ML @ CMU

APRIL 9, 2025

Thus far, over 11,000 users have downloaded Copilot Arena, and the tool has served over 100K completions, and accumulated over 25,000 code completion battles. In contrast, Copilot Arena users are working on a diverse set of realistic tasks, including but not limited to frontend components, backend logic, and ML pipelines.

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Python ML ML Algorithm 180

AWS Machine Learning Blog

JANUARY 21, 2025

In this post, we show you how Amazon Web Services (AWS) helps in solving forecasting challenges by customizing machine learning (ML) models for forecasting. This visual, point-and-click interface democratizes ML so users can take advantage of the power of AI for various business applications. One of these methods is quantiles.

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ML ML Algorithm AWS 102

AWS Machine Learning Blog

FEBRUARY 11, 2025

GraphStorm is a low-code enterprise graph machine learning (ML) framework that provides ML practitioners a simple way of building, training, and deploying graph ML solutions on industry-scale graph data. To download and preprocess the data as an Amazon SageMaker Processing step, use the following code. million edges.

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AWS Python ML ML 113

AWS Machine Learning Blog

NOVEMBER 13, 2024

To upload the dataset Download the dataset : Go to the Shoe Dataset page on Kaggle.com and download the dataset file (350.79MB) that contains the images. To search against the database, you can use a vector search, which is performed using the k-nearest neighbors (k-NN) algorithm. b64encode(image_file.read()).decode('utf-8')

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AWS Database K-nearest Neighbors AI 120

AWS Machine Learning Blog

JANUARY 31, 2025

These models are trained using self-supervised learning algorithms on expansive datasets, enabling them to capture a comprehensive repertoire of visual representations and patterns inherent within pathology images. script that automatically downloads and organizes the data in your EFS storage. Our repository includes a download_mhist.sh

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Flipboard

APRIL 23, 2025

When processing is triggered, endpoints are automatically initialized and model artifacts are downloaded from Amazon S3. Extractive summarization: The extractive summarization process employs the TextRank algorithm, powered by sumy and NLTK libraries, to identify and extract the most significant sentences from source documents.

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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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ML ML AWS Deep Learning 132

AWS Machine Learning Blog

NOVEMBER 15, 2023

AutoML allows you to derive rapid, general insights from your data right at the beginning of a machine learning (ML) project lifecycle. Understanding up front which preprocessing techniques and algorithm types provide best results reduces the time to develop, train, and deploy the right model.

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Algorithm AWS ML ML 124

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. This provides a major flexibility advantage over the majority of ML frameworks, which require neural networks to be defined as static objects before runtime.

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ML ML AWS Natural Language Processing 123

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. On the Studio console, choose AutoML in the navigation pane.

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ML ML Data Scientist AWS 96

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 ML ML ETL 123

Applied Data Science

AUGUST 2, 2021

Download the free, unabridged version here. 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. This allows for a much richer interpretation of predictions, without sacrificing the algorithm’s power.

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Data Science Data Scientist ML ML 130

IBM Data Science in Practice

NOVEMBER 21, 2022

Leverage the Watson NLP library to build the best classification models by combining the power of classic ML, Deep Learning, and Transformed based models. In this blog, you will walk through the steps of building several ML and Deep learning-based models using the Watson NLP library. So, let’s get started with this. sample(frac=0.8,

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Deep Learning Deep Learning Exploratory Data Analysis ML 130

AWS Machine Learning Blog

OCTOBER 10, 2023

Machine learning (ML), especially deep learning, requires a large amount of data for improving model performance. It is challenging to centralize such data for ML due to privacy requirements, high cost of data transfer, or operational complexity. The ML framework used at FL clients is TensorFlow.

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AWS ML ML Algorithm 124

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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Machine Learning Machine Learning ML ML 105

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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ML ML Python AWS 112

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. I gained a couple of badges and a lot of skills while doing these, but still, the goal was to have a proper implementation of the Machine Learning Algorithm. Part 01 of ML Implementation. Until net time.

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ML ML Machine Learning Machine Learning 52

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. Primarily known for its tree-based model training algorithm, XGBoost prioritizes optimizing performance and is especially potent […]

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Machine Learning Machine Learning ML ML 98

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. With SageMaker training jobs, you can bring your own algorithm or choose from more than 25 built-in algorithms.

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AWS Machine Learning Blog

JULY 29, 2024

Amazon Forecast is a fully managed service that uses statistical and machine learning (ML) algorithms to deliver highly accurate time series forecasts. With SageMaker Canvas, you get faster model building , cost-effective predictions, advanced features such as a model leaderboard and algorithm selection, and enhanced transparency.

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ML ML Algorithm AWS 123

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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ML ML AWS Deep Learning 98

AWS Machine Learning Blog

MAY 9, 2023

Amazon SageMaker provides a number of options for users who are looking for a solution to host their machine learning (ML) models. For that use case, SageMaker provides SageMaker single model endpoints (SMEs), which allow you to deploy a single ML model against a logical endpoint.

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Python ML ML Deep Learning 109

AWS Machine Learning Blog

NOVEMBER 15, 2024

Amazon SageMaker is a comprehensive, fully managed machine learning (ML) platform that revolutionizes the entire ML workflow. It offers an unparalleled suite of tools that cater to every stage of the ML lifecycle, from data preparation to model deployment and monitoring. jpg") or doc.endswith(".png")) b64encode(fIn.read()).decode("utf-8")

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AWS Computer Science Computer Science Database 112

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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ML ML Deep Learning Deep Learning 70

AWS Machine Learning Blog

MAY 8, 2024

Amazon SageMaker JumpStart provides a suite of built-in algorithms , pre-trained models , and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning.

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Algorithm Machine Learning Machine Learning ML 127

AWS Machine Learning Blog

OCTOBER 9, 2024

Amazon Lookout for Metrics is a fully managed service that uses machine learning (ML) to detect anomalies in virtually any time-series business or operational metrics—such as revenue performance, purchase transactions, and customer acquisition and retention rates—with no ML experience required. To learn more, see the documentation.

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AWS Machine Learning Blog

MAY 8, 2023

SageMaker provides single model endpoints (SMEs), which allow you to deploy a single ML model, or multi-model endpoints (MMEs), which allow you to specify multiple models to host behind a logical endpoint for higher resource utilization. To get started with MME support for GPU, see Supported algorithms, frameworks, and instances.

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ML ML Deep Learning Deep Learning 102

AWS Machine Learning Blog

JANUARY 27, 2025

Amazon SageMake r provides a seamless experience for building, training, and deploying machine learning (ML) models at scale. file Now that you have downloaded the complete inference.py compressing, and S3 upload Before you start this step, download the Prithvi model artifacts and the Prithvi flood fine-tuning of the model.

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AWS Deep Learning Deep Learning Machine Learning 111

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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ML ML AWS Natural Language Processing 162

O'Reilly Media

MAY 13, 2025

If theres more than one algorithm that might work, you need to tell the model which algorithm to use (if you care, and you may not). Algorithm choice can be an issue. If I told the model I wanted to use the Miller-Rabin algorithm , I got it, with only minor bugs. Thats nowhere near good enough.

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Algorithm AI AI Python 66

AWS Machine Learning Blog

AUGUST 4, 2023

Although we use a specific algorithm to train the model in our example, you can use any algorithm that you find appropriate for your use case. This completes the setup to enable data access from Salesforce Data Cloud to SageMaker Studio to build AI and machine learning (ML) models. Let’s look at the file without downloading it.

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ML ML AWS AI 98

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 ML ML Deep Learning 132

AWS Machine Learning Blog

MARCH 21, 2025

JupyterLab applications flexible and extensive interface can be used to configure and arrange machine learning (ML) workflows. We download the documents and store them under a samples folder locally. He is passionate about applying cloud technologies and ML to solve real life problems. samples/2003.10304/page_0.png'

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AWS Machine Learning Blog

JULY 18, 2023

The built-in BlazingText algorithm offers optimized implementations of Word2vec and text classification algorithms. We walk you through the following steps to set up our spam detector model: Download the sample dataset from the GitHub repo. With the BlazingText algorithm, you can scale to large datasets.

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Supervised Learning Algorithm Natural Language Processing AWS 98

Smart Data Collective

JULY 4, 2021

Machine learning (ML) is a form of AI that is becoming more widely used in the market because of the rising number of AI vendors in the banking industry. Machine learning is also an asset manager’s aid as it triggers algorithms to help analyze data sets and make predictions possible. Data Analysis. For Non-Tech Users.

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Machine Learning Machine Learning Natural Language Processing Artificial Intelligence 143

AWS Machine Learning Blog

MARCH 15, 2024

Many organizations are implementing machine learning (ML) to enhance their business decision-making through automation and the use of large distributed datasets. With increased access to data, ML has the potential to provide unparalleled business insights and opportunities.

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AWS ML ML Machine Learning 140

AWS Machine Learning Blog

MAY 25, 2023

Amazon Kendra is a highly accurate and intelligent search service that enables users to search unstructured and structured data using natural language processing (NLP) and advanced search algorithms. Abhijit Kalita is a Senior AI/ML Evangelist at Amazon Web Services. Clone the GitHub repo to create the container image: app.py

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ML ML AWS Database 101