Data Science Dojo

OCTOBER 31, 2024

For data scientists, this shift has opened up a global market of remote data science jobs, with top employers now prioritizing skills that allow remote professionals to thrive. Here’s everything you need to know to land a remote data science job, from advanced role insights to tips on making yourself an unbeatable candidate.

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Data Science Dojo

FEBRUARY 20, 2023

These tools will help you streamline your machine learning workflow, reduce operational overheads, and improve team collaboration and communication. Machine learning (ML) is the technology that automates tasks and provides insights. It allows data scientists to build models that can automate specific tasks.

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AWS Machine Learning Blog

DECEMBER 4, 2024

Our customers want a simple and secure way to find the best applications, integrate the selected applications into their machine learning (ML) and generative AI development environment, manage and scale their AI projects. Comet has been trusted by enterprise customers and academic teams since 2017.

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AWS Machine Learning Blog

FEBRUARY 27, 2024

In this post, we share how Axfood, a large Swedish food retailer, improved operations and scalability of their existing artificial intelligence (AI) and machine learning (ML) operations by prototyping in close collaboration with AWS experts and using Amazon SageMaker.

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AWS Machine Learning Blog

NOVEMBER 6, 2024

However, as exciting as these advancements are, data scientists often face challenges when it comes to developing UIs and to prototyping and interacting with their business users. Streamlit allows data scientists to create interactive web applications using Python, using their existing skills and knowledge.

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AWS Machine Learning Blog

DECEMBER 19, 2023

Artificial intelligence (AI) and machine learning (ML) are becoming an integral part of systems and processes, enabling decisions in real time, thereby driving top and bottom-line improvements across organizations. Machine learning operations (MLOps) applies DevOps principles to ML systems.

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IBM Data Science in Practice

APRIL 4, 2023

Introduction This article shows how to monitor a model deployed on AWS Sagemaker for quality, bias and explainability, using IBM Watson OpenScale on the IBM Cloud Pak for Data platform. This article shows how to use the endpoint generated from that tutorial to demonstrate how to monitor the AWS deployment with Watson OpenScale.

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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.

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AWS Machine Learning Blog

JULY 8, 2024

As industries begin adopting processes dependent on machine learning (ML) technologies, it is critical to establish machine learning operations (MLOps) that scale to support growth and utilization of this technology. AWS CloudTrail – Monitors and records account activity across AWS infrastructure.

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AWS Machine Learning Blog

OCTOBER 1, 2024

Over the last 18 months, AWS has announced more than twice as many machine learning (ML) and generative artificial intelligence (AI) features into general availability than the other major cloud providers combined. The following figure highlights where AWS lands in the DSML Magic Quadrant.

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AWS Machine Learning Blog

AUGUST 5, 2024

This post demonstrates how to seamlessly automate the deployment of an end-to-end RAG solution using Knowledge Bases for Amazon Bedrock and AWS CloudFormation , enabling organizations to quickly and effortlessly set up a powerful RAG system. On the AWS CloudFormation console, create a new stack. txt,md,html,doc/docx,csv,xls/.xlsx,pdf).

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Data Science Dojo

AUGUST 24, 2023

While DevOps and MLOps share many similarities, MLOps requires a more specialized set of tools and practices to address the unique challenges posed by data-driven and computationally intensive ML workflows. AWS Integration : As Netflix developed Metaflow, it closely integrates with Amazon Web Services (AWS) infrastructure.

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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. Choose Create stack.

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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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IBM Data Science in Practice

MARCH 8, 2023

Introduction This article shows how to build a machine learning model on a Watson Studio platform running on IBM Cloud Pak for Data (CPD). You can then export the model and deploy it on Amazon Sagemaker on Amazon Web Server (AWS). environment running on IBM Cloud Pak for Data 4.5.x,

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AWS Machine Learning Blog

DECEMBER 9, 2024

With access to a wide range of generative AI foundation models (FM) and the ability to build and train their own machine learning (ML) models in Amazon SageMaker , users want a seamless and secure way to experiment with and select the models that deliver the most value for their business.

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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.

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AWS Machine Learning Blog

NOVEMBER 26, 2024

With the current demand for AI and machine learning (AI/ML) solutions, the processes to train and deploy models and scale inference are crucial to business success. Even though AI/ML and especially generative AI progress is rapid, machine learning operations (MLOps) tooling is continuously evolving to keep pace.

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AWS Machine Learning Blog

APRIL 17, 2024

Recent developments in machine learning (ML) have led to increasingly large models, some of which require hundreds of billions of parameters. For data scientists, ML chips utilization and saturation are also relevant for capacity planning. Solution overview The following diagram illustrates the solution architecture.

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AWS Machine Learning Blog

DECEMBER 4, 2023

In this post, we explain how we built an end-to-end product category prediction pipeline to help commercial teams by using Amazon SageMaker and AWS Batch , reducing model training duration by 90%. An important aspect of our strategy has been the use of SageMaker and AWS Batch to refine pre-trained BERT models for seven different languages.

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AWS Machine Learning Blog

DECEMBER 24, 2024

To simplify infrastructure setup and accelerate distributed training, AWS introduced Amazon SageMaker HyperPod in late 2023. In this blog post, we showcase how you can perform efficient supervised fine tuning for a Meta Llama 3 model using PEFT on AWS Trainium with SageMaker HyperPod. architectures/5.sagemaker-hyperpod/LifecycleScripts/base-config/

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AWS Machine Learning Blog

SEPTEMBER 18, 2023

Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model. The full code can be found on the aws-samples-for-ray GitHub repository.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

Since launching in June 2023, the AWS Generative AI Innovation Center team of strategists, data scientists, machine learning (ML) engineers, and solutions architects have worked with hundreds of customers worldwide, and helped them ideate, prioritize, and build bespoke solutions that harness the power of generative AI.

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AWS Machine Learning Blog

SEPTEMBER 5, 2023

In order to improve our equipment reliability, we partnered with the Amazon Machine Learning Solutions Lab to develop a custom machine learning (ML) model capable of predicting equipment issues prior to failure. We first highlight how we use AWS Glue for highly parallel data processing. Additionally, 10.4%

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AWS Machine Learning Blog

JUNE 20, 2024

AWS customers that implement secure development environments often have to restrict outbound and inbound internet traffic. This becomes increasingly important with artificial intelligence (AI) development because of the data assets that need to be protected. You can learn more about Gateway VPC endpoints here.

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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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AWS Machine Learning Blog

SEPTEMBER 27, 2024

Architecting specific AWS Cloud solutions involves creating diagrams that show relationships and interactions between different services. Instead of building the code manually, you can use Anthropic’s Claude 3’s image analysis capabilities to generate AWS CloudFormation templates by passing an architecture diagram as input.

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AWS Machine Learning Blog

OCTOBER 22, 2024

Amazon SageMaker is a cloud-based machine learning (ML) platform within the AWS ecosystem that offers developers a seamless and convenient way to build, train, and deploy ML models. By using a combination of AWS services, you can implement this feature effectively, overcoming the current limitations within SageMaker.

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AWS Machine Learning Blog

DECEMBER 14, 2023

This is a customer post jointly authored by ICL and AWS employees. To overcome this business challenge, ICL decided to develop in-house capabilities to use machine learning (ML) for computer vision (CV) to automatically monitor their mining machines.

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AWS Machine Learning Blog

MAY 1, 2024

Llama2 by Meta is an example of an LLM offered by AWS. To learn more about Llama 2 on AWS, refer to Llama 2 foundation models from Meta are now available in Amazon SageMaker JumpStart. Virginia) and US West (Oregon) AWS Regions, and most recently announced general availability in the US East (Ohio) Region.

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AWS Machine Learning Blog

FEBRUARY 23, 2023

To address the large value challenge, you can utilize the Amazon SageMaker distributed data parallelism feature (SMDDP). SageMaker is a fully managed machine learning (ML) service. With data parallelism, a large volume of data is split into batches. This reduces the development velocity and ability to fail fast.

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AWS Machine Learning Blog

MAY 1, 2024

Our unstructured data comes from the Amazon EC2 User Guide for Linux Instances and Amazon EC2 Instance Types documentation, and the structured data is derived from the EC2 Instance On-Demand Pricing for the US East (N. Virginia) AWS Region. file for deploying the solution using the AWS CDK.

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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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Analytics Vidhya

JANUARY 5, 2022

This article was published as a part of the Data Science Blogathon. Introduction Are you a Data Science enthusiast or already a Data Scientist who is trying to make his or her portfolio strong by adding a good amount of hands-on projects to your resume? But have no clue where to get the datasets from so […].

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AWS Machine Learning Blog

MARCH 13, 2024

In this post, we introduce solutions that enable audio and text chat moderation using various AWS services, including Amazon Transcribe , Amazon Comprehend , Amazon Bedrock , and Amazon OpenSearch Service. OpenSearch Service is a fully managed service that makes it straightforward to deploy, scale, and operate OpenSearch in the AWS Cloud.

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AWS Machine Learning Blog

JUNE 23, 2023

For data scientists, moving machine learning (ML) models from proof of concept to production often presents a significant challenge. We also show you how to automate the deployment using the AWS Cloud Development Kit (AWS CDK). To build this image locally, we need Docker.

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AWS Machine Learning Blog

SEPTEMBER 29, 2023

This guest post is co-written by Lydia Lihui Zhang, Business Development Specialist, and Mansi Shah, Software Engineer/Data Scientist, at Planet Labs. In late 2023, Planet announced a partnership with AWS to make its geospatial data available through Amazon SageMaker.

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