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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AWS Machine Learning Blog

DECEMBER 9, 2024

In the initial stages of an ML project, data scientists collaborate closely, sharing experimental results to address business challenges. MLflow , a popular open-source tool, helps data scientists organize, track, and analyze ML and generative AI experiments, making it easier to reproduce and compare results.

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AWS Machine Learning Blog

NOVEMBER 26, 2024

Amazon SageMaker Studio provides a single web-based visual interface where data scientists create dedicated workspaces to perform all ML development steps required to prepare data and build, train, and deploy models. The AWS CDK is a framework for defining cloud infrastructure as code.

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KDnuggets

NOVEMBER 10, 2021

Data Scientist Career Path: from Novice to First Job; Understand Neural Networks from a Bayesian Perspective; The Best Ways for Data Professionals to Market AWS Skills; Build Your Own Automated Machine Learning App.

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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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KDnuggets

NOVEMBER 10, 2021

Data Scientist Career Path: from Novice to First Job; Understand Neural Networks from a Bayesian Perspective; The Best Ways for Data Professionals to Market AWS Skills; Build Your Own Automated Machine Learning App.

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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

FEBRUARY 21, 2025

The Hadoop environment was hosted on Amazon Elastic Compute Cloud (Amazon EC2) servers, managed in-house by Rockets technology team, while the data science experience infrastructure was hosted on premises. Communication between the two systems was established through Kerberized Apache Livy (HTTPS) connections over AWS PrivateLink.

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AWS Machine Learning Blog

OCTOBER 29, 2024

Refer to Supported Regions and models for batch inference for current supporting AWS Regions and models. To address this consideration and enhance your use of batch inference, we’ve developed a scalable solution using AWS Lambda and Amazon DynamoDB. Amazon S3 invokes the {stack_name}-create-batch-queue-{AWS-Region} Lambda function.

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Smart Data Collective

MARCH 2, 2022

Standards and expectations are rapidly changing, especially in regards to the types of technology used to create data science projects. Most data scientists are using some form of DevOps interface these days. There are a lot of important nuances for data scientists using Kubernetes. Why Serverless in Kubernetes?

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AWS Machine Learning Blog

MARCH 10, 2025

We walk through the journey Octus took from managing multiple cloud providers and costly GPU instances to implementing a streamlined, cost-effective solution using AWS services including Amazon Bedrock, AWS Fargate , and Amazon OpenSearch Service. Along the way, it also simplified operations as Octus is an AWS shop more generally.

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AWS Machine Learning Blog

OCTOBER 29, 2024

Recently, we’ve been witnessing the rapid development and evolution of generative AI applications, with observability and evaluation emerging as critical aspects for developers, data scientists, and stakeholders. This feature allows you to separate data into logical partitions, making it easier to analyze and process data later.

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Flipboard

JANUARY 6, 2025

Precise), an Amazon Web Services (AWS) Partner , participated in the AWS Think Big for Small Business Program (TBSB) to expand their AWS capabilities and to grow their business in the public sector. Precise Software Solutions, Inc. The platform helped the agency digitize and process forms, pictures, and other documents.

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Flipboard

NOVEMBER 22, 2024

Solution overview The following diagram illustrates the ML platform reference architecture using various AWS services. The functional architecture with different capabilities is implemented using a number of AWS services, including AWS Organizations , Amazon SageMaker , AWS DevOps services, and a data lake.

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AWS Machine Learning Blog

MARCH 15, 2023

Tens of thousands of AWS customers use AWS machine learning (ML) services to accelerate their ML development with fully managed infrastructure and tools. We demonstrate how two different personas, a data scientist and an MLOps engineer, can collaborate to lift and shift hundreds of legacy models.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

Streamlit is an open source framework for data scientists to efficiently create interactive web-based data applications in pure Python. Prerequisites To perform this solution, complete the following: Create and activate an AWS account. Make sure your AWS credentials are configured correctly. Install Python 3.7

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AWS Machine Learning Blog

OCTOBER 24, 2024

It allows data scientists and machine learning engineers to interact with their data and models and to visualize and share their work with others with just a few clicks. SageMaker Canvas has also integrated with Data Wrangler , which helps with creating data flows and preparing and analyzing your data.

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AWS Machine Learning Blog

JULY 8, 2024

MLOps practitioners have many options to establish an MLOps platform; one among them is cloud-based integrated platforms that scale with data science teams. AWS provides a full-stack of services to establish an MLOps platform in the cloud that is customizable to your needs while reaping all the benefits of doing ML in the cloud.

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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

MARCH 21, 2025

Amazon Simple Storage Service (Amazon S3) Amazon S3 is an object storage service built to store and protect any amount of data. AWS Lambda AWS Lambda is a compute service that runs code in response to triggers such as changes in data, changes in application state, or user actions. We use the following graph.

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AWS Machine Learning Blog

FEBRUARY 27, 2025

Analysis The final stage empowers healthcare data scientists with detailed analytical capabilities. Data ScientistGenerative AI, Amazon Bedrock, where he contributes to cutting edge innovations in foundational models and generative AI applications at AWS. About the Authors Adewale Akinfaderin is a Sr.

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AWS Machine Learning Blog

APRIL 17, 2024

Despite the availability of advanced distributed training libraries, it’s common for training and inference jobs to need hundreds of accelerators (GPUs or purpose-built ML chips such as AWS Trainium and AWS Inferentia ), and therefore tens or hundreds of instances. or later NPM version 10.0.0

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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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Analytics Vidhya

FEBRUARY 2, 2024

As the demand for data-driven insights continues to surge across industries, the role of data scientists has become increasingly crucial.

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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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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

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

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

OCTOBER 28, 2024

Clean up To avoid incurring future charges, delete all resources related to the SageMaker Model Monitor schedule by completing the following steps: Delete data capture and any ground truth data: ! aws s3 rm s3://{bucket}/{prefix_name}/model-monitor/data-capture/{predictor.endpoint_name} --recursive ! Raju Patil is a Sr.

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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, pkl format for deploying with AWS Sagemaker.

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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

DECEMBER 4, 2024

Generate accurate training data for SageMaker models – For model training, data scientists can use Tecton’s SDK within their SageMaker notebooks to retrieve historical features. You can also find Tecton at AWS re:Invent. You can view and create EMR clusters directly through the SageMaker notebook.

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Flipboard

NOVEMBER 20, 2024

Prerequisites Before proceeding with this tutorial, make sure you have the following in place: AWS account – You should have an AWS account with access to Amazon Bedrock. She speaks at internal and external conferences such AWS re:Invent, Women in Manufacturing West, YouTube webinars, and GHC 23. model in Amazon Bedrock.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

It offers an unparalleled suite of tools that cater to every stage of the ML lifecycle, from data preparation to model deployment and monitoring. Prerequisites Make sure you meet the following prerequisites: Make sure your SageMaker AWS Identity and Access Management (IAM) role has the AmazonSageMakerFullAccess permission policy attached.

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Data Science Dojo

FEBRUARY 20, 2023

It allows data scientists to build models that can automate specific tasks. SageMaker boosts machine learning model development with the power of AWS, including scalable computing, storage, networking, and pricing. AWS SageMaker also has a CLI for model creation and management.

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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. About the Author Xiong Zhou is a Senior Applied Scientist at AWS. His current area of research includes LLM evaluation and data generation.

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