AWS Machine Learning Blog

MARCH 8, 2023

Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. Create a custom container image for ML model training and push it to Amazon ECR.

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IBM Journey to AI blog

JULY 6, 2023

These computer science terms are often used interchangeably, but what differences make each a unique technology? To keep up with the pace of consumer expectations, companies are relying more heavily on machine learning algorithms to make things easier. Machine learning is a subset of AI.

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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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Flipboard

JANUARY 16, 2025

& µg∕mL, 25.78& & µg∕mL, and ˃ 100& µg∕mL against non-small Lung cancer (A-549), Human Melanoma (A-375), and Human epidermoid Skin carcinoma (skin/epidermis) (A-431) respectively. & µg/ml respectively). & µg/ml respectively).

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Dataconomy

DECEMBER 18, 2024

Artificial Intelligence (AI) is a field of computer science focused on creating systems that perform tasks requiring human intelligence, such as language processing, data analysis, decision-making, and learning. Since DL falls under ML, this discussion will primarily focus on machine learning.

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Dataconomy

MAY 2, 2023

The machine learning systems developed by Machine Learning Engineers are crucial components used across various big data jobs in the data processing pipeline. Additionally, Machine Learning Engineers are proficient in implementing AI or ML algorithms. Is ML engineering a stressful job?

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AWS Machine Learning Blog

DECEMBER 24, 2024

Trainium chips are purpose-built for deep learning training of 100 billion and larger parameter models. Model training on Trainium is supported by the AWS Neuron SDK, which provides compiler, runtime, and profiling tools that unlock high-performance and cost-effective deep learning acceleration.

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Towards AI

JANUARY 6, 2025

Professional certificate for computer science for AI by HARVARD UNIVERSITY Professional certificate for computer science for AI is a 5-month AI course that is inclusive of self-paced videos for participants; who are beginners or possess intermediate-level understanding of artificial intelligence.

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AWS Machine Learning Blog

JUNE 11, 2024

release , you can now launch Neuron DLAMIs (AWS Deep Learning AMIs) and Neuron DLCs (AWS Deep Learning Containers) with the latest released Neuron packages on the same day as the Neuron SDK release. AWS DLCs provide a set of Docker images that are pre-installed with deep learning frameworks.

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AWS Machine Learning Blog

SEPTEMBER 9, 2024

To address customer needs for high performance and scalability in deep learning, generative AI, and HPC workloads, we are happy to announce the general availability of Amazon Elastic Compute Cloud (Amazon EC2) P5e instances, powered by NVIDIA H200 Tensor Core GPUs. 48xlarge sizes through Amazon EC2 Capacity Blocks for ML.

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AWS Machine Learning Blog

JUNE 25, 2024

This solution simplifies the integration of advanced monitoring tools such as Prometheus and Grafana, enabling you to set up and manage your machine learning (ML) workflows with AWS AI Chips. By deploying the Neuron Monitor DaemonSet across EKS nodes, developers can collect and analyze performance metrics from ML workload pods.

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PyImageSearch

JANUARY 13, 2025

Figure 13: Multi-Object Tracking for Pose Estimation (source: output video generated by running the above code) How to Train with YOLO11 Training a deep learning model is a crucial step in building a solution for tasks like object detection. When exporting, we can choose from formats like ONNX, TensorRT, Core ML, and more.

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ODSC - Open Data Science

FEBRUARY 17, 2023

Developing NLP tools isn’t so straightforward, and requires a lot of background knowledge in machine & deep learning, among others. Machine & Deep Learning Machine learning is the fundamental data science skillset, and deep learning is the foundation for NLP.

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Flipboard

SEPTEMBER 4, 2023

This lesson is the 1st of a 3-part series on Docker for Machine Learning : Getting Started with Docker for Machine Learning (this tutorial) Lesson 2 Lesson 3 Overview: Why the Need? Envision yourself as an ML Engineer at one of the world’s largest companies. Or requires a degree in computer science?

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ODSC - Open Data Science

APRIL 20, 2023

Deep Learning Approaches to Sentiment Analysis (with spaCy!) In this post, we’ll be demonstrating two deep learning approaches to sentiment analysis, specifically using spaCy. Deep Learning Approaches to Sentiment Analysis, Data Integrity, and Dolly 2.0

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AWS Machine Learning Blog

SEPTEMBER 5, 2023

In order to improve our equipment reliability, we partnered with the Amazon Machine Learning Solutions Lab to develop a custom machine learning (ML) model capable of predicting equipment issues prior to failure. We partnered with the AI/ML experts at the Amazon ML Solutions Lab for a 14-week development effort.

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PyImageSearch

JANUARY 2, 2023

This blog will cover the benefits, applications, challenges, and tradeoffs of using deep learning in healthcare. Computer Vision and Deep Learning for Healthcare Benefits Unlocking Data for Health Research The volume of healthcare-related data is increasing at an exponential rate.

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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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ODSC - Open Data Science

FEBRUARY 2, 2023

Just as a writer needs to know core skills like sentence structure, grammar, and so on, data scientists at all levels should know core data science skills like programming, computer science, algorithms, and so on. They’re looking for people who know all related skills, and have studied computer science and software engineering.

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AWS Machine Learning Blog

MARCH 21, 2025

JupyterLab applications flexible and extensive interface can be used to configure and arrange machine learning (ML) workflows. million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.

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NYU Center for Data Science

JULY 18, 2023

MaD & MaD+ The Math and Data (MaD) group is a collaboration between CDS and the NYU Courant Institute of Mathematical Sciences. Their work specializes in signal processing and inverse problems, machine learning and deep learning, and high-dimensional statistics and probability.

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NYU Center for Data Science

NOVEMBER 15, 2023

Will, Gunnar Behrens, Julius Busecke, Nora Loose, Charles Stern, Tom Beucler, Bryce Harrop, Benjamin Hillman, Andrea Jenney, Savannah L.

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Towards AI

AUGUST 24, 2023

Common mistakes and misconceptions about learning AI/ML Markus Spiske on Unsplash A common misconception of beginners is that they can learn AI/ML from a few tutorials that implement the latest algorithms, so I thought I would share some notes and advice on learning AI. Trying to code ML algorithms from scratch.

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AWS Machine Learning Blog

MAY 1, 2024

AWS Trainium instances for training workloads SageMaker ml.trn1 and ml.trn1n instances, powered by Trainium accelerators, are purpose-built for high-performance deep learning training and offer up to 50% cost-to-train savings over comparable training optimized Amazon Elastic Compute Cloud (Amazon EC2) instances.

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Becoming Human

MARCH 16, 2023

Since the advent of deep learning in the 2000s, AI applications in healthcare have expanded. Machine Learning Machine learning (ML) focuses on training computer algorithms to learn from data and improve their performance, without being explicitly programmed.

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AWS Machine Learning Blog

MAY 31, 2024

The Falcon 2 11B model is available on SageMaker JumpStart, a machine learning (ML) hub that provides access to built-in algorithms, FMs, and pre-built ML solutions that you can deploy quickly and get started with ML faster. She helps key customer accounts on their generative AI and AI/ML journeys.

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AWS Machine Learning Blog

MAY 2, 2023

SageMaker provides single model endpoints , which allow you to deploy a single machine learning (ML) model against a logical endpoint. Triton supports various backends as engines to support the running and serving of various ML models for inference. cp xgboost.json model_cpu_repository/fil/1/ !cp

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ODSC - Open Data Science

MARCH 13, 2023

As you know, ODSC East brings together some of the best and brightest minds in data science and AI. They are experts in machine learning, NLP, deep learning, data engineering, MLOps, and data visualization. His book, Deep Learning Illustrated , is a #1 bestseller and has been translated into six languages.

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ODSC - Open Data Science

JANUARY 24, 2023

One is data infrastructure, so just marshaling data from point A to point B and getting it from your production stream back into your training process in a way that is scalable, version controlled, auditable, and repeatable just in the same way that our offline ML workflows should be. The second challenge is evaluation.

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AWS Machine Learning Blog

JANUARY 13, 2023

Machine learning (ML) presents an opportunity to address some of these concerns and is being adopted to advance data analytics and derive meaningful insights from diverse HCLS data for use cases like care delivery, clinical decision support, precision medicine, triage and diagnosis, and chronic care management.

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ODSC - Open Data Science

MARCH 30, 2023

Using Azure ML to Train a Serengeti Data Model, Fast Option Pricing with DL, and How To Connect a GPU to a Container Using Azure ML to Train a Serengeti Data Model for Animal Identification In this article, we will cover how you can train a model using Notebooks in Azure Machine Learning Studio.

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Flipboard

JULY 10, 2023

Figure 4: Architecture of fully connected autoencoders (source: Amor, “Comprehensive introduction to Autoencoders,” ML Cheat Sheet , 2021 ). Figure 5: Architecture of Convolutional Autoencoder for Image Segmentation (source: Bandyopadhyay, “Autoencoders in Deep Learning: Tutorial & Use Cases [2023],” V7Labs , 2023 ).

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Flipboard

DECEMBER 29, 2024

Seven Machine Learning (ML) and Deep Learning (DL) models were developed for various scenarios. Moreover, two ensemble learning models were developed from the proposed ML and DL models. The best hyperparameters for all models were fine-tuned.

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AWS Machine Learning Blog

SEPTEMBER 13, 2024

Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and confidently build, train, and deploy ML models into a production-ready hosted environment. Multimodal is a type of deep learning using multiple modalities of data, such as text, audio, or images.

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Dataconomy

FEBRUARY 22, 2023

The systems can only “understand” specific situations they have been exposed to in the past and are unable to generalize in a way that humans find easy, said Stuart Russell, a computer science professor at the University of California, Berkeley.

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AWS Machine Learning Blog

MARCH 30, 2023

In this post, we introduce a new interactive tool called Snapper, powered by a machine learning (ML) model that reduces the effort required of annotators. ML model optimized for annotators A tremendous number of high-performing object detection models have been proposed by the computer vision community in recent years.

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AWS Machine Learning Blog

JUNE 7, 2023

Libraries such as DeepSpeed (an open-source deep learning optimization library for PyTorch) address some of these challenges, and can help accelerate model development and training. RJ is an engineer in Search M5 team leading the efforts for building large scale deep learning systems for training and inference.

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