Flipboard

DECEMBER 2, 2024

This long-awaited capability is a game changer for our customers using the power of AI and machine learning (ML) inference in the cloud. The scale down to zero feature presents new opportunities for how businesses can approach their cloud-based ML operations.

ML 148

ML ML AWS Machine Learning 148

NYU Center for Data Science

SEPTEMBER 28, 2023

Building on this momentum is a dynamic research group at the heart of CDS called the Machine Learning and Language (ML²) group. By 2020, ML² was a thriving community, primarily known for its recurring speaker series where researchers presented their work to peers. What does it mean to work in NLP in the age of LLMs?

Machine Learning 98

Machine Learning Machine Learning ML ML 98

Flipboard

NOVEMBER 6, 2024

We developed and validated a deep learning model designed to identify pneumoperitoneum in computed tomography images. when cases with a small amount of free air (total volume <10 ml) are excluded. Delays or misdiagnoses in detecting pneumoperitoneum can significantly increase mortality and morbidity.

Deep Learning 85

Deep Learning Deep Learning Algorithm ML 85

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.

ML 135

ML ML AWS Python 135

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.

Deep Learning 71

Deep Learning Deep Learning Machine Learning Machine Learning 71

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.

ML 123

ML ML AWS Data Warehouse 123

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.

AWS 109

AWS Clustering Deep Learning Deep Learning 109

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).

Deep Learning 79

Deep Learning Deep Learning ML ML 79

AWS Machine Learning Blog

MAY 10, 2023

Project Jupyter is a multi-stakeholder, open-source project that builds applications, open standards, and tools for data science, machine learning (ML), and computational science. Given the importance of Jupyter to data scientists and ML developers, AWS is an active sponsor and contributor to Project Jupyter.

ML 106

ML ML AWS AI 106

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.

AI 91

AI AI Machine Learning Machine Learning 91

AWS Machine Learning Blog

MAY 15, 2025

By using cutting-edge generative AI and deep learning technologies, Apoidea has developed innovative AI-powered solutions that address the unique needs of multinational banks. To further enhance the capabilities of specialized information extraction solutions, advanced ML infrastructure is essential.

AWS 98

AWS ML ML Machine Learning 98

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.

AWS 129

AWS Deep Learning Deep Learning ML 129

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?

ML 110

ML ML Machine Learning Machine Learning 110

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.

Python 109

Python Deep Learning Deep Learning Computer Science 109

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.

Artificial Intelligence 105

Artificial Intelligence Artificial Intelligence AI AI 105

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.

AWS 99

AWS ML ML Clustering 99

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.

AWS 110

AWS Deep Learning Deep Learning ML 110

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.

Deep Learning 111

Deep Learning Deep Learning Data Science Natural Language Processing 111

AWS Machine Learning Blog

FEBRUARY 29, 2024

Large-scale deep learning has recently produced revolutionary advances in a vast array of fields. is a startup dedicated to the mission of democratizing artificial intelligence technologies through algorithmic and software innovations that fundamentally change the economics of deep learning. Founded in 2021, ThirdAI Corp.

AWS 137

AWS Deep Learning Deep Learning ML 137

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

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

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.

AWS 126

AWS ML ML Machine Learning 126

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?

Machine Learning 152

Machine Learning Machine Learning Deep Learning Deep Learning 152

AWS Machine Learning Blog

APRIL 7, 2025

This approach allows for greater flexibility and integration with existing AI and machine learning (AI/ML) workflows and pipelines. By providing multiple access points, SageMaker JumpStart helps you seamlessly incorporate pre-trained models into your AI/ML development efforts, regardless of your preferred interface or workflow.

AWS 120

AWS Machine Learning Machine Learning ML 120

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.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

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?

ML 145

ML ML Data Scientist AWS 145

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.

Data Science 98

Data Science Data Scientist Computer Science Computer Science 98

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

ML 96

ML ML Python Machine Learning 96

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.

Data Science 98

Data Science Computer Science Computer Science Supervised Learning 98

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.

Data Science 98

Data Science Machine Learning Machine Learning Natural Language Processing 98

PyImageSearch

MAY 19, 2025

Text-to-Vector Conversion (Sentence Transformer Model) Inside OpenSearch, the neural search module passes the query text to a pre-trained Sentence Transformer model (from Hugging Face or another ML framework). Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated?

K-nearest Neighbors 91

K-nearest Neighbors AWS Deep Learning Deep Learning 91

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.

AI 98

AI AI Algorithm ML 98

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.

AWS 129

AWS ML ML Clustering 129

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.

AWS 117

AWS AI AI Data Scientist 117

AWS Machine Learning Blog

SEPTEMBER 6, 2023

Multi-model endpoints (MMEs) are a powerful feature of Amazon SageMaker designed to simplify the deployment and operation of machine learning (ML) models. This feature is particularly beneficial for deep learning and generative AI models that require accelerated compute.

AWS 134

AWS ML ML AI 134

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.

AWS 105

AWS Analytics Analytics Machine Learning 105

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.

AWS 124

AWS Python ML ML 124

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.

AWS 97

AWS Clustering Deep Learning Deep Learning 97