IBM Data Science in Practice

MARCH 8, 2023

This is Part 2 of a series of blogs on IBM AI Governance. While AI lifecycle governance is essential to risk management and regulatory compliance, today much of this is done manually and across disparate tools, creating costly overheads and an unwieldy operating model. Setting up frameworks are part of this effort.

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Hacker News

JUNE 27, 2023

Leading users and industry-standard benchmarks agree: NVIDIA H100 Tensor Core GPUs deliver the best AI performance, especially on the large language models ( LLMs ) powering generative AI. H100 GPUs set new records on all eight tests in the latest MLPerf training benchmarks released today, excelling on a new MLPerf test for generative AI.

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AWS Machine Learning Blog

DECEMBER 19, 2023

Artificial intelligence (AI) and machine learning (ML) are becoming an integral part of systems and processes, enabling decisions in real time, thereby driving top and bottom-line improvements across organizations. However, putting an ML model into production at scale is challenging and requires a set of best practices.

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The MLOps Blog

MAY 9, 2023

One of the most prevalent complaints we hear from ML engineers in the community is how costly and error-prone it is to manually go through the ML workflow of building and deploying models. Building end-to-end machine learning pipelines lets ML engineers build once, rerun, and reuse many times. If all goes well, of course ?

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Towards AI

MAY 7, 2024

A collaboration between Nvidia and UPenn released DrEureka, a new open-source model that uses an LLM agent to write code for training robots in simulation and then write further code to transfer to real-world deployment. The model was trained on 8.1 The model scores 77.8 trillion tokens and has 21bn activated parameters.

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DagsHub

SEPTEMBER 5, 2023

Most Machine Learning models are dynamic. They continuously learn and enhance their performance with additional data. This requires us to constantly update the deployed model. However, doing it for ML models is not as intuitive as you imagine. What will this blog cover?

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Dataconomy

MAY 10, 2023

AI logo generators might be useful tools if you’re wanting to create a logo for your startup that stands out from the crowd. Logos can be created quickly and easily with the help of AI logo generators, which are online tools that employ AI and machine learning to create logo ideas depending on your preferences.

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AWS Machine Learning Blog

DECEMBER 13, 2023

In this post, we showcase fine-tuning a Llama 2 model using a Parameter-Efficient Fine-Tuning (PEFT) method and deploy the fine-tuned model on AWS Inferentia2. We then use a large model inference container powered by Deep Java Library (DJLServing) as our model serving solution.

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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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IBM Journey to AI blog

FEBRUARY 13, 2024

Tools such as Midjourney and ChatGPT are gaining attention for their capabilities in generating realistic images, video and sophisticated, human-like text, extending the limits of AI’s creative potential. This data is fed into generational models, and there are a few to choose from, each developed to excel at a specific task.

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Dataconomy

APRIL 25, 2023

Best AI tools can help professionals automate tasks, optimize workflows, enhance creativity, and improve decision-making. Whether you are a marketer, designer, developer, writer, or any other kind of professional, there is an AI tool that can make your life easier and your work better. Good news, you can still get on the AI train!

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Smart Data Collective

NOVEMBER 9, 2022

It has opened the door for companies to improve their business models in many ways. There are a number of great applications of machine learning. One of the biggest benefits is testing processes for optimal effectiveness. The main purpose of machine learning is to partially or completely replace manual testing.

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Google Research AI blog

MAY 23, 2023

However, with machine learning (ML), we have an opportunity to automate and streamline the code review process, e.g., by proposing code changes based on a comment’s text. Predicting the code edit We started by training a model that predicts code edits needed to address reviewer comments. a “Was this helpful?”

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AWS Machine Learning Blog

MAY 10, 2024

Fine-tuning large language models (LLMs) creates tailored customer experiences that align with a brand’s unique voice. It works both with SageMaker JumpStart and Amazon Bedrock models, giving you the flexibility to choose the foundation model (FM) for your needs. On the domain details page, view the user profiles.

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IBM Journey to AI blog

NOVEMBER 13, 2023

Docker was the first open-source software tool to popularize building, deploying and managing containerized applications. But Docker lacked an automated “orchestration” tool, which made it time-consuming and complex for data science teams to scale applications.

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BAIR

FEBRUARY 17, 2024

Please provide this image (and any other images and GIFs) in the blog to the BAIR Blog editors directly. The `static/blog` directory is a location on the blog server which permanently stores the images/GIFs in BAIR Blog posts. Is this paradigm here to stay as models improve?

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Flipboard

JULY 31, 2023

In this post, we’ll show you the datasets you can use to build your machine learning projects. After you create a free account, you’ll have access to the best machine learning datasets. Importance and Role of Datasets in Machine Learning Data is king. Many call this software 2.0.

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Flipboard

OCTOBER 9, 2023

This lesson is the 2nd of a 3-part series on Docker for Machine Learning : Getting Started with Docker for Machine Learning Getting Used to Docker for Machine Learning (this tutorial) Lesson 3 To learn how to create a Docker Container for Machine Learning, just keep reading.

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DrivenData Labs

MARCH 28, 2023

Joint effort and open science are the fundamental bricks to build new models and services for addressing societal challenges.” Let's see how the winners of this challenge bio-passed the test! To build these high-performing models, the winners brought a wide variety of creative strategies to this task! Sentinel-1.

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The MLOps Blog

MARCH 28, 2023

In this second installment of the series “Real-world MLOps Examples,” Paweł Pęczek , Machine Learning Engineer at Brainly , will walk you through the end-to-end Machine Learning Operations (MLOps) process in the Visual Search team at Brainly. Watch this video to learn how the Content AI team does MLOps. “If

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AWS Machine Learning Blog

FEBRUARY 29, 2024

Creating scalable and efficient machine learning (ML) pipelines is crucial for streamlining the development, deployment, and management of ML models. The framework code and examples presented here only cover model training pipelines, but can be readily extended to batch inference pipelines as well.

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IBM Journey to AI blog

MARCH 20, 2024

In the business sphere, both large enterprises and small startups depend on public cloud computing models to provide the flexibility, cost-effectiveness and scalability needed to fuel business growth. In a public cloud computing model, a cloud service provider (CSP) owns and operates vast physical data centers that run client workloads.

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The MLOps Blog

JANUARY 26, 2024

Training and evaluating models is just the first step toward machine-learning success. To generate value from your model, it should make many predictions, and these predictions should improve a product or lead to better decisions. An ML system needs to transform the data into features, train models, and make predictions.

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AWS Machine Learning Blog

APRIL 2, 2024

This blog post is co-written with Hwalsuk Lee at Upstage. Today, we’re excited to announce that the Solar foundation model developed by Upstage is now available for customers using Amazon SageMaker JumpStart. You can now use the Solar Mini Chat and Solar Mini Chat – Quant pretrained models within SageMaker JumpStart.

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Pickl AI

NOVEMBER 8, 2023

The Scikit-Learn cheat sheet is a concise reference guide for using Scikit-Learn , a popular Machine Learning library in Python. It offers quick access to key functions and concepts, including data preprocessing, supervised and unsupervised learning techniques, and model evaluation.

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AWS Machine Learning Blog

APRIL 8, 2024

Unlocking accurate and insightful answers from vast amounts of text is an exciting capability enabled by large language models (LLMs). When building LLM applications, it is often necessary to connect and query external data sources to provide relevant context to the model.

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AWS Machine Learning Blog

MAY 17, 2024

Today, we are excited to announce the Mixtral-8x22B large language model (LLM), developed by Mistral AI , is available for customers through Amazon SageMaker JumpStart to deploy with one click for running inference. In this post, we walk through how to discover and deploy the Mixtral-8x22B model.

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ODSC - Open Data Science

DECEMBER 4, 2023

ChatGPT’s explosion in popularity has created a surge in AI-generated blogs, articles, emails, resumes, and academic papers. Now that it’s easier for people to try and pass off AI-created content as their own, these tools have also improved to check for AI-generated text. Thankfully, AI detection tools are improving.

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AWS Machine Learning Blog

DECEMBER 14, 2023

Amazon Comprehend customers can train custom named entity recognition (NER) models to extract entities of interest, such as location, person name, and date, that are unique to their business. To train a custom model, you first prepare training data by manually annotating entities in documents. The first technique is fuzzy matching.

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Data Science Dojo

AUGUST 22, 2023

Unlocking the Power of LLM Use-Cases: AI applications now excel at summarizing articles, weaving narratives, and sparking conversations, all thanks to advanced large language models. Large language models, which are a prominent category of transformer models, have proven to be exceptionally versatile.

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The MLOps Blog

MARCH 21, 2023

Moving across the typical machine learning lifecycle can be a nightmare. From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. Choose the perfect tool for your needs.

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AWS Machine Learning Blog

MARCH 6, 2024

In this post, we demonstrate how to efficiently fine-tune a state-of-the-art protein language model (pLM) to predict protein subcellular localization using Amazon SageMaker. Proteins are the molecular machines of the body, responsible for everything from moving your muscles to responding to infections. COVID-19 Spikevax Moderna $21.8

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AWS Machine Learning Blog

JANUARY 26, 2024

Generative artificial intelligence (AI) applications built around large language models (LLMs) have demonstrated the potential to create and accelerate economic value for businesses. All the principles of the AWS Shared Responsibility Model are applicable to generative AI solutions.

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Pickl AI

APRIL 26, 2024

This comprehensive blog outlines vital aspects of Data Analyst interviews, offering insights into technical, behavioural, and industry-specific questions. It covers essential topics such as SQL queries, data visualization, statistical analysis, machine learning concepts, and data manipulation techniques.

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AWS Machine Learning Blog

FEBRUARY 19, 2024

Amazon SageMaker multi-model endpoints (MMEs) are a fully managed capability of SageMaker inference that allows you to deploy thousands of models on a single endpoint. In this post, we discuss a solution in which an MME can dynamically adjust the compute power assigned to each model based on the model’s traffic pattern.

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The MLOps Blog

MAY 10, 2023

When working on real-world machine learning (ML) use cases, finding the best algorithm/model is not the end of your responsibilities. It is crucial to save, store, and package these models for their future use and deployment to production. Reusability & reproducibility: Building ML models is time-consuming by nature.

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Pickl AI

JULY 10, 2023

As a programming language it provides objects, operators and functions allowing you to explore, model and visualise data. The programming language can handle Big Data and perform effective data analysis and statistical modelling. Hence, you can use R for classification, clustering, statistical tests and linear and non-linear modelling.

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