KDnuggets

JUNE 23, 2025

Managing ML projects without MLFlow is challenging. MLFlow Projects MLflow Projects enable reproducibility and portability by standardizing the structure of ML code. It also works with cloud services like AWS SageMaker. Version Control : Maintain version control for code, data, and models. Why Use MLFlow?

Machine Learning 229

Machine Learning Machine Learning Natural Language Processing Data Science 229

Analytics Vidhya

NOVEMBER 26, 2020

Introduction: Gone are the days when enterprises set up their own in-house server and spending a gigantic amount of budget on storage infrastructure & The post Deployment of ML models in Cloud – AWS SageMaker?(in-built in-built algorithms) appeared first on Analytics Vidhya.

AWS 178

AWS ML ML Algorithm 178

AWS Machine Learning Blog

NOVEMBER 15, 2024

Principal wanted to use existing internal FAQs, documentation, and unstructured data and build an intelligent chatbot that could provide quick access to the right information for different roles. Principal also used the AWS open source repository Lex Web UI to build a frontend chat interface with Principal branding.

AWS 115

AWS AI AI Machine Learning 115

databricks

JUNE 11, 2025

" — James Lin, Head of AI ML Innovation, Experian The Path Forward: From Lab to Production in Days, Not Months Early customers are already experiencing the transformation Agent Bricks delivers – accuracy improvements that double performance benchmarks and reduce development timelines from weeks to a single day.

Analytics 348

Analytics Analytics Data Science AI 348

Flipboard

JULY 3, 2025

Organizations need a unified, streamlined approach that simplifies the entire process from data preparation to model deployment. To address these challenges, AWS has expanded Amazon SageMaker with a comprehensive set of data, analytics, and generative AI capabilities.

ML 114

ML AWS ML Data Engineering 114

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.

ML 115

ML ML AWS Data Preparation 115

AWS Machine Learning Blog

OCTOBER 16, 2024

Amazon SageMaker supports geospatial machine learning (ML) capabilities, allowing data scientists and ML engineers to build, train, and deploy ML models using geospatial data. Identify areas of interest We begin by illustrating how SageMaker can be applied to analyze geospatial data at a global scale.

ML 121

ML ML Clustering Machine Learning 121

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.

Data Warehouse 115

Data Warehouse Machine Learning Machine Learning Cloud Data 115

AWS Machine Learning Blog

DECEMBER 4, 2024

Businesses are under pressure to show return on investment (ROI) from AI use cases, whether predictive machine learning (ML) or generative AI. Only 54% of ML prototypes make it to production, and only 5% of generative AI use cases make it to production. Using SageMaker, you can build, train and deploy ML models.

ML 101

ML ML AWS AI 101

AWS Machine Learning Blog

OCTOBER 20, 2023

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries.

ML 135

ML ML AWS Data Lakes 135

AWS Machine Learning Blog

JUNE 3, 2025

This post details our technical implementation using AWS services to create a scalable, multilingual AI assistant system that provides automated assistance while maintaining data security and GDPR compliance. Amazon Titan Embeddings also integrates smoothly with AWS, simplifying tasks like indexing, search, and retrieval.

AWS 105

AWS AI AI Machine Learning 105

Flipboard

APRIL 23, 2025

Solution overview The NER & LLM Gen AI Application is a document processing solution built on AWS that combines NER and LLMs to automate document analysis at scale. Click here to open the AWS console and follow along. The endpoint lifecycle is orchestrated through dedicated AWS Lambda functions that handle creation and deletion.

AWS 108

AWS ML ML Natural Language Processing 108

AWS Machine Learning Blog

AUGUST 12, 2024

Instead, organizations are increasingly looking to take advantage of transformative technologies like machine learning (ML) and artificial intelligence (AI) to deliver innovative products, improve outcomes, and gain operational efficiencies at scale.

ML 125

ML ML AWS AI 125

databricks

JUNE 18, 2025

160 Spear Street, 15th Floor San Francisco, CA 94105 1-866-330-0121 See Careers at Databricks © Databricks 2025. 160 Spear Street, 15th Floor San Francisco, CA 94105 1-866-330-0121 See Careers at Databricks © Databricks 2025.

AI 205

AI AI Data Science Artificial Intelligence 205

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.

ML 151

ML ML AWS AI 151

databricks

JUNE 11, 2025

Product December 12, 2024 / 4 min read Making AI More Accessible: Up to 80% Cost Savings with Meta Llama 3.3 160 Spear Street, 15th Floor San Francisco, CA 94105 1-866-330-0121 See Careers at Databricks © Databricks 2025. Product December 12, 2024 / 4 min read Making AI More Accessible: Up to 80% Cost Savings with Meta Llama 3.3

Database 214

Database Data Lakes ETL Analytics 214

Flipboard

DECEMBER 18, 2023

Customers use Amazon Redshift as a key component of their data architecture to drive use cases from typical dashboarding to self-service analytics, real-time analytics, machine learning (ML), data sharing and monetization, and more. Hear also from Adidas, GlobalFoundries, and University of California, Irvine.

AWS 137

AWS Data Warehouse ETL SQL 137

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.

Machine Learning 110

Machine Learning Machine Learning ML ML 110

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.

ML 117

ML ML AWS Machine Learning 117

Data Science Dojo

OCTOBER 31, 2024

Growth Outlook: Companies like Google DeepMind, NASA’s Jet Propulsion Lab, and IBM Research actively seek research data scientists for their teams, with salaries typically ranging from $120,000 to $180,000. With the continuous growth in AI, demand for remote data science jobs is set to rise.

Data Science 195

Data Science Data Scientist Machine Learning Machine Learning 195

AWS Machine Learning Blog

FEBRUARY 21, 2025

Lets assume that the question What date will AWS re:invent 2024 occur? The corresponding answer is also input as AWS re:Invent 2024 takes place on December 26, 2024. If the question was Whats the schedule for AWS events in December?, This setup uses the AWS SDK for Python (Boto3) to interact with AWS services.

AWS 127

AWS Natural Language Processing Machine Learning Machine Learning 127

AWS Machine Learning Blog

JANUARY 5, 2024

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models.

AWS 135

AWS Data Science ML ML 135

AWS Machine Learning Blog

FEBRUARY 25, 2025

Using an Amazon Q Business custom data source connector , you can gain insights into your organizations third party applications with the integration of generative AI and natural language processing. Alation is a data intelligence company serving more than 600 global enterprises, including 40% of the Fortune 100.

AWS 112

AWS AI AI Natural Language Processing 112

Flipboard

MAY 14, 2025

Amazon QuickSight is a comprehensive Business Intelligence (BI) environment that offers a range of advanced features for data analysis and visualization. Prerequisites To perform the solution in this walkthrough, you need to have the following resources: An active AWS account to access Amazon Q Business and QuickSight features.

AWS 140

AWS AI AI Database 140

Data Science Dojo

FEBRUARY 20, 2023

Machine learning (ML) is the technology that automates tasks and provides insights. It allows data scientists to build models that can automate specific tasks. It comes in many forms, with a range of tools and platforms designed to make working with ML more efficient. It also has ML algorithms built into the platform.

Machine Learning 195

Machine Learning Machine Learning AWS Azure 195

AWS Machine Learning Blog

NOVEMBER 15, 2024

Our relentless pursuit of valuable insights from data fuels our business decisions and works to achieve customer satisfaction. In this post, we discuss how GoDaddy’s Care & Services team, in close collaboration with the AWS GenAI Labs team, built Lighthouse—a generative AI solution powered by Amazon Bedrock.

Analytics 117

Analytics Analytics AWS Data Analysis 117

AWS Machine Learning Blog

SEPTEMBER 20, 2023

For example, you might have acquired a company that was already running on a different cloud provider, or you may have a workload that generates value from unique capabilities provided by AWS. We show how you can build and train an ML model in AWS and deploy the model in another platform.

ML 132

ML ML Azure AWS 132

AWS Machine Learning Blog

SEPTEMBER 18, 2024

The ZMP analyzes billions of structured and unstructured data points to predict consumer intent by using sophisticated artificial intelligence (AI) to personalize experiences at scale. Hosted on Amazon ECS with tasks run on Fargate, this platform streamlines the end-to-end ML workflow, from data ingestion to model deployment.

AWS 123

AWS Machine Learning Machine Learning ML 123

Flipboard

APRIL 21, 2025

In this post, to address the aforementioned challenges, we introduce an automated evaluation framework that is deployable on AWS. We then present a typical evaluation workflow, followed by our AWS-based solution that facilitates this process. The UI service can be run locally in a Docker container or deployed to AWS Fargate.

AWS 146

AWS AI AI Machine Learning 146

phData

APRIL 24, 2025

phData, a leading AI and data services company, announced today that it has achieved the AWS Generative AI Competency as an AWS Service Delivery partner. Achieving the AWS Generative AI Competency strengthens our commitment to helping our clients adopt AI.

AWS 52

AWS AI AI Artificial Intelligence 52

AWS Machine Learning Blog

NOVEMBER 29, 2023

Data preparation is a crucial step in any machine learning (ML) workflow, yet it often involves tedious and time-consuming tasks. Amazon SageMaker Canvas now supports comprehensive data preparation capabilities powered by Amazon SageMaker Data Wrangler.

Data Preparation 127

Data Preparation ML ML Data Quality 127

AWS Machine Learning Blog

OCTOBER 18, 2023

Purina used artificial intelligence (AI) and machine learning (ML) to automate animal breed detection at scale. The solution focuses on the fundamental principles of developing an AI/ML application workflow of data preparation, model training, model evaluation, and model monitoring. DynamoDB is used to store the pet attributes.

AWS 130

AWS ML ML Machine Learning 130

AWS Machine Learning Blog

DECEMBER 7, 2023

In this post, we describe the end-to-end workforce management system that begins with location-specific demand forecast, followed by courier workforce planning and shift assignment using Amazon Forecast and AWS Step Functions. AWS Step Functions automatically initiate and monitor these workflows by simplifying error handling.

AWS 131

AWS Algorithm Machine Learning Machine Learning 131

Hacker News

NOVEMBER 19, 2024

Here are a few of the things that you might do as an AI Engineer at TigerEye: - Design, develop, and validate statistical models to explain past behavior and to predict future behavior of our customers’ sales teams - Own training, integration, deployment, versioning, and monitoring of ML components - Improve TigerEye’s existing metrics collection and (..)

Computer Science 155

Computer Science Computer Science ETL ML 155

AWS Machine Learning Blog

JUNE 16, 2023

In this post, we discuss how SambaSafety used AWS machine learning (ML) and continuous integration and continuous delivery (CI/CD) tools to deploy their existing data science application for batch inference. Customer use case SambaSafety’s data science team had long been using the power of data to inform their business.

AWS 110

AWS Data Science ML ML 110

AWS Machine Learning Blog

JUNE 26, 2023

Machine learning (ML) administrators play a critical role in maintaining the security and integrity of ML workloads. To address this challenge, AWS introduced Amazon SageMaker Role Manager in December 2022. Their primary focus is to ensure that users operate with the utmost security, adhering to the principle of least privilege.

AWS 98

AWS ML ML Data Scientist 98

AWS Machine Learning Blog

JULY 10, 2024

During the last 18 months, we’ve launched more than twice as many machine learning (ML) and generative AI features into general availability than the other major cloud providers combined. Each application can be immediately scaled to thousands of users and is secure and fully managed by AWS, eliminating the need for any operational expertise.

AWS 143

AWS Machine Learning Machine Learning Database 143