Towards AI

SEPTEMBER 27, 2023

Experiments, Model Training & Evaluation: Exploring 6 Key MLOps Questions using AWS SageMaker This article is part of the AWS SageMaker series for exploration of ’31 Questions that Shape Fortune 500 ML Strategy’. Reproducibility] How do you track and manage different versions of trained models?▢

Data Scientist 62

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ODSC - Open Data Science

MARCH 6, 2023

In the first blog, we’re going to discuss the technical side of things, such as what languages and platforms people are using. Machine Learning Tools & Frameworks We asked three things about machine learning frameworks: What you currently use, what you plan to use, and what you aren’t using or plan to use.

Machine Learning 116

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Snorkel AI

OCTOBER 13, 2023

Fine-tuning pre-trained large language models like OpenAI’s GPT-3.5 The Snorkel Flow data development platform makes it easy for users to do so, making large language models like GPT-3.5 In this guide, we will explore how to fine-tune GPT-3.5 The cost benefits of fine-tuning OpenAI models come with caveats.

Prompt Engineer 64

Prompt Engineer Prompt Engineering Large Language Models OpenAI 64

AWS Machine Learning Blog

JUNE 22, 2023

The final ML model combines CNN and Transformer, which are the state-of-the-art neural network architectures for modeling sequential machine log data. This post is co-written with Aruna Abeyakoon and Denisse Colin from Light and Wonder (L&W). Headquartered in Las Vegas, Light & Wonder, Inc.

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Snorkel AI

NOVEMBER 28, 2023

Fine-tuning pre-trained large language models like Meta’s Llama 2 adapts them to specific tasks, ensuring higher quality results and achieving reliable output formatting. In this guide, we will explore how to fine-tune Llama 2 using Snorkel Flow in two ways: A user-friendly interface for accessing and fine-tuning all available models.

Prompt Engineer 59

Prompt Engineer Prompt Engineering Large Language Models Data Science 59

Snorkel AI

NOVEMBER 28, 2023

Fine-tuning pre-trained large language models like Meta’s Llama 2 adapts them to specific tasks, ensuring higher quality results and achieving reliable output formatting. In this guide, we will explore how to fine-tune Llama 2 using Snorkel Flow in two ways: A user-friendly interface for accessing and fine-tuning all available models.

Prompt Engineer 59

Prompt Engineer Prompt Engineering Large Language Models Data Science 59

Pickl AI

NOVEMBER 8, 2023

It offers quick access to key functions and concepts, including data preprocessing, supervised and unsupervised learning techniques, and model evaluation. This resource is invaluable for Data Scientists and Machine Learning practitioners, streamlining their workflow and aiding in model development.

Machine Learning 52

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Heartbeat

AUGUST 15, 2023

Specifically, we will discuss CNNs, RNNs, data augmentation, transfer learning, and ensemble models. These libraries provide pre-built functions and modules that can be used to create CNN models for handwritten recognition. Many sectors, including finance, security, and education, are starting to emphasize this method more.

Python 52

Python Convolutional Neural Networks Neural Network Deep Learning 52

Snorkel AI

OCTOBER 13, 2023

Fine-tuning pre-trained large language models like OpenAI’s GPT-3.5 The Snorkel Flow data development platform makes it easy for users to do so, making large language models like GPT-3.5 In this guide, we will explore how to fine-tune GPT-3.5 The cost benefits of fine-tuning OpenAI models come with caveats.

Prompt Engineer 59

Prompt Engineer Prompt Engineering Large Language Models OpenAI 59

Google Research AI blog

JULY 11, 2023

Posted by Amir Yazdanbakhsh, Research Scientist, and Vijay Janapa Reddi, Visiting Researcher, Google Research Computer Architecture research has a long history of developing simulators and tools to evaluate and shape the design of computer systems. learning rate, warm-up steps, etc.).

Machine Learning 74

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Heartbeat

JANUARY 24, 2023

Guide for programmers with Optuna experience Photo by Diane Picchiottino on Unsplash Introduction Hyperparameters are among the most important aspects of any given model in Data Science and Machine Learning applications. The right combination of hyperparameters is essential when one desires to come up with a great model.

ML 52

ML Algorithm Machine Learning Deep Learning 52

The MLOps Blog

JUNE 27, 2023

Picture this, you’ve dedicated countless hours to training and fine-tuning your model, meticulously analyzing mountains of data. SHAP values serve as an exact tool for this task, empowering you to identify key variables that shape a model’s output. So without further ado, let’s begin!

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ML Data Scientist Machine Learning Explainability 52

The MLOps Blog

FEBRUARY 22, 2024

TL;DR: In many machine-learning projects, the model has to frequently be retrained to adapt to changing data or to personalize it. Continual learning is a set of approaches to train machine learning models incrementally, using data samples only once as they arrive. Then, you repeat this process until you are satisfied with the results.

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Pickl AI

AUGUST 1, 2023

As technology continues to impact how machines operate, Machine Learning has emerged as a powerful tool enabling computers to learn and improve from experience without explicit programming. In this blog, we will delve into the fundamental concepts of data model for Machine Learning, exploring their types. What is Machine Learning?

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Heartbeat

FEBRUARY 23, 2023

Introduction Have you ever wondered how to use Bayesian optimization with TensorFlow? Image Courtesy of UrielSC A big part of data science is tuning our models and improving upon them over time. But instead of just tuning specific hyperparameters, we can also decide how our network is shaped. STEP 1: Imports!

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Neural Network Categorization Deep Learning Data Science 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). We must create new tools and best practices to manage the LLM application lifecycle to address these issues. What is LLMOps?

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Large Language Models Natural Language Processing LLM Machine Learning 52

AWS Machine Learning Blog

JANUARY 5, 2024

Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models. Data science and DevOps teams may face challenges managing these isolated tool stacks and systems. This increases the cost of infrastructure maintenance and hampers productivity.

Data Science 113

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Pickl AI

NOVEMBER 2, 2023

A cheat sheet for Data Scientists is a concise reference guide, summarizing key concepts, formulas, and best practices in Data Analysis, statistics, and Machine Learning. It serves as a handy quick-reference tool to assist data professionals in their work, aiding in data interpretation, modeling , and decision-making processes.

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Data Scientist Data Science Neural Network Deep Learning 52

Mlearning.ai

APRIL 6, 2023

⚠ You can solve the below-mentioned questions from this blog ⚠ ✔ What if I am building Low code — No code ML automation tool and I do not have any orchestrator or memory management system ? ✔ What if my CPU memory is insufficient and makes things time-consuming in the case of random search in multiple hyperparameter tuning at a time?

Machine Learning 52

Machine Learning Algorithm Neural Network Computer Vision 52

Pickl AI

AUGUST 3, 2023

This data is split into two sets: the training set used to update the model’s parameters, and the validation/test set used to evaluate the model’s performance. At the core of Deep Learning is the artificial neural network (ANN), which is inspired by the structure and function of the human brain.

Deep Learning 52

Deep Learning Machine Learning Algorithm Neural Network 52

Heartbeat

JULY 17, 2023

[link] Transfer learning using pre-trained computer vision models has become essential in modern computer vision applications. It involves customizing a pre-trained model to work with a new set of data and tasks. This process requires careful monitoring and bench-marking to ensure the model performs optimally.

Computer Vision 52

Computer Vision Auto-classification Deep Learning Machine Learning 52

Pickl AI

JULY 28, 2023

Read Blog 6 Best Artificial Intelligence Courses for Beginners in India Difference Between Image Recognition vs. Object Recognition The process of locating and classifying different visual patterns or elements inside digital photographs is referred to as image recognition. What is Image Recognition? Why Image Recognition Matters?

Machine Learning 52

Machine Learning Convolutional Neural Networks Neural Network Deep Learning 52

Topbots

MAY 17, 2023

In 2015, a research paper from Stanford University and UC Berkeley introduced diffusion models, coming originally from statistical physics, into the field of machine learning. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data.”

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AWS Machine Learning Blog

JANUARY 13, 2023

As Parsons’ footprint moves into the cloud, their leadership recognized the need for a change in culture and a fundamental requirement to educate their engineering task force on the new cloud operating model, tools, and technologies. Parsons is a builder community and invests heavily in employee development programs and upskilling.

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Artificial Intelligence Artificial Intelligence Machine Learning ML 86

TensorFlow

APRIL 6, 2023

Posted by Bhaktipriya Radharapu, Software Engineer TensorFlow Model Remediation is an open source toolkit that showcases solutions to help mitigate unfair bias in Machine Learning models. The toolkit offers resources to build fairer models for everyone – in line with Google’s AI Principles.

Responsible AI 52

Responsible AI Machine Learning Software Engineer ML 52

Bugra Akyildiz

JUNE 11, 2023

Lyft wrote about their efforts to detect anomalies in time-series signals in this following blog post. DIDACT utilizes interactions among engineers and tools to power ML models that assist Google developers, by suggesting or enhancing actions developers take — in context — while pursuing their software-engineering tasks.

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Algorithm Deep Learning Python NLP 52

AWS Machine Learning Blog

JULY 6, 2023

Many organizations choose SageMaker as their ML platform because it provides a common set of tools for developers and data scientists. In this post, we cover the benefits for SaaS platforms to integrate with SageMaker, the range of possible integrations, and the process for developing these integrations.

ML 76

ML Metadata Data Scientist ETL 76

Mlearning.ai

AUGUST 22, 2023

Activation Functions in Neural Networks From the traditional Sigmoid and ReLU to cutting-edge activation functions like GeLU, this article delves into their significance, math, and guidelines for choosing the ideal function for your model. This enables the models to learn complex patterns and helps them make accurate predictions.

Neural Network 52

Neural Network Algorithm Computer Vision Deep Learning 52

AWS Machine Learning Blog

FEBRUARY 12, 2024

The Ranking team at Booking.com plays a pivotal role in ensuring that the search and recommendation algorithms are optimized to deliver the best results for their users. Essential ML capabilities such as hyperparameter tuning and model explainability were lacking on premises.

ML 113

ML Explainability Machine Learning Deep Learning 113

The MLOps Blog

MARCH 15, 2023

This includes the tools and techniques we used to streamline the ML model development and deployment processes, as well as the measures taken to monitor and maintain models in a production environment. CI/CD ensures that models are thoroughly tested and validated before they are deployed to a production environment.

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AWS Machine Learning Blog

NOVEMBER 10, 2023

Creating high-performance machine learning (ML) solutions relies on exploring and optimizing training parameters, also known as hyperparameters. Exploring hyperparameters involves systematically varying the values of each parameter and observing the impact on model performance.

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ML Algorithm Auto-complete Python 90

AWS Machine Learning Blog

MARCH 13, 2024

Today, we’re excited to announce that the Gemma model is now available for customers using Amazon SageMaker JumpStart. Gemma is a family of language models based on Google’s Gemini models, trained on up to 6 trillion tokens of text. Gemma released the model weights to support developer innovation using Gemma models.

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Large Language Models LLM Machine Learning Python 110

The MLOps Blog

JANUARY 18, 2023

Model monitoring is an essential part of the CI/CD pipeline. One of the major issues with any model is that it may perform well in the development phase, but when deployed, it may perform poorly or may even fail. This is especially true with the time series model, as the changes in the dataset can be quite rapid.

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AWS Machine Learning Blog

JANUARY 19, 2024

In this post, we demonstrate how to use neural architecture search (NAS) based structural pruning to compress a fine-tuned BERT model to improve model performance and reduce inference times. Pruning a PLM reduces the size and complexity of the model while retaining its predictive capabilities.

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The MLOps Blog

APRIL 20, 2023

With over 3 years of experience in designing, building, and deploying computer vision (CV) models , I’ve realized people don’t focus enough on crucial aspects of building and deploying such complex systems. Hopefully, at the end of this blog, you will know a bit more about finding your way around computer vision projects.

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Computer Vision Auto-classification Neural Network Convolutional Neural Networks 64

AWS Machine Learning Blog

SEPTEMBER 18, 2023

This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model. Ray, an open-source distributed computing framework, provides a flexible framework for distributed training and serving of ML models. SageMaker is a fully managed service for building, training, and deploying ML models.

Machine Learning 103

Machine Learning ML Python Auto-complete 103

Heartbeat

MAY 29, 2023

These models have the potential to revolutionize industries ranging from customer service to scientific research, but their capabilities and limitations are still not fully understood. We will explore how to better understand the data that these models are trained on, and how to evaluate and optimize them for real-world use.

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Large Language Models LLM Machine Learning Natural Language Processing 78