Marktechpost

OCTOBER 12, 2024

Machine Learning (ML) models have shown promising results in various coding tasks, but there remains a gap in effectively benchmarking AI agents’ capabilities in ML engineering. MLE-bench is a novel benchmark aimed at evaluating how well AI agents can perform end-to-end machine learning engineering.

Machine Learning 116

Machine Learning OpenAI ML Engineer ML 116

Marktechpost

MAY 30, 2024

Introduction to AI and Machine Learning on Google Cloud This course introduces Google Cloud’s AI and ML offerings for predictive and generative projects, covering technologies, products, and tools across the data-to-AI lifecycle. It includes labs on feature engineering with BigQuery ML, Keras, and TensorFlow.

Artificial Intelligence 132

Artificial Intelligence Artificial Intelligence BERT Computer Vision 132

Flipboard

APRIL 17, 2025

The AI Model Serving team supports a wide range of models for both traditional machine learning (ML) and generative AI including LLMs, multi-modal foundation models (FMs), speech recognition, and computer vision-based models. About the authors Sai Guruju is working as a Lead Member of Technical Staff at Salesforce.

Large Language Models 92

Large Language Models Deep Learning Machine Learning AI 92

Flipboard

SEPTEMBER 4, 2023

Envision yourself as an ML Engineer at one of the world’s largest companies. You make a Machine Learning (ML) pipeline that does everything, from gathering and preparing data to making predictions. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated?

Machine Learning 150

Machine Learning Computer Vision Deep Learning Python 150

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.

ML 88

ML Computer Vision Data Scientist Machine Learning 88

AWS Machine Learning Blog

APRIL 2, 2025

Rushabh Lokhande is a Senior Data & ML Engineer with AWS Professional Services Analytics Practice. He has 12 years of experience in full life cycle of machine learning, computer vision, and data science from sales support to end-to-end solution delivery specially in healthcare and life sciences vertical.

Large Language Models 106

Large Language Models LLM Machine Learning Big Data 106

AWS Machine Learning Blog

DECEMBER 10, 2024

Today, we are excited to announce that Pixtral 12B ( pixtral-12b-2409 ), a state-of-the-art vision language model (VLM) from Mistral AI that excels in both text-only and multimodal tasks, is available for customers through Amazon SageMaker JumpStart. With SageMaker JumpStart, you can deploy models in a secure environment.

ML 103

ML Generative AI Computer Vision ML Engineer 103

AWS Machine Learning Blog

FEBRUARY 11, 2025

is a state-of-the-art vision segmentation model designed for high-performance computer vision tasks, enabling advanced object detection and segmentation workflows. You can now use state-of-the-art model architectures, such as language models, computer vision models, and more, without having to build them from scratch.

Computer Vision 82

Computer Vision ML Python Automation 82

Flipboard

FEBRUARY 28, 2023

in Electrical Engineering and a B.S. More posts by this contributor 4 questions to ask before building a computer vision model During the past six months, we have witnessed some incredible developments in AI. He holds an S.M. in Applied Physics from Harvard University, an M.S. in Physics from Stanford University.

Generative AI 130

Generative AI ML Engineer Computer Vision Robotics 130

Heartbeat

JULY 17, 2023

[link] Transfer learning using pre-trained computer vision models has become essential in modern computer vision applications. In this article, we will explore the process of fine-tuning computer vision models using PyTorch and monitoring the results using Comet. Pre-trained models, such as VGG, ResNet.

Computer Vision 52

Computer Vision Auto-classification Deep Learning Machine Learning 52

AWS Machine Learning Blog

APRIL 7, 2025

You can use state-of-the-art model architecturessuch as language models, computer vision models, and morewithout having to build them from scratch. This approach allows for greater flexibility and integration with existing AI and machine learning (AI/ML) workflows and pipelines. On the endpoint details page, choose Delete.

Machine Learning 114

Machine Learning Large Language Models Python Automation 114

Flipboard

JUNE 20, 2023

Machine learning (ML) engineers have traditionally focused on striking a balance between model training and deployment cost vs. performance. This is important because training ML models and then using the trained models to make predictions (inference) can be highly energy-intensive tasks.

Machine Learning 123

Machine Learning BERT Deep Learning ML Engineer 123

Towards AI

JANUARY 6, 2025

Artificial Intelligence graduate certificate by STANFORD SCHOOL OF ENGINEERING Artificial Intelligence graduate certificate; taught by Andrew Ng, and other eminent AI prodigies; is a popular course that dives deep into the principles and methodologies of AI and related fields. Generative AI with LLMs course by AWS AND DEEPLEARNING.AI

Artificial Intelligence 105

Artificial Intelligence Artificial Intelligence Computer Vision Deep Learning 105

Viso.ai

FEBRUARY 11, 2024

About us: We are viso.ai, the creators of the end-to-end computer vision platform, Viso Suite. With Viso Suite, enterprises can get started using computer vision to solve business challenges without any code. Viso Suite : the only end-to-end computer vision platform Detectron2: What’s Inside?

Computer Vision 52

Computer Vision Robotics Deep Learning Large Language Models 52

Flipboard

OCTOBER 9, 2023

Getting Used to Docker for Machine Learning Introduction Docker is a powerful addition to any development environment, and this especially rings true for ML Engineers or enthusiasts who want to get started with experimentation without having to go through the hassle of setting up several drivers, packages, and more.

Machine Learning 150

Machine Learning Computer Vision Deep Learning Python 150

AWS Machine Learning Blog

DECEMBER 1, 2023

Launched in 2019, Amazon SageMaker Studio provides one place for all end-to-end machine learning (ML) workflows, from data preparation, building and experimentation, training, hosting, and monitoring. About the Authors Mair Hasco is an AI/ML Specialist for Amazon SageMaker Studio. She is also the author of a book on computer vision.

Machine Learning 123

Machine Learning ML DevOps Data Science 123

AWS Machine Learning Blog

FEBRUARY 12, 2025

About the authors Daniel Zagyva is a Senior ML Engineer at AWS Professional Services. As a next step, you can explore fine-tuning your own LLM with Medusa heads on your own dataset and benchmark the results for your specific use case, using the provided GitHub repository.

LLM 77

LLM ML Natural Language Processing Machine Learning 77

AWS Machine Learning Blog

APRIL 19, 2023

Businesses are increasingly using machine learning (ML) to make near-real-time decisions, such as placing an ad, assigning a driver, recommending a product, or even dynamically pricing products and services. As a result, some enterprises have spent millions of dollars inventing their own proprietary infrastructure for feature management.

ML 97

ML Data Scientist Big Data Data Science 97

AWS Machine Learning Blog

JUNE 27, 2023

When machine learning (ML) models are deployed into production and employed to drive business decisions, the challenge often lies in the operation and management of multiple models. They swiftly began to work on AI/ML capabilities by building image recognition models using Amazon SageMaker.

DevOps 116

DevOps ML Machine Learning ML Engineer 116

AWS Machine Learning Blog

NOVEMBER 16, 2023

Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.

Data Scientist 120

Data Scientist ML Data Science Machine Learning 120

AWS Machine Learning Blog

NOVEMBER 30, 2023

Code Editor is based on Code-OSS , Visual Studio Code Open Source, and provides access to the familiar environment and tools of the popular IDE that machine learning (ML) developers know and love, fully integrated with the broader SageMaker Studio feature set. Choose Open CodeEditor to launch the IDE.

Machine Learning 127

Machine Learning ML LLM Python 127

Pickl AI

JANUARY 29, 2024

2024 Tech breakdown: Understanding Data Science vs ML vs AI Quoting Eric Schmidt , the former CEO of Google, ‘There were 5 exabytes of information created between the dawn of civilisation through 2003, but that much information is now created every two days.’ AI comprises Natural Language Processing, computer vision, and robotics.

Data Science 52

Data Science ML Machine Learning Artificial Intelligence 52

AWS Machine Learning Blog

OCTOBER 2, 2023

A successful deployment of a machine learning (ML) model in a production environment heavily relies on an end-to-end ML pipeline. Although developing such a pipeline can be challenging, it becomes even more complex when dealing with an edge ML use case. This helps avoid costly defects at later stages of the production process.

DevOps 121

DevOps Automation ML Computer Vision 121

Towards AI

APRIL 27, 2023

Because selecting it judicially reduces the data movement, data processing computation, and data labeling costs downstream Then once the data is collected, synchronized, and selected, it needs to be labeled, which, again, no one from the AI team wants to do. SAM from Meta AI — the chatGPT moment for computer vision AI It’s a disruption.

Automation 98

Automation Auto-complete Computer Vision ChatGPT 98

AWS Machine Learning Blog

FEBRUARY 12, 2025

Machine learning (ML) engineers must make trade-offs and prioritize the most important factors for their specific use case and business requirements. He finds particular satisfaction in collaborating with customers to turn their ambitious technological visions into reality. Nitin Eusebius is a Sr.

Auto-classification 90

Auto-classification Generative AI Metadata Machine Learning 90

AWS Machine Learning Blog

MARCH 13, 2024

The audio moderation workflow uses Amazon Transcribe Toxicity Detection, which is a machine learning (ML)-powered capability that uses audio and text-based cues to identify and classify voice-based toxic content across seven categories, including sexual harassment, hate speech, threats, abuse, profanity, insults, and graphic language.

Natural Language Processing 135

Natural Language Processing LLM Prompt Engineer Prompt Engineering 135

AWS Machine Learning Blog

JULY 23, 2024

You can now use state-of-the-art model architectures, such as language models, computer vision models, and more, without having to build them from scratch. This approach allows for greater flexibility and integration with existing AI and ML workflows and pipelines. Deploy Llama 3.1

Machine Learning 126

Machine Learning Computer Vision Python ML 126

Pickl AI

JANUARY 23, 2023

Most of the organizations make use of Caffe in order to deal with computer vision and classification related problems. Theano Theano is one of the fastest and simplest ML libraries, and it was built on top of NumPy. Pros It has various algorithms and even ensemble features that help in prediction of several ML models.

Machine Learning 52

Machine Learning ML Neural Network Auto-classification 52

Flipboard

MARCH 13, 2023

With terabytes of data generated by the product, the security analytics team focuses on building machine learning (ML) solutions to surface critical attacks and spotlight emerging threats from noise. Solution overview The following diagram illustrates the ML platform architecture.

Data Scientist 123

Data Scientist Auto-complete Machine Learning ML 123

The MLOps Blog

NOVEMBER 14, 2023

This is where visualizations in ML come in. Visualizing deep learning models can help us with several different objectives: Interpretability and explainability: The performance of deep learning models is, at times, staggering, even for seasoned data scientists and ML engineers. Which one is right for you depends on your goal.

Deep Learning 111

Deep Learning Neural Network Convolutional Neural Networks Data Scientist 111

Towards AI

FEBRUARY 20, 2025

This week, we are introducing new frameworks through hands-on guides such as APDTFlow (addresses challenges with time series forecasting), NSGM (addresses variable selection and time-series network modeling), and MLFlow (streamlines ML workflows by tracking experiments, managing models, and more).

Neural Network 86

Neural Network Explainability LLM Software Development 86

AWS Machine Learning Blog

OCTOBER 4, 2024

About the Authors Akarsha Sehwag is a Data Scientist and ML Engineer in AWS Professional Services with over 5 years of experience building ML based solutions. Leveraging her expertise in Computer Vision and Deep Learning, she empowers customers to harness the power of the ML in AWS cloud efficiently.

Data Scientist 102

Data Scientist Generative AI Machine Learning ML 102

AWS Machine Learning Blog

SEPTEMBER 25, 2024

SageMaker JumpStart is a machine learning (ML) hub that provides access to algorithms, models, and ML solutions so you can quickly get started with ML. 11B Vision model using SageMaker JumpStart. This approach allows for greater flexibility and integration with existing AI/ML workflows and pipelines. models today.

Software Engineer 130

Software Engineer Software Development ML Python 130

AWS Machine Learning Blog

SEPTEMBER 29, 2023

In this post, we illustrate how to use a segmentation machine learning (ML) model to identify crop and non-crop regions in an image. Identifying crop regions is a core step towards gaining agricultural insights, and the combination of rich geospatial data and ML can lead to insights that drive decisions and actions.

Machine Learning 135

Machine Learning Data Scientist ML Python 135

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.

Machine Learning 98

Machine Learning ML Algorithm Data Scientist 98

Pickl AI

APRIL 19, 2023

Different industries from education, healthcare to marketing, retail and ecommerce require Machine Learning Engineers. Job market will experience a rise of 13% by 2026 for ML Engineers Why is Machine Learning Important? Accordingly, an entry-level ML engineer will earn around 5.1 Consequently.

Machine Learning 52

Machine Learning ML Engineer Data Scientist ML 52

AWS Machine Learning Blog

MARCH 22, 2023

Generating this data can take months to gather and require large teams of labelers to prepare it for use in machine learning (ML). Behind the scenes, Rekognition Custom Labels automatically loads and inspects the training data, selects the right ML algorithms, trains a model, and provides model performance metrics.

Automation 97

Automation Machine Learning ML Computer Vision 97