Snorkel AI

APRIL 27, 2023

The benefits of using Kubernetes for ML training Despite the challenges we faced building architecture around Kubernetes, there are three big reasons we used it: containerization, scalability, and infrastructure standardization. Scaling gets first-class support in Kubernetes. Reproducibility is important in ML.

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Snorkel AI

APRIL 27, 2023

The benefits of using Kubernetes for ML training Despite the challenges we faced building architecture around Kubernetes, there are three big reasons we used it: containerization, scalability, and infrastructure standardization. Scaling gets first-class support in Kubernetes. Reproducibility is important in ML.

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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.

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Google Research AI blog

APRIL 27, 2023

Large national efforts, such as the UK Biobank , have created large, public resources to better understand the links between environment, genetics, and disease. That is where we can use the ability of ML models to pick up on subtle intricate patterns in large amounts of data.

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NYU Center for Data Science

APRIL 19, 2023

To explore this question, the field of Vision and Language Navigation (VLN) has developed to train VLN models that navigate in photorealistic environments. Due to a lack of diversity in training environments and scarce human instruction data, these models still face limitations.

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Towards AI

MAY 9, 2024

I tried to make that part easier with this GitHub Repository I created for those without any background in the field: a complete guide to start and improve in ML and AI. We are working on something similar but at a much larger scale. use each of these tools worldwide to streamline their data and ML pipelines.

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IBM Journey to AI blog

AUGUST 22, 2023

It uses real-world data (both real time and historical) combined with engineering, simulation or machine learning (ML) models to enhance operations and support human decision-making. Traditional AI models versus foundation models These capabilities can help to continuously determine the state of the physical object.

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AWS Machine Learning Blog

JUNE 6, 2024

Today, we are excited to announce that the Jina Embeddings v2 model, developed by Jina AI, is available for customers through Amazon SageMaker JumpStart to deploy with one click for running model inference. This state-of-the-art model supports an impressive 8,192-tokens context length. What is RAG?

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Dataconomy

MARCH 27, 2023

However, while RPA and ML share some similarities, they differ in functionality, purpose, and the level of human intervention required. In this article, we will explore the similarities and differences between RPA and ML and examine their potential use cases in various industries. What is machine learning (ML)?

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Google Research AI blog

FEBRUARY 7, 2023

In the last 10 years, AI and ML models have become bigger and more sophisticated — they’re deeper, more complex, with more parameters, and trained on much more data, resulting in some of the most transformative outcomes in the history of machine learning.

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AWS Machine Learning Blog

OCTOBER 26, 2023

Intuitivo, a pioneer in retail innovation, is revolutionizing shopping with its cloud-based AI and machine learning (AI/ML) transactional processing system. Unlocking high-performance and cost-effective inference using AWS Inferentia As retailers look to scale operations, cost of A-POPs becomes a consideration.

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AWS Machine Learning Blog

FEBRUARY 13, 2024

They offer a large assortment of over 50,000 products across 1,000 brands, and are operating in more than 500 cities and towns. This has two issues: It requires additional manpower, weight stickers, and repeated training for the in-store operational team as they scale. You can also buy from their physical stores and vending machines.

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Snorkel AI

OCTOBER 31, 2023

Large language models (LLMs) have taken center stage in the world of AI. While impressive, these models demand a lot of infrastructure and generate high costs. Distilling LLMs can create models that are just as powerful, but cheaper to run and easier to deploy. Manual annotation presents obvious challenges.

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Google Research AI blog

MARCH 16, 2023

The team’s mission is to examine socioeconomic and human rights impacts of AI, publish foundational research, and incubate novel mitigations enabling machine learning (ML) practitioners to advance global equity. Our work gives weight to Google's AI Principles.

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Snorkel AI

SEPTEMBER 29, 2023

How you develop and use your proprietary data is the key to unlocking its value and to delivering accurate, reliable, and trustworthy AI and ML applications. Data labeling in the age of large language models (LLMs) The recent explosion of large language models (LLMs) and generative AI (genAI) has made data labeling more necessary than ever.

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ODSC - Open Data Science

MARCH 22, 2023

A 2021 VentureBeat analysis suggests that 87% of AI models never make it to a production environment and an MIT Sloan Management Review article found that 70% of companies reported minimal impact from AI projects. As a bonus, we’ll look into boosting your ML performance with smart upsampling. billion in 2022, an increase of 21.3%

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AWS Machine Learning Blog

FEBRUARY 22, 2023

In this post, we discuss how Boomi used the bring-your-own-container (BYOC) approach to develop a new AI/ML enabled solution for their customers to tackle the “blank canvas” problem. Boomi’s ML and data engineering teams needed the solution to be deployed quickly, in a repeatable and consistent way, at scale.

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ODSC - Open Data Science

FEBRUARY 17, 2023

ChatGPT: The Google Killer, Distributed Training with PyTorch and Azure ML, and Many Models Batch Training Distributed Training with PyTorch and Azure ML Continue reading to learn the simplest way to do distributed training with PyTorch and Azure ML. Learn more about the work they’re doing here!

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Snorkel AI

JUNE 23, 2023

Enterprises—especially the world’s largest—are excited to use large language models , but they want to fine-tune them on proprietary data. This practice adjusts the weights in the underlying model and yields more useful performance from LLMs. We examine some of the results below.

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Snorkel AI

JUNE 23, 2023

Enterprises—especially the world’s largest—are excited to use large language models , but they want to fine-tune them on proprietary data. This practice adjusts the weights in the underlying model and yields more useful performance from LLMs. We examine some of the results below.

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AWS Machine Learning Blog

APRIL 3, 2024

In this post, we show how to build a contextual text and image search engine for product recommendations using the Amazon Titan Multimodal Embeddings model , available in Amazon Bedrock , with Amazon OpenSearch Serverless. By default, the model generates vectors (embeddings) of 1,024 dimensions, and is accessed via Amazon Bedrock.

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AWS Machine Learning Blog

FEBRUARY 20, 2024

Trusted by the biggest names in entertainment, ZOO Digital delivers high-quality localization and media services at scale, including dubbing, subtitling, scripting, and compliance. In this post, we discuss deploying scalable machine learning (ML) models for diarizing media content using Amazon SageMaker , with a focus on the WhisperX model.

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Snorkel AI

OCTOBER 4, 2023

Artificial intelligence and machine learning (AI/ML) offer new avenues for credit scoring solutions and could usher in a new era of fairness, efficiency, and risk management. Traditional credit scoring models rely on static variables and historical data like income, employment, and debt-to-income ratio. Supercharge predictive modeling.

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Google Research AI blog

MARCH 2, 2023

This allows the training of models on locally available signals without exposing raw data to servers, increasing user privacy. This allows the training of models on locally available signals without exposing raw data to servers, increasing user privacy. In the new protocol , client computation now scales logarithmically in N.

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phData

MAY 30, 2024

From quality control in manufacturing to advanced medical diagnostics , the ability to harness and analyze visual data is still relatively untouched, and we firmly believe this presents an enormous opportunity for our clients. In other words, LandingAI trains its foundation models on domain-specific datasets.

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phData

JULY 17, 2023

Still, there are tremendous unexplored opportunities to create AI applications that can scale and accelerate across an entire enterprise. The advancement of technology in large language models (LLMs), machine learning (ML), and data science can truly transform industries through insights and predictions.

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Snorkel AI

OCTOBER 4, 2023

Artificial intelligence and machine learning (AI/ML) offer new avenues for credit scoring solutions and could usher in a new era of fairness, efficiency, and risk management. Traditional credit scoring models rely on static variables and historical data like income, employment, and debt-to-income ratio. Supercharge predictive modeling.

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Google Research AI blog

FEBRUARY 14, 2023

An undercurrent this past year has been the exploration of how large, generalist models, like PaLM , can work alongside other approaches to surface capabilities allowing robots to learn from a breadth of human knowledge and allowing people to engage with robots more naturally.

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Snorkel AI

JUNE 9, 2023

Snorkel Co-Founder and CEO Alex Ratner kicked off the day’s events by giving attendees a peek into Snorkel’s new Foundation Model Data Platform, which includes solutions to develop and adapt large language models and foundation models. You need to find a place to park your data.

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Snorkel AI

JUNE 9, 2023

Snorkel Co-Founder and CEO Alex Ratner kicked off the day’s events by giving attendees a peek into Snorkel’s new Foundation Model Data Platform, which includes solutions to develop and adapt large language models and foundation models. You need to find a place to park your data.

AI 52

AI AI Data Scientist Machine Learning 52

Heartbeat

JANUARY 9, 2024

Large language models have emerged as ground-breaking technologies with revolutionary potential in the fast-developing fields of artificial intelligence (AI) and natural language processing (NLP). These LLMs are artificial intelligence (AI) systems trained using large data sets, including text and code. What is LLMOps?

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ODSC - Open Data Science

SEPTEMBER 11, 2023

You’ll cover topics like GANs, and LLMs and get hands-on practice deploying generative models to create pieces of art and text. Topics covered included: Changes in credit risk trends through the pandemic and the challenges it posed in risk models. The use of auto-training methods to respond to a quick buildup of events.

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Google Research AI blog

MAY 25, 2023

Posted by Vincent Cohen-Addad and Alessandro Epasto, Research Scientists, Google Research, Graph Mining team Clustering is a central problem in unsupervised machine learning (ML) with many applications across domains in both industry and academic research more broadly.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

AutoML allows you to derive rapid, general insights from your data right at the beginning of a machine learning (ML) project lifecycle. Understanding up front which preprocessing techniques and algorithm types provide best results reduces the time to develop, train, and deploy the right model.

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AWS Machine Learning Blog

JANUARY 9, 2024

Foundation models (FMs) are often pre-trained on vast corpora of data with parameters ranging in scale of millions to billions and beyond. Large language models (LLMs) are a type of FM that generate text as a response of the user inference. Llama 2 models are autoregressive models with decoder only architecture.

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Dataconomy

MARCH 27, 2023

However, while RPA and ML share some similarities, they differ in functionality, purpose, and the level of human intervention required. In this article, we will explore the similarities and differences between RPA and ML and examine their potential use cases in various industries. What is machine learning (ML)?

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Heartbeat

OCTOBER 5, 2023

Unsurprisingly, Machine Learning (ML) has seen remarkable progress, revolutionizing industries and how we interact with technology. The emergence of Large Language Models (LLMs) like OpenAI's GPT , Meta's Llama , and Google's BERT has ushered in a new era in this field. Their mission?

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