML) helps organizations generate revenue, reduce costs, mitigate risk, drive efficiencies, and improve quality by optimizing core business functions across multiple business units such as marketing, manufacturing, operations, sales, finance, and customer service. Let’s assume the role of a data scientist.

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AWS Machine Learning Blog

AUGUST 21, 2024

Amazon DataZone makes it straightforward for engineers, data scientists, product managers, analysts, and business users to access data throughout an organization so they can discover, use, and collaborate to derive data-driven insights.

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AWS Machine Learning Blog

AUGUST 3, 2023

As machine learning (ML) becomes increasingly prevalent in a wide range of industries, organizations are finding the need to train and serve large numbers of ML models to meet the diverse needs of their customers. Here, the checkpoints need to be saved in a pre-specified location, with the default being /opt/ml/checkpoints.

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ML ML AWS Python 97

Flipboard

NOVEMBER 21, 2024

We use Amazon SageMaker Pipelines , which helps automate the different steps, including data preparation, fine-tuning, and creating the model. Define the text generation configuration AutoMLV2 automates the entire ML process, from data preprocessing to model training and deployment.

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Dataconomy

MARCH 27, 2023

However, while RPA and ML share some similarities, they differ in functionality, purpose, and the level of human intervention required. In this article, we will explore the similarities and differences between RPA and ML and examine their potential use cases in various industries. What is machine learning (ML)?

ML 70

ML ML Machine Learning Machine Learning 70

AWS Machine Learning Blog

SEPTEMBER 11, 2024

Machine learning operations (MLOps) are a set of practices that automate and simplify machine learning (ML) workflows and deployments. AWS published Guidance for Optimizing MLOps for Sustainability on AWS to help customers maximize utilization and minimize waste in their ML workloads.

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