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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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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phData

MAY 17, 2024

The brand-new Forecasting tool created on Snowflake Data Cloud Cortex ML allows you to do just that. What is Cortex ML, and Why Does it Matter? Cortex ML is Snowflake’s newest feature, added to enhance the ease of use and low-code functionality of your business’s machine learning needs.

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Data Science Dojo

OCTOBER 25, 2023

Whether you are a researcher, developer, or simply curious, here are six ways to get your hands on the Llama 2 model right now: Understanding Llama2, Six Access Methods Download Llama 2 Model Since Llama 2 large language model is open-source, you can freely install it on your desktop and start using it.

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AWS Machine Learning Blog

MAY 10, 2023

Quick iteration and faster time-to-value can be achieved by providing these analysts with a visual business intelligence (BI) tool for simple analysis, supported by technologies like machine learning (ML). You can copy the prediction by choosing Copy , or download it by choosing Download prediction.

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AWS Machine Learning Blog

JUNE 7, 2023

You can now retrain machine learning (ML) models and automate batch prediction workflows with updated datasets in Amazon SageMaker Canvas , thereby making it easier to constantly learn and improve the model performance and drive efficiency. An ML model’s effectiveness depends on the quality and relevance of the data it’s trained on.

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PyImageSearch

DECEMBER 4, 2023

Home Table of Contents ML Days in Tashkent — Day 1: City Tour Arriving at Tashkent! This blog is the 1st of a 3-part series: ML Days in Tashkent — Day 1: City Tour (this tutorial) ML Days in Tashkent — Day 2: Sprints and Sessions ML Days in Tashkent — Day 3: Demos and Workshops ML Days in Tashkent — Day 1: City Tour Arriving at Tashkent!

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Mlearning.ai

APRIL 6, 2023

Automate and streamline our ML inference pipeline with SageMaker and Airflow Building an inference data pipeline on large datasets is a challenge many companies face. SageMaker Batch Job Allows you to run batch inference on large datasets and generate predictions in a batch mode using machine learning (ML) models hosted in SageMaker.

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PyImageSearch

DECEMBER 11, 2023

Kicking Off with a Keynote The second day of the Google Machine Learning Community Summit began with an inspiring keynote session by Soonson Kwon, the ML Community Lead at Google. The focus of his presentation was clear and forward-thinking: Accelerate AI/ML research and application.

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Mlearning.ai

FEBRUARY 16, 2023

This blog gives a detailed demonstration of how to download and preprocess stock OHLCV data into the form that we could use in the further steps. Install and Import Packages We will use the code provided by FinRL to download data from Yahoo Finance. 1 Download Data was originally published in MLearning.ai

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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

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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phData

MARCH 22, 2023

As companies continue to adopt machine learning (ML) in their workflows, the demand for scalable and efficient tools has increased. In this blog post, we will explore the performance benefits of Snowpark for ML workloads and how it can help businesses make better use of their data. Want to learn more? Can’t wait?

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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. This open-source library is renowned for its capabilities in numerical computation, particularly in large-scale machine learning projects.

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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. 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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The MLOps Blog

MAY 17, 2023

From data processing to quick insights, robust pipelines are a must for any ML system. Often the Data Team, comprising Data and ML Engineers , needs to build this infrastructure, and this experience can be painful. However, efficient use of ETL pipelines in ML can help make their life much easier.

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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. Instead, you can focus on the higher value-added effort of training jobs at scale in a shorter amount of time and iterating on your ML models faster.

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Heartbeat

JUNE 26, 2023

A guide to performing end-to-end computer vision projects with PyTorch-Lightning, Comet ML and Gradio Image by Freepik Computer vision is the buzzword at the moment. Today, I’ll walk you through how to implement an end-to-end image classification project with Lightning , Comet ML, and Gradio libraries.

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Smart Data Collective

AUGUST 16, 2022

In the vast majority of cases, the email looks like it’s from a legitimate source, but it actually contains malware that, once downloaded, can give the attacker access to the organization’s network. The post Can ML Fix Cybersecurity Challenges in Healthcare? Ransomware Attacks. appeared first on SmartData Collective.

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AWS Machine Learning Blog

JUNE 9, 2023

ONNX provides tools for optimizing and quantizing models to reduce the memory and compute needed to run machine learning (ML) models. One of the biggest benefits of ONNX is that it provides a standardized format for representing and exchanging ML models between different frameworks and tools.

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ODSC - Open Data Science

JULY 26, 2023

As a senior data scientist, I often encounter aspiring data scientists eager to learn about machine learning (ML). The ML Process The machine learning process typically consists of the following steps: Data Collection Gathering relevant data is the first step in the machine learning process.

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Heartbeat

MARCH 6, 2023

library(keras) library(cometr) library(tidyr) To download the Fashion MNIST dataset, add the following code to your R script. Ensure you have your API key from your Comet ML account, then create a .comet.yml First, let’s create a custom function to log losses to Comet ML after each step. comet.yml file in your working directory.

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Heartbeat

FEBRUARY 15, 2023

Add the code below to download the IMDB dataset that has 50K+ reviews for movies from the IMDB website. plot(history) Make sure you log the training loss and accuracy metrics to Comet ML. In addition, we logged some metrics like loss, accuracy, and epochs to Comet ML’s platform. Create a new R Script and call it train.R.

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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 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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Mlearning.ai

JULY 29, 2023

Submission Suggestions Deploy Open AI Whisper V2 Manage Endpoint — Azure ML was originally published in MLearning.ai In the example we extract the data from the json input and call the scikit-learn model's predict() method and return the result back """ #logging.info(data) inputs = base64.b64decode(data)

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KDnuggets

SEPTEMBER 20, 2019

This white paper provides the first-ever standard for managing risk in AI and ML, focusing on both practical processes and technical best practices “beyond explainability” alone. Download now.

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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. Give this technique a try to take your team’s ML modelling to the next level. Team How to determine the optimal team structure ?

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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. About the Authors Melanie Li is a Senior AI/ML Specialist TAM at AWS based in Sydney, Australia.

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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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AWS Machine Learning Blog

AUGUST 4, 2023

This completes the setup to enable data access from Salesforce Data Cloud to SageMaker Studio to build AI and machine learning (ML) models. In this step, we use some of these transformations to prepare the dataset for an ML model. Let’s look at the file without downloading it. Copy and paste the link into a new browser tab URL.

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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. You can now view the predictions and download them as CSV.

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The MLOps Blog

JANUARY 26, 2024

Luckily, we have tried and trusted tools and architectural patterns that provide a blueprint for reliable ML systems. In this article, I’ll introduce you to a unified architecture for ML systems built around the idea of FTI pipelines and a feature store as the central component. But what is an ML pipeline?

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AWS Machine Learning Blog

OCTOBER 9, 2023

Machine learning (ML) can analyze large volumes of product reviews and identify patterns, sentiments, and topics discussed. However, implementing ML can be a challenge for companies that lack resources such as ML practitioners, data scientists, or artificial intelligence (AI) developers. Set up SageMaker Canvas.

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Mlearning.ai

APRIL 24, 2023

Download the new Runway now. Text to Video Continue reading on MLearning.ai »

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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. SageMaker Processing provisions cluster resources for you to run city-, country-, or continent-scale geospatial ML workloads.

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Mlearning.ai

MAY 19, 2023

Here is HuggingFace Link: [link] From the Mosaic ML paper. Here is the HuggingFace Link: [link] From the Mosaic ML paper. This approach offers several benefits, including the elimination of the need to download the entire dataset before commencing training. This model was trained with 9.6M This model was trained with 86M tokens.

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