AWS Machine Learning Blog

DECEMBER 2, 2024

Container Caching addresses this scaling challenge by pre-caching the container image, eliminating the need to download it when scaling up. We discuss how this innovation significantly reduces container download and load times during scaling events, a major bottleneck in LLM and generative AI inference.

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Pickl AI

MARCH 21, 2025

Summary: Features of Python Programming Language is a versatile, beginner-friendly language known for its simple syntax, vast libraries, and cross-platform compatibility. With continuous updates and strong community support, Python remains a top choice for developers. Learn Python with Pickl.AI Learn Python with Pickl.AI

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AWS Machine Learning Blog

DECEMBER 24, 2024

source env_vars After setting your environment variables, download the lifecycle scripts required for bootstrapping the compute nodes on your SageMaker HyperPod cluster and define its configuration settings before uploading the scripts to your S3 bucket. The following is the bash script for the Python environment setup.

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Flipboard

AUGUST 17, 2023

Many practitioners are extending these Redshift datasets at scale for machine learning (ML) using Amazon SageMaker , a fully managed ML service, with requirements to develop features offline in a code way or low-code/no-code way, store featured data from Amazon Redshift, and make this happen at scale in a production environment.

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The MLOps Blog

MAY 10, 2023

When working on real-world machine learning (ML) use cases, finding the best algorithm/model is not the end of your responsibilities. Reusability & reproducibility: Building ML models is time-consuming by nature. Save vs package vs store ML models Although all these terms look similar, they are not the same.

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AWS Machine Learning Blog

JANUARY 29, 2025

You can customize the retry behavior using the AWS SDK for Python (Boto3) Config object. Yanyan Zhang is a Senior Generative AI Data Scientist at Amazon Web Services, where she has been working on cutting-edge AI/ML technologies as a Generative AI Specialist, helping customers use generative AI to achieve their desired outcomes.

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AWS Machine Learning Blog

FEBRUARY 19, 2024

A SageMaker MME dynamically loads models from Amazon Simple Storage Service (Amazon S3) when invoked, instead of downloading all the models when the endpoint is first created. If the model is already loaded on the container when invoked, then the download step is skipped and the model returns the inferences with low latency.

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Flipboard

MARCH 7, 2023

This post presents and compares options and recommended practices on how to manage Python packages and virtual environments in Amazon SageMaker Studio notebooks. Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models.

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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. Additionally, you can use AWS Lambda directly to expose your models and deploy your ML applications using your preferred open-source framework, which can prove to be more flexible and cost-effective.

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AWS Machine Learning Blog

MAY 31, 2023

PyTorch is a machine learning (ML) framework based on the Torch library, used for applications such as computer vision and natural language processing. One of the primary reasons that customers are choosing a PyTorch framework is its simplicity and the fact that it’s designed and assembled to work with Python.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

jpg", "prompt": "Which part of Virginia is this letter sent from", "completion": "Richmond"} SageMaker JumpStart SageMaker JumpStart is a powerful feature within the SageMaker machine learning (ML) environment that provides ML practitioners a comprehensive hub of publicly available and proprietary foundation models (FMs).

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Flipboard

JUNE 26, 2023

These techniques utilize various machine learning (ML) based approaches. In this post, we look at how we can use AWS Glue and the AWS Lake Formation ML transform FindMatches to harmonize (deduplicate) customer data coming from different sources to get a complete customer profile to be able to provide better customer experience.

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AWS Machine Learning Blog

SEPTEMBER 23, 2024

Machine learning (ML) projects are inherently complex, involving multiple intricate steps—from data collection and preprocessing to model building, deployment, and maintenance. To start our ML project predicting the probability of readmission for diabetes patients, you need to download the Diabetes 130-US hospitals dataset.

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Data Science Dojo

JUNE 14, 2023

Learn how the synergy of AI and ML algorithms in paraphrasing tools is redefining communication through intelligent algorithms that enhance language expression. Paraphrasing tools in AI and ML algorithms Machine learning is a subset of AI. Pegasus Transformer This is a part of the Transformers library available in Python 3.

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Data Science Dojo

JUNE 14, 2023

Learn how the synergy of AI and ML algorithms in paraphrasing tools is redefining communication through intelligent algorithms that enhance language expression. Paraphrasing tools in AI and ML algorithms Machine learning is a subset of AI. Pegasus Transformer This is a part of the Transformers library available in Python 3.

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AWS Machine Learning Blog

AUGUST 22, 2024

In this post, we show you how to convert Python code that fine-tunes a generative AI model in Amazon Bedrock from local files to a reusable workflow using Amazon SageMaker Pipelines decorators. You can use Amazon SageMaker Model Building Pipelines to collaborate between multiple AI/ML teams. Create and run the SageMaker pipeline.

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AWS Machine Learning Blog

JUNE 6, 2023

PyTorch is a machine learning (ML) framework that is widely used by AWS customers for a variety of applications, such as computer vision, natural language processing, content creation, and more. Our next generation release that is faster, more Pythonic and Dynamic as ever for details. With the recent PyTorch 2.0 Refer to PyTorch 2.0:

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Mlearning.ai

MAY 31, 2023

In this article, we will explore the process of creating a simple chatbot using Python and NLP techniques. Setting up the Development Environment To get started, we need to set up our Python development environment with the necessary libraries and tools. These techniques form the foundation for building intelligent chatbots.

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AWS Machine Learning Blog

OCTOBER 19, 2023

For many industries, data that is useful for machine learning (ML) may contain personally identifiable information (PII). This post demonstrates how to use Amazon SageMaker Data Wrangler and Amazon Comprehend to automatically redact PII from tabular data as part of your machine learning operations (ML Ops) workflow.

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Towards AI

JUNE 27, 2023

Let’s get started with the best machine learning (ML) developer tools: TensorFlow TensorFlow, developed by the Google Brain team, is one of the most utilized machine learning tools in the industry. PyTorch PyTorch, a Python-based machine learning library, stands out among its peers in the machine learning tools ecosystem.

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Towards AI

NOVEMBER 2, 2024

Model deployment is the process of making a model accessible and usable in production environments, where it can generate predictions and provide real-time insights to end-users and it’s an essential skill for every ML or AI engineer. 🤖 What is Detectron2? Image taken from the official Colab for Detectron2 training.

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AWS Machine Learning Blog

APRIL 7, 2025

Discover Llama 4 models in SageMaker JumpStart SageMaker JumpStart provides FMs through two primary interfaces: SageMaker Studio and the Amazon SageMaker Python SDK. Alternatively, you can use the SageMaker Python SDK to programmatically access and use SageMaker JumpStart models. b64encode(img).decode('utf-8')

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AWS Machine Learning Blog

APRIL 29, 2024

For AWS and Outerbounds customers, the goal is to build a differentiated machine learning and artificial intelligence (ML/AI) system and reliably improve it over time. Second, open source Metaflow provides the necessary software infrastructure to build production-grade ML/AI systems in a developer-friendly manner.

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Applied Data Science

AUGUST 2, 2021

Download the free, unabridged version here. The most common data science languages are Python and R   —  SQL is also a must have skill for acquiring and manipulating data. Machine Learning In this section, we look beyond ‘standard’ ML practices and explore the 6 ML trends that will set you apart from the pack in 2021.

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Mlearning.ai

AUGUST 11, 2023

ML Implementation — 00 I do not know how I will be proceeding with this project(s) but I plan to document it to some extent. The goal is to utilize ML-Agents with C# and Unity engine to make a couple of ML projects, obviously with visualization. Part 01 of ML Implementation. Until net time. Might take a while to run).

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AWS Machine Learning Blog

SEPTEMBER 29, 2023

In this post, we illustrate how to use a segmentation machine learning (ML) model to identify crop and non-crop regions in an image. Identifying crop regions is a core step towards gaining agricultural insights, and the combination of rich geospatial data and ML can lead to insights that drive decisions and actions.

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Flipboard

FEBRUARY 7, 2023

Amazon SageMaker Studio Lab provides no-cost access to a machine learning (ML) development environment to everyone with an email address. The following example illustrates Studio Lab running a Jupyter notebook that downloads TCIA prostate MRI data, segments it using MONAI, and displays the results using itkWidgets.

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AWS Machine Learning Blog

APRIL 19, 2023

Right now, most deep learning frameworks are built for Python, but this neglects the large number of Java developers and developers who have existing Java code bases they want to integrate the increasingly powerful capabilities of deep learning into. Business requirements We are the US squad of the Sportradar AI department.

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AWS Machine Learning Blog

MARCH 3, 2025

This design simplifies the complexity of distributed training while maintaining the flexibility needed for diverse machine learning (ML) workloads, making it an ideal solution for enterprise AI development. Download the prepared dataset that you uploaded to S3 into the FSx for Lustre volume attached to the cluster. instance_type: p4d.24xlarge.

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PyImageSearch

DECEMBER 18, 2023

This format made for a fast-paced and diverse showcase of ideas and applications in AI and ML. In just 3 minutes, each participant managed to highlight the core of their work, offering insights into the innovative ways in which AI and ML are being applied across various fields. But again, stick around for a surprise demo at the end. ?

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AWS Machine Learning Blog

OCTOBER 2, 2023

You can try out this model with SageMaker JumpStart, a machine learning (ML) hub that provides access to algorithms, models, and ML solutions so you can quickly get started with ML. What is SageMaker JumpStart With SageMaker JumpStart, ML practitioners can choose from a growing list of best-performing foundation models.

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Flipboard

FEBRUARY 15, 2023

Amazon SageMaker JumpStart is the machine learning (ML) hub of SageMaker that offers over 350 built-in algorithms, pre-trained models, and pre-built solution templates to help you get started with ML fast. We then use a pre-built MLOps template to bootstrap the ML workflow and provision a CI/CD pipeline with sample code.

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AWS Machine Learning Blog

JANUARY 9, 2023

Machine learning (ML) applications are complex to deploy and often require the ability to hyper-scale, and have ultra-low latency requirements and stringent cost budgets. Deploying ML models at scale with optimized cost and compute efficiencies can be a daunting and cumbersome task. Design patterns for building ML applications.

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Mlearning.ai

JULY 4, 2023

In this article I will show you how to run a version of the Vicuna model in WSL2 with GPU acceleration and prompt the model from Python via an API. The CUDA toolkit can be downloaded from the NVidia website. venv Using python venv is a personal preference — I like how lightweight it is. Simply run python download-model.py

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Flipboard

DECEMBER 11, 2024

Second, because data, code, and other development artifacts like machine learning (ML) models are stored within different services, it can be cumbersome for users to understand how they interact with each other and make changes. For Project profile , choose Data analytics and AI-ML model development. Choose Continue.

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AWS Machine Learning Blog

MAY 30, 2023

Since its introduction, we’ve helped hundreds of customers optimize their workloads, set guardrails, and improve the visibility of their machine learning (ML) workloads’ cost and usage. When an On-Demand job is launched, it goes through five phases: Starting, Downloading, Training, Uploading, and Completed.

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Flipboard

SEPTEMBER 4, 2023

Envision yourself as an ML Engineer at one of the world’s largest companies. You make a Machine Learning (ML) pipeline that does everything, from gathering and preparing data to making predictions. This is suitable for making a variety of Python applications with other dependencies being added to it at the user’s convenience.

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