AWS Machine Learning Blog

FEBRUARY 13, 2024

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference. For a deep dive, refer to Cross account feature group discoverability and access.

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AWS Machine Learning Blog

FEBRUARY 27, 2024

In this post, we share how Axfood, a large Swedish food retailer, improved operations and scalability of their existing artificial intelligence (AI) and machine learning (ML) operations by prototyping in close collaboration with AWS experts and using Amazon SageMaker.

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The MLOps Blog

JUNE 27, 2023

To provide you with a comprehensive overview, this article explores the key players in the MLOps and FMOps (or LLMOps) ecosystems, encompassing both open-source and closed-source tools, with a focus on highlighting their key features and contributions. For example, neptune.ai

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AWS Machine Learning Blog

MAY 30, 2024

In this blog, we walkthrough the architectural components, evaluation criteria for the components selected by Vitech and the process flow of user interaction within VitechIQ. This post is co-written with Murthy Palla and Madesh Subbanna from Vitech. Vitech is a global provider of cloud-centered benefit and investment administration software.

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AWS Machine Learning Blog

MAY 15, 2024

In a March 2023 survey, Amazon Ads found that among advertisers who were unable to build successful campaigns, nearly 75 percent cited building the creative content as one of their biggest challenges. Next, we present the solution architecture and process flows for machine learning (ML) model building, deployment, and inferencing.

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AWS Machine Learning Blog

APRIL 23, 2024

The AWS Well-Architected Framework provides best practices and guidelines for designing and operating reliable, secure, efficient, and cost-effective systems in the cloud. This post explores the new enterprise-grade features for Knowledge Bases on Amazon Bedrock and how they align with the AWS Well-Architected Framework.

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IBM Journey to AI blog

NOVEMBER 3, 2023

NVMe (non-volatile memory express) is a protocol for highly parallel data transfer with reduced system overheads per input/output per second (I/O, or iops) that is used in flash storage and solid-state drives (SSDs). SSDs are semiconductor-based storage devices that rely on flash memory to store persistent data in computer systems.

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IBM Journey to AI blog

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Today, 35% of companies report using AI in their business, which includes ML, and an additional 42% reported they are exploring AI, according to the IBM Global AI Adoption Index 2022. What is MLOps?

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AWS Machine Learning Blog

OCTOBER 6, 2023

One such component is a feature store, a tool that stores, shares, and manages features for machine learning (ML) models. Features are the inputs used during training and inference of ML models. We include some examples of personalized messages later in this post.

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IBM Journey to AI blog

JANUARY 16, 2024

Businesses are increasingly embracing data-intensive workloads, including high-performance computing, artificial intelligence (AI) and machine learning (ML). To aid in building more sustainable IT estates, IBM has partnered up with Amazon Web Services (AWS) to facilitate sustainable cloud modernization journeys.

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Flipboard

AUGUST 17, 2023

Many practitioners are extending these Redshift datasets at scale for machine learning (ML) using Amazon SageMaker , a fully managed ML service, with requirements to develop features offline in a code way or low-code/no-code way, store featured data from Amazon Redshift, and make this happen at scale in a production environment.

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TensorFlow

FEBRUARY 6, 2024

Yet most machine learning (ML) algorithms allow only for regular and uniform relations between input objects, such as a grid of pixels, a sequence of words, or no relation at all. They encode a graph's discrete , relational information in a continuous way so that it can be included naturally in another deep learning system.

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AWS Machine Learning Blog

FEBRUARY 13, 2024

BigBasket is India’s largest online food and grocery store. You can also buy from their physical stores and vending machines. Customer challenges Today, most supermarkets and physical stores in India provide manual checkout at the checkout counter. The following figure provides an overview of the checkout process.

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AWS Machine Learning Blog

MAY 30, 2024

Advanced content moderation makes sure ads appear alongside safe, compliant content, building trust with consumers. In parallel, the system extracts audio transcription from the uploaded content using Amazon Transcribe. Policy evaluation – Using the extracted metadata from the video, the system conducts LLM evaluation.

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AWS Machine Learning Blog

SEPTEMBER 18, 2023

Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model. SageMaker is a fully managed service for building, training, and deploying ML models.

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ODSC - Open Data Science

MAY 31, 2023

The combination of data streaming and machine learning (ML) enables you to build one scalable, reliable, but also simple infrastructure for all machine learning tasks using the Apache Kafka ecosystem. Data processing happens in batch mode with the data stored at rest and can take minutes or even hours.

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AWS Machine Learning Blog

APRIL 16, 2024

Customers are building generative AI applications using large language models (LLMs) and other foundation models (FMs), which enhance customer experiences, transform operations, improve employee productivity, and create new revenue channels. With the AWS Nitro System , we delivered a first-of-its-kind innovation on behalf of our customers.

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AWS Machine Learning Blog

JANUARY 10, 2024

ML Engineer at Tiger Analytics. The large machine learning (ML) model development lifecycle requires a scalable model release process similar to that of software development. Model developers often work together in developing ML models and require a robust MLOps platform to work in.

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AWS Machine Learning Blog

OCTOBER 20, 2023

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries.

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AWS Machine Learning Blog

FEBRUARY 12, 2024

Sharing in-house resources with other internal teams, the Ranking team machine learning (ML) scientists often encountered long wait times to access resources for model training and experimentation – challenging their ability to rapidly experiment and innovate. This post is co-written with Kostia Kofman and Jenny Tokar from Booking.com.

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IBM Journey to AI blog

JANUARY 30, 2024

Multicloud architecture not only empowers businesses to choose a mix of the best cloud products and services to match their business needs, but it also accelerates innovation by supporting game-changing technologies like generative AI and machine learning (ML).

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AWS Machine Learning Blog

DECEMBER 14, 2023

To ensure the effective operation of this job recommendation engine, it is crucial to implement a large-scale data processing pipeline responsible for extracting and refining features from Talent.com’s aggregated job listings. Talent.com’s mission is to facilitate global workforce connections.

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AWS Machine Learning Blog

OCTOBER 2, 2023

It is architected to automate the entire machine learning (ML) process, from data labeling to model training and deployment at the edge. In this post, we delve deep into how the labeling and model building and training parts of the pipeline are implemented. The quality of our labels will affect the quality of our ML model.

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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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AWS Machine Learning Blog

JANUARY 5, 2024

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models.

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AWS Machine Learning Blog

APRIL 24, 2024

With AWS generative AI services like Amazon Bedrock , developers can create systems that expertly manage and respond to user requests. An AI assistant is an intelligent system that understands natural language queries and interacts with various tools, data sources, and APIs to perform tasks or retrieve information on behalf of the user.

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AWS Machine Learning Blog

APRIL 3, 2024

Search engines and recommendation systems powered by generative AI can improve the product search experience exponentially by understanding natural language queries and returning more accurate results. With Amazon Titan Multimodal Embeddings, you can generate embeddings for your content and store them in a vector database.

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IBM Journey to AI blog

SEPTEMBER 11, 2023

On June 7, 1983, a product was born that would revolutionize how organizations would store, manage, process , and query their data: IBM Db2. It also stores and queries anything and powers faster decision-making across organizations. Over the past 40 years, Db2 has been on an exciting and transformational journey.

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IBM Journey to AI blog

MARCH 20, 2024

They also manage the underlying virtualization of servers, operating system software networking and other infrastructure that power a public cloud data center and maximize data center resources. A public cloud is a type of cloud computing in which a third-party service provider (e.g., How does public cloud computing work?

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AWS Machine Learning Blog

JANUARY 26, 2024

The goal of this post is to empower AI and machine learning (ML) engineers, data scientists, solutions architects, security teams, and other stakeholders to have a common mental model and framework to apply security best practices, allowing AI/ML teams to move fast without trading off security for speed.

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AWS Machine Learning Blog

DECEMBER 1, 2023

The ability to quickly build and deploy machine learning (ML) models is becoming increasingly important in today’s data-driven world. However, building ML models requires significant time, effort, and specialized expertise. This is where the AWS suite of low-code and no-code ML services becomes an essential tool.

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AWS Machine Learning Blog

MARCH 28, 2024

However, when building generative AI applications, you can use an alternative solution that allows for the dynamic incorporation of external knowledge and allows you to control the information used for generation without the need to fine-tune your existing foundational model.

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AWS Machine Learning Blog

DECEMBER 14, 2023

Amazon SageMaker Studio offers a broad set of fully managed integrated development environments (IDEs) for machine learning (ML) development, including JupyterLab, Code Editor based on Code-OSS (Visual Studio Code Open Source), and RStudio. It’s attached to a ML compute instance whenever a Space is run.

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BAIR

MARCH 11, 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. Their journey is just beginning, and the future they will help build is bright!

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AWS Machine Learning Blog

APRIL 19, 2023

Businesses are increasingly using machine learning (ML) to make near-real-time decisions, such as placing an ad, assigning a driver, recommending a product, or even dynamically pricing products and services. As a result, some enterprises have spent millions of dollars inventing their own proprietary infrastructure for feature management.

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Flipboard

APRIL 13, 2023

The seeds of a machine learning (ML) paradigm shift have existed for decades, but with the ready availability of scalable compute capacity, a massive proliferation of data, and the rapid advancement of ML technologies, customers across industries are transforming their businesses.

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IBM Journey to AI blog

JULY 6, 2023

While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. This blog post will clarify some of the ambiguity. Artificial intelligence is the overarching system.

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