AWS Machine Learning Blog

AUGUST 4, 2023

In Part 1 , we show how the Salesforce Data Cloud and Einstein Studio integration with SageMaker allows businesses to access their Salesforce data securely using SageMaker and use its tools to build, train, and deploy models to endpoints hosted on SageMaker. We use the following sample dataset.

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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.

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Heartbeat

SEPTEMBER 18, 2023

Unleashing Innovation and Success: Comet — The Trusted ML Platform for Enterprise Environments Machine learning (ML) is a rapidly developing field, and businesses are increasingly depending on ML platforms to fuel innovation, improve efficiency, and mine data for insights.

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Towards AI

MARCH 28, 2024

This e-book focuses on adapting large language models (LLMs) to specific use cases by leveraging Prompt Engineering, Fine-Tuning, and Retrieval Augmented Generation (RAG), tailored for readers with an intermediate knowledge of Python. It offers free API service to access AI models like Gemma, GPT-4, GPT-4–1106-PREVIEW, GPT-3.5-turbo,

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AWS Machine Learning Blog

AUGUST 4, 2023

With this capability, businesses can access their Salesforce data securely with a zero-copy approach using SageMaker and use SageMaker tools to build, train, and deploy AI models. With Einstein Studio, a gateway to AI tools on the data platform, admins and data scientists can effortlessly create models with a few clicks or using code.

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

Nowadays, the majority of our customers is excited about large language models (LLMs) and thinking how generative AI could transform their business. However, bringing such solutions and models to the business-as-usual operations is not an easy task. Our approach applies to both open-source and proprietary models equally.

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Towards AI

SEPTEMBER 28, 2023

Deployment & Serving: 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’. A/B testing, canary, shadow, etc.)▢ [Reproducibility] How do you package your ML models for serving in the cloud or at the edge?▢

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AWS Machine Learning Blog

NOVEMBER 16, 2023

This is a joint blog with AWS and Philips. Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.

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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

OCTOBER 2, 2023

We show you how to use AWS IoT Greengrass to manage model inference at the edge and how to automate the process using AWS Step Functions and other AWS services. In Part 2 , we showed how to automate the labeling and model training parts of the pipeline. If you haven’t read it yet, we recommend checking out Part 1.

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AWS Machine Learning Blog

FEBRUARY 15, 2024

With the use of cloud computing, big data and machine learning (ML) tools like Amazon Athena or Amazon SageMaker have become available and useable by anyone without much effort in creation and maintenance. This dilemma hampers the creation of efficient models that use data to generate business-relevant insights.

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AWS Machine Learning Blog

SEPTEMBER 20, 2023

In these scenarios, as you start to embrace generative AI, large language models (LLMs) and machine learning (ML) technologies as a core part of your business, you may be looking for options to take advantage of AWS AI and ML capabilities outside of AWS in a multicloud environment.

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AWS Machine Learning Blog

MAY 25, 2023

The Spectrum AI platform combines customer attitudes with customers’ operational data and uses machine learning (ML) to generate continuous insight on CX. OCX built Spectrum AI on AWS because AWS offered a wide range of tools, elastic computing, and an ML environment that would keep pace with evolving needs.

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AWS Machine Learning Blog

MARCH 13, 2023

Amazon SageMaker Ground Truth Plus is a managed data labeling service that makes it easy to label data for machine learning (ML) applications. One common use case is semantic segmentation, which is a computer vision ML technique that involves assigning class labels to individual pixels in an image. This labeling process is complex.

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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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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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Mlearning.ai

FEBRUARY 6, 2023

Use SageMaker Processing Jobs to easily run inference on your large Dataset with Hugging Face’s Transformer Models Photo by Alex Kulikov on Unsplash This blog will give you a complete walk through of running a distributed batch inference on large data in production. Note: /opt/ml and all its subdirectories are reserved by SageMaker.

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AWS Machine Learning Blog

MAY 10, 2023

In this post, we share how Sleepme used Amazon SageMaker to developed a machine learning (ML) model proof of concept that recommends temperatures to maximize your sleep score. This use case demanded an ML model that served real-time inference. This ML model needed to make sleep environment adjustments in real time.

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AWS Machine Learning Blog

MAY 10, 2023

You can now register machine learning (ML) models built in Amazon SageMaker Canvas with a single click to the Amazon SageMaker Model Registry , enabling you to operationalize ML models in production. If it performs to your requirements, you can update the approval status of the model version to approved.

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AWS Machine Learning Blog

APRIL 7, 2023

As one of the most prominent use cases to date, machine learning (ML) at the edge has allowed enterprises to deploy ML models closer to their end-customers to reduce latency and increase responsiveness of their applications. Even ground and aerial robotics can use ML to unlock safer, more autonomous operations.

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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

APRIL 19, 2023

In this post, the Sportradar team discusses the challenges they encountered and the solutions they created to build their model inference platform using the DJL. Since 2018, our team has been developing a variety of ML models to enable betting products for NFL and NCAA football. We recently developed four more new models.

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deepsense.ai

JULY 30, 2023

The recent strides made in the field of machine learning have given us an array of powerful language models and algorithms. These models offer tremendous potential but also bring a unique set of challenges when it comes to building large-scale ML projects. But what happens next?

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AWS Machine Learning Blog

MARCH 8, 2023

Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. Create a custom container image for ML model training and push it to Amazon ECR.

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AWS Machine Learning Blog

DECEMBER 5, 2023

New and powerful large language models (LLMs) are changing businesses rapidly, improving efficiency and effectiveness for a variety of enterprise use cases. Enterprises need a responsible and safer way to send sensitive information to the models without needing to take on the often prohibitively high overheads of on-premises DevOps.

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The MLOps Blog

APRIL 16, 2024

When building ML models, you spend a lot of time experimenting. Already with one model in the pipeline, you may try out hundreds of parameters and produce tons of metadata about your runs. And the more models you develop (and later deploy), the more stuff is there to store, track, compare, organize, and share with others.

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AWS Machine Learning Blog

MAY 3, 2023

New generations of CPUs offer a significant performance improvement in machine learning (ML) inference due to specialized built-in instructions. times the speed for BERT, making Graviton-based instances the fastest compute optimized instances on AWS for these models. inference for Arm-based processors. is up to 3.5

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AWS Machine Learning Blog

AUGUST 30, 2023

Generative AI is powered by machine learning (ML) models—very large models that are pre-trained on vast amounts of data and commonly referred to as foundation models (FMs). There are significant benefits to using a pre-trained model. We demonstrate how to accomplish this using a notebook in Amazon SageMaker Studio.

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Pickl AI

JUNE 7, 2023

Some of the Data Science Projects on Github that you work upon have been listed in this blog. The primary goal of the Kaggle competition is creating an ML Model that can predict the total number of bikes rented. ” It additionally provides an array of additional helpful functions and layers to generate customised models.

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AWS Machine Learning Blog

NOVEMBER 8, 2023

The MONAI AI models and applications can be hosted on Amazon SageMaker , which is a fully managed service to deploy machine learning (ML) models at scale. To create an asynchronous inference endpoint , you will need to create a SageMaker model and endpoint configuration first. The model.tar.gz and PyTorch 1.12.1

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AWS Machine Learning Blog

MARCH 1, 2023

The ability to train custom models through the Custom classification and Custom entity recognition features of Comprehend has enabled customers to explore out-of-the-box NLP capabilities tied to their requirements without having to take the approach of building classification and entity recognition models from scratch.

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AWS Machine Learning Blog

MARCH 20, 2023

Customers are always looking for ways to improve the performance and response times of their machine learning (ML) inference workloads without increasing the cost per transaction and without sacrificing the accuracy of the results. In the following example figure, we show INT8 inference performance in C6i for a BERT-base model.

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AWS Machine Learning Blog

MAY 23, 2023

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

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AWS Machine Learning Blog

JULY 24, 2023

When deploying Deep Learning models at scale, it is crucial to effectively utilize the underlying hardware to maximize performance and cost benefits. In this post we walk you through the process of deploying FastAPI model servers on AWS Inferentia devices (found on Amazon EC2 Inf1 and Amazon EC Inf2 instances).

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AWS Machine Learning Blog

JUNE 8, 2023

Data scientists need a consistent and reproducible environment for machine learning (ML) and data science workloads that enables managing dependencies and is secure. AWS Deep Learning Containers already provides pre-built Docker images for training and serving models in common frameworks such as TensorFlow, PyTorch, and MXNet.

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AWS Machine Learning Blog

AUGUST 3, 2023

Computer vision (CV) is one of the most common applications of machine learning (ML) and deep learning. Amazon Rekognition is a fully managed service that can perform CV tasks like object detection, video segment detection, content moderation, and more to extract insights from data without the need of any prior ML experience.

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AWS Machine Learning Blog

MAY 25, 2023

One of the most common applications of generative AI and large language models (LLMs) in an enterprise environment is answering questions based on the enterprise’s knowledge corpus. RAG models were introduced by Lewis et al. We can use the same architecture to swap the open-source models with the Amazon Titan models.

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