Chatbots Life

MAY 16, 2023

Services : AI Solution Development, ML Engineering, Data Science Consulting, NLP, AI Model Development, AI Strategic Consulting, Computer Vision. Data Monsters can help companies deploy, train and test machine learning pipelines for natural language processing and computer vision.

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Generative AI Chatbots Conversational AI Software Development 92

AWS Machine Learning Blog

MARCH 13, 2024

The traditional method of training an in-house classification model involves cumbersome processes such as data annotation, training, testing, and model deployment, requiring the expertise of data scientists and ML engineers. LLMs, in contrast, offer a high degree of flexibility.

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Natural Language Processing LLM Prompt Engineer Prompt Engineering 136

Allen AI

NOVEMBER 8, 2023

Patrick Beukema is the Lead ML Engineer for Skylight Patrick Beukema is the Lead ML Engineer for Skylight. Later this month, we will be adding a third real-time satellite computer vision service for vessel detection using the Sentinel-2 optical imagery from the European Space Agency.

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Computer Vision ML Engineer ML Neural Network 52

The MLOps Blog

NOVEMBER 14, 2023

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. Data scientists and ML engineers: Creating and training deep learning models is no easy feat.

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Deep Learning Neural Network Convolutional Neural Networks Data Scientist 111

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

NOVEMBER 30, 2023

In this example, Code Editor can be used by an ML engineering team who needs advanced IDE features to debug their code and deploy the endpoint. He has worked on projects in different domains, including MLOps, computer vision, and NLP, involving a broad set of AWS services.

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Machine Learning ML LLM Software Engineer 126

Snorkel AI

MAY 4, 2023

Reinforcement learning has shown great promise in mastering complex games and decision-making tasks, while computer vision has progressed rapidly, allowing for more accurate image recognition, object detection, and scene understanding. Enterprise use cases: predictive AI, generative AI, NLP, computer vision, conversational AI.

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Snorkel AI

MAY 4, 2023

Reinforcement learning has shown great promise in mastering complex games and decision-making tasks, while computer vision has progressed rapidly, allowing for more accurate image recognition, object detection, and scene understanding. Enterprise use cases: predictive AI, generative AI, NLP, computer vision, conversational AI.

Computer Vision 52

Computer Vision Large Language Models Natural Language Processing AI 52

Flipboard

MARCH 13, 2023

After meticulous analysis of the evaluation results, the data scientist or ML engineer can deploy the new model if the performance of the newly trained model is better compared to the previous version. He is passionate about recommendation systems, NLP, and computer vision areas in AI and ML.

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Data Scientist Auto-complete Machine Learning ML 123

Heartbeat

OCTOBER 23, 2023

The Role of Data Scientists and ML Engineers in Health Informatics At the heart of the Age of Health Informatics are data scientists and ML engineers who play a critical role in harnessing the power of data and developing intelligent algorithms.

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Data Scientist Machine Learning Big Data Algorithm 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

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Snorkel AI

OCTOBER 12, 2023

The center aimed to address recurring bottlenecks in their ML projects and improve collaborative workflows between data scientists and subject-matter experts. In this presentation, center NLP Engineer James Dunham shares takeaways from the half-dozen project teams who used Snorkel in the past year.

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Data Scientist ML Computer Vision AI 64

Snorkel AI

JUNE 9, 2023

Applying weak supervision and foundation models for computer vision Snorkel AI Machine Learning Research Scientist Ravi Teja Mullapudi discussed the latest advancements in computer vision, focusing on the use of weak supervision and foundation models.

Large Language Models 52

Large Language Models Data Scientist Machine Learning Computer Vision 52

Snorkel AI

JUNE 9, 2023

Applying weak supervision and foundation models for computer vision Snorkel AI Machine Learning Research Scientist Ravi Teja Mullapudi discussed the latest advancements in computer vision, focusing on the use of weak supervision and foundation models.

Large Language Models 52

Large Language Models Data Scientist Machine Learning Computer Vision 52

Snorkel AI

OCTOBER 12, 2023

The center aimed to address recurring bottlenecks in their ML projects and improve collaborative workflows between data scientists and subject-matter experts. In this presentation, center NLP Engineer James Dunham shares takeaways from the half-dozen project teams who used Snorkel in the past year.

Data Scientist 59

Data Scientist ML Computer Vision AI 59

Snorkel AI

OCTOBER 12, 2023

The center aimed to address recurring bottlenecks in their ML projects and improve collaborative workflows between data scientists and subject-matter experts. In this presentation, center NLP Engineer James Dunham shares takeaways from the half-dozen project teams who used Snorkel in the past year.

Data Scientist 52

Data Scientist NLP ML Computer Vision 52

Heartbeat

OCTOBER 5, 2023

These LLMs can generate human-like text, understand context, and perform various Natural Language Processing (NLP) tasks. Integration with Other AI Technologies: LLMOps will collaborate with computer vision, speech recognition, and other AI domains, creating complex AI systems.

Large Language Models 52

Large Language Models DevOps Machine Learning Prompt Engineer 52

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.

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Generative AI Auto-complete AI Modeling Machine Learning 93

The MLOps Blog

MAY 10, 2023

2 For dynamic models, such as those with variable-length inputs or outputs, which are frequent in natural language processing (NLP) and computer vision, PyTorch offers improved support. This allows for more flexibility in modifying the model during training or inference. In this example, I’ll use the Neptune.

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Python Metadata ML Machine Learning 105

Snorkel AI

JULY 5, 2023

RC : I have had ML engineers tell me, “You didn’t need to do feature selection anymore, and that you could just throw everything at the model and it will figure out what to keep and what to throw away.” So does that mean feature selection is no longer necessary? If not, when should we consider using feature selection?”

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Large Language Models Metadata Machine Learning AI 52

The MLOps Blog

OCTOBER 3, 2023

I see so many of these job seekers, especially on the MLOps side or the ML engineer side. You see them all the time with a headline like: “data science, machine learning, Java, Python, SQL, or blockchain, computer vision.” That’s a huge part of what they do, so NLP is very big there, obviously.

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ML Data Scientist Machine Learning Data Science 52

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.

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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

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.

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Convolutional Neural Networks Neural Network Computer Vision Deep Learning 52

Snorkel AI

MAY 24, 2023

He has over 30 publications and more than 20 patents in machine learning and NLP. Matei Zaharia is co-founder and Chief Technologist at Databricks as well as an Associate Professor of Computer Science at Stanford. Within Wayfair, she is recognized as an expert in computer vision.

Machine Learning 52

Machine Learning Data Scientist Computer Vision ML 52

Snorkel AI

MAY 24, 2023

He has over 30 publications and more than 20 patents in machine learning and NLP. Matei Zaharia is co-founder and Chief Technologist at Databricks as well as an Associate Professor of Computer Science at Stanford. Within Wayfair, she is recognized as an expert in computer vision.

Machine Learning 52

Machine Learning Data Scientist Computer Vision ML 52

AWS Machine Learning Blog

OCTOBER 17, 2024

Amazon SageMaker Studio is the latest web-based experience for running end-to-end machine learning (ML) workflows. He has worked on a wide range of projects spanning NLP, computer vision, and generative AI. He also has experience with building end-to-end MLOps pipelines to productionize analytical models.

Data Science 115

Data Science ML Machine Learning Computer Vision 115