AWS Machine Learning Blog

APRIL 4, 2024

In this post, we describe how Nielsen Sports modernized a system running thousands of different machine learning (ML) models in production by using Amazon SageMaker multi-model endpoints (MMEs) and reduced operational and financial cost by 75%. It took months to get models with new model architecture to production.

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AWS Machine Learning Blog

MAY 16, 2024

For this reason, we built the MLOps architecture to manage the created models and provide real-time services. Solution architecture The following diagram illustrates the solution architecture for serving Neural Collaborative Filtering (NCF) algorithm-based recommendation models as MLOps.

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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. I had previously discussed example use cases and architectures that leverage Apache Kafka and machine learning.

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AWS Machine Learning Blog

FEBRUARY 24, 2023

AWS recently released Amazon SageMaker geospatial capabilities to provide you with satellite imagery and geospatial state-of-the-art machine learning (ML) models, reducing barriers for these types of use cases. For more information, refer to Preview: Use Amazon SageMaker to Build, Train, and Deploy ML Models Using Geospatial Data.

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AWS Machine Learning Blog

OCTOBER 2, 2023

This is Part 3 of our series where we design and implement an MLOps pipeline for visual quality inspection at the edge. In this post, we focus on how to automate the edge deployment part of the end-to-end MLOps pipeline. In Part 2 , we showed how to automate the labeling and model training parts of the pipeline.

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Iguazio

JANUARY 22, 2024

In this blog post, we show all the elements and practices you need to to take to productize LLMs and generative AI. You can watch the full talk this blog post is based on, which took place at ODSC West 2023, here. For businesses and developers, this means they need to maintain a flexible approach in design strategies.

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AWS Machine Learning Blog

MAY 13, 2024

In part 1 of this blog series, we discussed how a large language model (LLM) available on Amazon SageMaker JumpStart can be fine-tuned for the task of radiology report impression generation. Prompt engineering helps to effectively design and improve prompts to get better results on different tasks with LLMs.

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AWS Machine Learning Blog

MARCH 1, 2023

Statistical methods and machine learning (ML) methods are actively developed and adopted to maximize the LTV. In this post, we share how Kakao Games and the Amazon Machine Learning Solutions Lab teamed up to build a scalable and reliable LTV prediction solution by using AWS data and ML services such as AWS Glue and Amazon SageMaker.

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AWS Machine Learning Blog

AUGUST 31, 2023

As Artificial Intelligence (AI) and Machine Learning (ML) technologies have become mainstream, many enterprises have been successful in building critical business applications powered by ML models at scale in production. They provide a fact sheet of the model that is important for model governance.

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The MLOps Blog

SEPTEMBER 7, 2023

How to best adapt SOLID principles, how to make use of effective software patterns, how to give the most appropriate names to functions and classes, how to organize code modules, etc. This situation is not different in the ML world. Data Scientists and ML Engineers typically write lots and lots of code.

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The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. For example, neptune.ai and Pandas or Apache Spark DataFrames.

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AWS Machine Learning Blog

MAY 9, 2024

The rapid advancements in artificial intelligence and machine learning (AI/ML) have made these technologies a transformative force across industries. An effective approach that addresses a wide range of observed issues is the establishment of an AI/ML center of excellence (CoE). What is an AI/ML CoE?

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Data Science Dojo

APRIL 18, 2024

By analyzing this data, LLMs become experts at recognizing patterns and relationships between words. This blog aims to provide in-depth guidance in your journey to understand large language models. Here’s an article that explains the architecture and performance of Mistral 7b in detail. trillion tokens.

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AWS Machine Learning Blog

MAY 23, 2023

This is a guest blog post co-written with Vik Pant and Kyle Bassett from PwC. With organizations increasingly investing in machine learning (ML), ML adoption has become an integral part of business transformation strategies. Many organizations start their journey into the world of ML with a model-centric viewpoint.

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AWS Machine Learning Blog

FEBRUARY 1, 2024

Data pipelines In cases where you need to provide contextual data to the foundation model using the RAG pattern, you need a data pipeline that can ingest the source data, convert it to embedding vectors, and store the embedding vectors in a vector database. The calling application should invoke the pipeline asynchronously.

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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. This can significantly shorten the time needed to deploy the Machine Learning (ML) pipeline to production.

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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. As of today, code-change authors at Google address a substantial amount of reviewer comments by applying an ML-suggested edit. 3-way-merge UX in IDE.

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AWS Machine Learning Blog

DECEMBER 6, 2023

However, the popular RAG design pattern with semantic search can’t answer all types of questions that are possible on documents. In this post, we show how to design an intelligent document assistant capable of answering analytical and multi-step reasoning questions in three parts.

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AWS Machine Learning Blog

MAY 30, 2024

The following diagram illustrates the solution workflow and architecture. The solution adopts microservice design principles, with loosely coupled components that can be deployed together to serve the video analysis and policy evaluation workflow, or independently to integrate into existing pipelines.

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AWS Machine Learning Blog

JANUARY 9, 2023

Machine learning (ML) applications are complex to deploy and often require the ability to hyper-scale, and have ultra-low latency requirements and stringent cost budgets. Deploying ML models at scale with optimized cost and compute efficiencies can be a daunting and cumbersome task. Design patterns for building ML applications.

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Google Research AI blog

FEBRUARY 2, 2023

For research, it has not only reduced language model latency for users , designed computer architectures , accelerated hardware , assisted protein discovery , and enhanced robotics , but also provided a reliable backend interface for users to search for neural architectures and evolve reinforcement learning algorithms.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

In this post, we show you how to integrate Amazon SageMaker Clarify LLM evaluation with Amazon SageMaker Pipelines to enable LLM evaluation at scale. Deployment of Amazon SageMaker Pipelines relies on repository interactions and CI/CD pipeline activation.

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AWS Machine Learning Blog

MAY 7, 2024

Hugging Face is an open-source machine learning (ML) platform that provides tools and resources for the development of AI projects. They are designed for real-time, interactive, and low-latency workloads and provide auto scaling to manage load fluctuations.

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AWS Machine Learning Blog

AUGUST 31, 2023

As Artificial Intelligence (AI) and Machine Learning (ML) technologies have become mainstream, many enterprises have been successful in building critical business applications powered by ML models at scale in production. They provide a fact sheet of the model that is important for model governance.

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AWS Machine Learning Blog

NOVEMBER 9, 2023

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

By using the Well-Architected Framework and the IDP Well-Architected Custom Lens, you will learn about operational and architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable workloads in the cloud. This means you’ll need to select your management pattern accordingly.

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AWS Machine Learning Blog

JULY 31, 2023

Drug discovery is the research component of this pipeline that generates candidate drugs with the highest likelihood of being effective with the least harm to patients. Precise 3D structures of target proteins can enable drug design with higher specificity and lower likelihood of cross-interactions with other proteins.

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IBM Journey to AI blog

NOVEMBER 13, 2023

For instance, when demand fluctuates, Kubernetes enables applications to run continuously and respond to changes in web traffic patterns This helps maintain the right amount of workload resources, without over- or under- provisioning. These typically include an order service, payment service, shipping service and customer service.

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IBM Journey to AI blog

NOVEMBER 29, 2023

AI and event processing: a two-way street An event-driven architecture is essential for accelerating the speed of business. Furthermore, symbolic AI can be designed to reason and infer about facts and structured data, making it useful for navigating through complex business scenarios.

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AWS Machine Learning Blog

APRIL 5, 2024

SageMaker geospatial capabilities make it straightforward for data scientists and machine learning (ML) engineers to build, train, and deploy models using geospatial data. It helps in understanding how proposed infrastructure and land use changes can impact UHI patterns and identifies the key factors influencing UHI.

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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. Revolution of AI: Will ChatGPT Replace Search Engines in 2023?

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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. Supporting the operations of data scientists and ML engineers requires you to reduce—or eliminate—the engineering overhead of building, deploying, and maintaining high-performance models.

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AWS Machine Learning Blog

MAY 1, 2024

Llama 2 is an auto-regressive language model that uses an optimized transformer architecture and is intended for commercial and research use in English. In this post, we use the Neuron Distributed library to continuously pre-train a Llama 2 model using tensor and pipeline parallelism using SageMaker training jobs.

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AWS Machine Learning Blog

JANUARY 26, 2024

Understanding and addressing LLM vulnerabilities, threats, and risks during the design and architecture phases helps teams focus on maximizing the economic and productivity benefits generative AI can bring.

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AWS Machine Learning Blog

SEPTEMBER 28, 2023

In the world of data-driven decision-making, time series forecasting is key in enabling businesses to use historical data patterns to anticipate future outcomes. The following diagram illustrates the architecture of the training pipeline. The following diagram illustrates the inference pipeline.

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AWS Machine Learning Blog

APRIL 21, 2023

Batch inference is a common pattern where prediction requests are batched together on input, a job runs to process those requests against a trained model, and the output includes batch prediction responses that can then be consumed by other applications or business functions.

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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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