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.

Computer Vision 52

Computer Vision Auto-classification Deep Learning Machine Learning 52

Viso.ai

FEBRUARY 11, 2024

Detectron2 is a deep learning model built on the Pytorch framework, which is said to be one of the most promising modular object detection libraries being pioneered. About us: We are viso.ai, the creators of the end-to-end computer vision platform, Viso Suite. It then teaches the model to “look” at them and “see” things.

Computer Vision 52

Computer Vision Robotics Deep Learning Large Language Models 52

AWS Machine Learning Blog

NOVEMBER 20, 2023

KT’s AI Food Tag is an AI-based dietary management solution that identifies the type and nutritional content of food in photos using a computer vision model. He conducted research on machine learning and deep learning, specifically on topics like hyperparameter optimization and domain adaptation, presenting algorithms and papers.

Deep Learning 121

Deep Learning Machine Learning ML Data Scientist 121

AWS Machine Learning Blog

SEPTEMBER 23, 2024

Throughout this exercise, you use Amazon Q Developer in SageMaker Studio for various stages of the development lifecycle and experience firsthand how this natural language assistant can help even the most experienced data scientists or ML engineers streamline the development process and accelerate time-to-value.

ML 90

ML Computer Vision Data Scientist Machine Learning 90

AWS Machine Learning Blog

JUNE 27, 2023

Earth.com’s leadership team recognized the vast potential of EarthSnap and set out to create an application that utilizes the latest deep learning (DL) architectures for computer vision (CV). We initiated a series of enhancements to deliver managed MLOps platform and augment ML engineering.

DevOps 115

DevOps ML Machine Learning ML Engineer 115

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 101

Data Scientist Generative AI Machine Learning ML 101

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 124

Data Scientist ML Data Science Automation 124

AWS Machine Learning Blog

FEBRUARY 12, 2025

About the authors Daniel Zagyva is a Senior ML Engineer at AWS Professional Services. He specializes in developing scalable, production-grade machine learning solutions for AWS customers. His experience extends across different areas, including natural language processing, generative AI and machine learning operations.

LLM 77

LLM ML Natural Language Processing Machine Learning 77

AWS Machine Learning Blog

JANUARY 25, 2023

Amazon SageMaker Clarify is a feature of Amazon SageMaker that enables data scientists and ML engineers to explain the predictions of their ML models. He has worked with organizations ranging from large enterprises to mid-sized startups on problems related to distributed computing, and Artificial Intelligence.

Explainability 97

Explainability Algorithm Natural Language Processing Machine Learning 97

The MLOps Blog

JUNE 27, 2023

MLOps workflows for computer vision and ML teams Use-case-centric annotations. Data storage and versioning You need data storage and versioning tools to maintain data integrity, enable collaboration, facilitate the reproducibility of experiments and analyses, and ensure accurate ML model development and deployment.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

Snorkel AI

MAY 4, 2023

Join us on June 7-8 to learn how to use your data to build your AI moat at The Future of Data-Centric AI 2023. The sessions at this year’s conference will focus on the following: Data development techniques: programmatic labeling, synthetic data, active learning, weak supervision, data cleaning, and augmentation.

Computer Vision 52

Computer Vision Large Language Models Natural Language Processing AI 52

Snorkel AI

MAY 4, 2023

Join us on June 7-8 to learn how to use your data to build your AI moat at The Future of Data-Centric AI 2023. The sessions at this year’s conference will focus on the following: Data development techniques: programmatic labeling, synthetic data, active learning, weak supervision, data cleaning, and augmentation.

Computer Vision 52

Computer Vision Large Language Models Natural Language Processing AI 52

The MLOps Blog

MAY 10, 2023

In this section, you will see different ways of saving machine learning (ML) as well as deep learning (DL) models. Note: The focus of this article is not to show you how you can create the best ML model but to explain how effectively you can save trained models. Now let’s see how we can save our model.

Python 105

Python Metadata ML Machine Learning 105

Pickl AI

JANUARY 23, 2023

It is mainly used for deep learning applications. PyTorch PyTorch is a popular, open-source, and lightweight machine learning and deep learning framework built on the Lua-based scientific computing framework for machine learning and deep learning algorithms.

Machine Learning 52

Machine Learning ML Neural Network Auto-classification 52

Heartbeat

OCTOBER 23, 2023

We will examine real-life applications where health informatics has outperformed traditional methods, discuss recent advances in the field, and highlight machine learning tools such as time series analysis with ARIMA and ARTXP that are transforming health informatics. We pay our contributors, and we don't sell ads.

Data Scientist 52

Data Scientist Machine Learning Big Data Algorithm 52

AWS Machine Learning Blog

OCTOBER 22, 2024

It accelerates your generative AI journey from prototype to production because you don’t need to learn about specialized workflow frameworks to automate model development or notebook execution at scale. You can learn more about the deep learning containers that are available on GitHub.

Automation 87

Automation LLM DevOps Python 87

AWS Machine Learning Blog

OCTOBER 2, 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 services and products. Leveraging her expertise in Computer Vision and Deep Learning, she empowers customers to harness the power of the ML in AWS cloud efficiently.

Generative AI 128

Generative AI Data Ingestion AI AI 128

ODSC - Open Data Science

JANUARY 10, 2024

Using Graphs for Large Feature Engineering Pipelines Wes Madrigal | ML Engineer | Mad Consulting This talk will outline the complexity of feature engineering from raw entity-level data, the reduction in complexity that comes with composable compute graphs, and an example of the working solution.

Large Language Models 52

Large Language Models Data Science Deep Learning LLM 52

ODSC - Open Data Science

JULY 19, 2023

Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team.

Machine Learning 52

Machine Learning Data Drift Data Science Data Scientist 52

AWS Machine Learning Blog

APRIL 25, 2023

This allows ML engineers and admins to configure these environment variables so data scientists can focus on ML model building and iterate faster. SageMaker uses training jobs to launch this function as a managed job. from sagemaker.remote_function import remote from numpy as np @remote(instance_type="ml.m5.large")

Machine Learning 101

Machine Learning Data Scientist Python ML 101

AWS Machine Learning Blog

JANUARY 13, 2023

At the application level, such as computer vision, natural language processing, and data mining, data scientists and engineers only need to write the model, data, and trainer in the same way as a standalone program and then pass it to the FedMLRunner object to complete all the processes, as shown in the following code.

Machine Learning 102

Machine Learning ML Algorithm Data Scientist 102

Heartbeat

OCTOBER 5, 2023

Integration with Other AI Technologies: LLMOps will collaborate with computer vision, speech recognition, and other AI domains, creating complex AI systems. We're committed to supporting and inspiring developers and engineers from all walks of life. In conclusion, LLMOps is at the forefront of the AI revolution.

Large Language Models 52

Large Language Models DevOps Machine Learning Prompt Engineer 52

Viso.ai

JANUARY 30, 2024

” We will cover the most important model training errors, such as: Overfitting and Underfitting Data Imbalance Data Leakage Outliers and Minima Data and Labeling Problems Data Drift Lack of Model Experimentation About us: At viso.ai, we offer the Viso Suite, the first end-to-end computer vision platform.

Data Drift 52

Data Drift Machine Learning Computer Vision Algorithm 52

AWS Machine Learning Blog

FEBRUARY 9, 2023

SageMaker now supports geospatial machine learning (ML), making it easier for data scientists and ML engineers to build, train, and deploy models using geospatial data. In this post, we showed how to acquire data, perform analysis, and visualize the changes with SageMaker geospatial AI/ML services.

Machine Learning 57

Machine Learning ML Deep Learning Robotics 57

AWS Machine Learning Blog

JUNE 13, 2023

This post is co-written with Jad Chamoun, Director of Engineering at Forethought Technologies, Inc. and Salina Wu, Senior ML Engineer at Forethought Technologies, Inc. He has worked with organizations ranging from large enterprises to mid-sized startups on problems related to distributed computing, and Artificial Intelligence.

Generative AI 93

Generative AI Auto-complete AI Modeling Machine Learning 93

Snorkel AI

JULY 5, 2023

Sheer volume—I think where this came about is when we had the rise of deep learning, there was a much larger volume of data used, and of course, we had big data that was driving a lot of that because we found ourselves with these mountains of data. So there are a lot of factors. But it’s really much more subtle.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

Snorkel AI

JULY 5, 2023

Sheer volume—I think where this came about is when we had the rise of deep learning, there was a much larger volume of data used, and of course, we had big data that was driving a lot of that because we found ourselves with these mountains of data. So there are a lot of factors. But it’s really much more subtle.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

Snorkel AI

JULY 5, 2023

Sheer volume—I think where this came about is when we had the rise of deep learning, there was a much larger volume of data used, and of course, we had big data that was driving a lot of that because we found ourselves with these mountains of data. So there are a lot of factors. But it’s really much more subtle.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

The MLOps Blog

OCTOBER 3, 2023

What helped me both in the transition to the data scientist role and then also to the MLOps engineer role was doing a combination of boot camps, and when I was going to the MLOps engineer role, I also took this one workshop that’s pretty well-known called Full Stack Deep Learning. I really enjoyed it.

ML 52

ML Data Scientist Machine Learning Data Science 52

BAIR

MARCH 11, 2024

Their work at BAIR, ranging from deep learning, robotics, and natural language processing to computer vision, security, and much more, has contributed significantly to their fields and has had transformative impacts on society. learning scenarios) for autonomous agents to improve generalization and sample efficiency.

Robotics 341

Robotics Machine Learning Natural Language Processing Deep Learning 341

BAIR

MARCH 11, 2024

Their work at BAIR, ranging from deep learning, robotics, and natural language processing to computer vision, security, and much more, has contributed significantly to their fields and has had transformative impacts on society. learning scenarios) for autonomous agents to improve generalization and sample efficiency.

Robotics 100

Robotics Machine Learning Natural Language Processing Deep Learning 100

AWS Machine Learning Blog

APRIL 19, 2023

About the Authors Mark Roy is a Principal Machine Learning Architect for AWS, helping customers design and build AI/ML solutions. Mark’s work covers a wide range of ML use cases, with a primary interest in feature stores, computer vision, deep learning, and scaling ML across the enterprise.

ML 98

ML Data Scientist Big Data Data Science 98

The MLOps Blog

AUGUST 11, 2023

At that point, the Data Scientists or ML Engineers become curious and start looking for such implementations. Advantages and disadvantages of embeddings design pattern The advantages of the embedding method of data representation in machine learning pipelines lie in its applicability to several ML tasks and ML pipeline components.

ML 52

ML Machine Learning Data Ingestion Deep Learning 52

Viso.ai

DECEMBER 21, 2023

Over the past decade, the field of computer vision has experienced monumental artificial intelligence (AI) breakthroughs. This blog will introduce you to the computer vision visionaries behind these achievements. Each of these individuals serves as an inspiration for aspiring AI and ML engineers breaking into the field.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 52

The MLOps Blog

MARCH 21, 2023

As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale. In this comprehensive guide, we’ll explore everything you need to know about machine learning platforms, including: Components that make up an ML platform.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

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. He is passionate about working with customers and is motivated by the goal of democratizing machine learning. Llama Guard is available on SageMaker JumpStart.

Machine Learning 126

Machine Learning Computer Vision Python ML 126

Snorkel AI

MAY 24, 2023

From generative modeling to automated product tagging, cloud computing, predictive analytics, and deep learning, the speakers present a diverse range of expertise. Within Wayfair, she is recognized as an expert in computer vision.

Machine Learning 52

Machine Learning Data Scientist Computer Vision ML 52