ML @ CMU

NOVEMBER 7, 2024

In close collaboration with the UN and local NGOs, we co-develop an interpretable predictive tool for landmine contamination to identify hazardous clusters under geographic and budget constraints, experimentally reducing false alarms and clearance time by half. RELand consistently outperforms the benchmark models on all relevant metrics.

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AWS Machine Learning Blog

SEPTEMBER 19, 2023

Amazon SageMaker Feature Store provides an end-to-end solution to automate feature engineering for machine learning (ML). For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training. SageMaker Studio set up.

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Towards AI

FEBRUARY 20, 2024

Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction Everyone is using mobile or web applications which are based on one or other machine learning algorithms. Machine learning(ML) is evolving at a very fast pace. Photo by Andrea De Santis on Unsplash So, What is Machine Learning?

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phData

JANUARY 30, 2024

Snowpark ML is transforming the way that organizations implement AI solutions. Snowpark allows ML models and code to run on Snowflake warehouses. By “bringing the code to the data,” we’ve seen ML applications run anywhere from 4-100x faster than other architectures.

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AWS Machine Learning Blog

JUNE 11, 2024

This allows machine learning (ML) practitioners to rapidly launch an Amazon Elastic Compute Cloud (Amazon EC2) instance with a ready-to-use deep learning environment, without having to spend time manually installing and configuring the required packages. You also need the ML job scripts ready with a command to invoke them.

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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. SageMaker is a fully managed service for building, training, and deploying ML models.

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O'Reilly Media

OCTOBER 19, 2021

This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. What does a modern technology stack for streamlined ML processes look like?

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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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The MLOps Blog

JUNE 13, 2024

As a result, poor code quality and reliance on manual workflows are two of the main issues in ML development processes. Using the following three principles helps you build a mature ML development process: Establish a standard repository structure you can use as a scaffold for your projects. What is a mature ML development process?

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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. Cluster resources are provisioned for the duration of your job, and cleaned up when a job is complete. You can easily extend this solution to add more functionality.

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Mlearning.ai

APRIL 15, 2023

How Clustering Can Help You Understand Your Customers Better Customer segmentation is crucial for businesses to better understand their customers, target marketing efforts, and improve satisfaction. Clustering, a popular machine learning technique, identifies patterns in large datasets to group similar customers and gain insights.

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AWS Machine Learning Blog

JUNE 7, 2023

Training setup We provisioned a managed compute cluster comprised of 16 dl1.24xlarge instances using AWS Batch. We developed an AWS Batch workshop that illustrates the steps to set up the distributed training cluster with AWS Batch. The distributed training workshop illustrates the steps to set up the distributed training cluster.

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Mlearning.ai

JUNE 29, 2023

Photo by Aditya Chache on Unsplash DBSCAN in Density Based Algorithms : Density Based Spatial Clustering Of Applications with Noise. Earlier Topics: Since, We have seen centroid based algorithm for clustering like K-Means.Centroid based : K-Means, K-Means ++ , K-Medoids. & One among the many density based algorithms is “DBSCAN”.

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AWS Machine Learning Blog

OCTOBER 16, 2023

As an AI-powered solution, Veriff needs to create and run dozens of machine learning (ML) models in a cost-effective way. Infrastructure and development challenges Veriff’s backend architecture is based on a microservices pattern, with services running on different Kubernetes clusters hosted on AWS infrastructure.

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AWS Machine Learning Blog

SEPTEMBER 8, 2023

The IDP CDK constructs and samples are a collection of components to enable definition of IDP processes on AWS and published to GitHub. Another metrics to monitor is the health of the OpenSearch cluster, which you should setup according to the Opernational best practices for Amazon OpenSearch Service.

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AWS Machine Learning Blog

APRIL 16, 2024

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them.

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AWS Machine Learning Blog

JANUARY 24, 2024

Text representation with Embed – Developers can access endpoints that capture the semantic meaning of text, enabling applications such as vector search engines, text classification and clustering, and more. Next, you set up a Weaviate cluster. Subscribe to the Weaviate Kubernetes Cluster on AWS Marketplace.

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AWS Machine Learning Blog

AUGUST 21, 2023

Amazon SageMaker Data Wrangler reduces the time it takes to collect and prepare data for machine learning (ML) from weeks to minutes. Account A is the data lake account that houses all the ML-ready data obtained through extract, transform, and load (ETL) processes. An EMR cluster with EMR runtime roles enabled. compute.internal.

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AWS Machine Learning Blog

APRIL 12, 2023

This step-function instantiated a cluster of instances to extract and process data from S3 and the further steps of pre-processing, training, evaluation would run on a single large EC2 instance. This became a bottleneck in troubleshooting, adding, or removing a step, or even in making some small changes in the overall infrastructure.

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AWS Machine Learning Blog

MAY 30, 2024

An Amazon OpenSearch Service cluster stores the extracted video metadata and facilitates users’ search and discovery needs. Building a robust solution to extract information from videos poses challenges from both machine learning (ML) and engineering perspectives. Classify the video into IAB categories.

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O'Reilly Media

JANUARY 17, 2023

This mindset has followed me into my work in ML/AI. Because if companies use code to automate business rules, they use ML/AI to automate decisions. Given that, what would you say is the job of a data scientist (or ML engineer, or any other such title)? But first, let’s talk about the typical ML workflow.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). ML is often associated with PBAs, so we start this post with an illustrative figure. The ML paradigm is learning followed by inference. The union of advances in hardware and ML has led us to the current day.

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The MLOps Blog

AUGUST 11, 2023

There comes a time when every ML practitioner realizes that training a model in Jupyter Notebook is just one small part of the entire project. At that point, the Data Scientists or ML Engineers become curious and start looking for such implementations. What are ML pipeline architecture design patterns?

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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. It plays a crucial role in every model’s development process and allows data scientists to focus on the most promising ML techniques. This logical grouping is required when creating the HPO job.

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The MLOps Blog

OCTOBER 3, 2023

This article was originally an episode of the ML Platform Podcast , a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals. Nice to have you here, Miki.

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AWS Machine Learning Blog

APRIL 17, 2023

You can integrate a Data Wrangler data preparation flow into your machine learning (ML) workflows to simplify data preprocessing and feature engineering, taking data preparation to production faster without the need to author PySpark code, install Apache Spark, or spin up clusters. He is very passionate about data-driven AI.

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AWS Machine Learning Blog

JULY 13, 2023

Amazon SageMaker distributed training jobs enable you with one click (or one API call) to set up a distributed compute cluster, train a model, save the result to Amazon Simple Storage Service (Amazon S3), and shut down the cluster when complete. We included the steps to achieve this in the last section of the notebook.

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AWS Machine Learning Blog

MAY 16, 2024

Problem definition Traditionally, the recommendation service was mainly provided by identifying the relationship between products and providing products that were highly relevant to the product selected by the customer. Gonsoo Moon is an AWS AI/ML Specialist Solutions Architect and provides AI/ML technical support.

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IBM Journey to AI blog

SEPTEMBER 20, 2023

Foundational models (FMs) are marking the beginning of a new era in machine learning (ML) and artificial intelligence (AI) , which is leading to faster development of AI that can be adapted to a wide range of downstream tasks and fine-tuned for an array of applications. IBM watsonx consists of the following: IBM watsonx.ai

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AWS Machine Learning Blog

AUGUST 22, 2023

Machine learning (ML) is revolutionizing solutions across industries and driving new forms of insights and intelligence from data. Many ML algorithms train over large datasets, generalizing patterns it finds in the data and inferring results from those patterns as new unseen records are processed. What is federated learning?

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DagsHub

APRIL 7, 2024

These pipelines cover the entire lifecycle of an ML project, from data ingestion and preprocessing, to model training, evaluation, and deployment. Adopted from [link] In this article, we will first briefly explain what ML workflows and pipelines are. around the world to streamline their data and ML pipelines.

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Towards AI

JUNE 30, 2024

This set of 3 numbers is a 3-dimensional representation or embedding for the visual quality (based on our definition) of the images. Training an Embedding Model Now that we know how to interpret embeddings let's see how we would go about training an ML model to generate embeddings.

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Mlearning.ai

JULY 22, 2023

__version__ Let's try clustering a sample dataset and compare the runtime of clustering functions by running it with CPU and then with GPU. host_data = device_data.get() host_labels = device_labels.get() Running KMeans clustering on CPU. . Hope you will definitely give it a try. Import the packages. The CPU took 5.15

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AWS Machine Learning Blog

SEPTEMBER 11, 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. Falcon 180B was trained by TII on Amazon SageMaker , on a cluster of approximately 4K A100 GPUs.

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ML @ CMU

MAY 12, 2023

Finetune : a common baseline for model personalization; IFCA / HypCluster : hard clustering of client models; Ditto : a recently proposed method for personalized FL. Graphs generally complicate DP : we are often used to ML settings where we can clearly define the privacy granularity and how it relates to an actual individual (e.g.

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Pickl AI

OCTOBER 24, 2024

Summary: This article compares Artificial Intelligence (AI) vs Machine Learning (ML), clarifying their definitions, applications, and key differences. While AI aims to replicate human intelligence across various domains, ML focuses on learning from data to improve performance. What is Machine Learning?

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Mlearning.ai

JULY 9, 2023

I realized that the algorithm assumes that we like a particular genre and artist and groups us into these clusters, not letting us discover and experience new music. It gives us this final result: Conclusion The app definitely isn’t perfect. While scrolling through my recommended playlist.

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