AWS Machine Learning

APRIL 11, 2024

AWS was delighted to present to and connect with over 18,000 in-person and 267,000 virtual attendees at NVIDIA GTC, a global artificial intelligence (AI) conference that took place March 2024 in San Jose, California, returning to a hybrid, in-person experience for the first time since 2019.

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AWS Machine Learning

AUGUST 30, 2023

To solve this problem, we propose the use of generative AI, a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music. We demonstrate how to accomplish this using a notebook in Amazon SageMaker Studio. The training instance type used in this post is ml.p3.16xlarge.

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AWS Machine Learning

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. This is a guest post written by Axfood AB.

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AWS Machine Learning

FEBRUARY 29, 2024

AI’s growing influence in large organizations brings crucial challenges in managing AI platforms. Amazon SageMaker Studio offers a comprehensive set of capabilities for machine learning (ML) practitioners and data scientists. Deutsche Bahn has been at the forefront in adopting AI, using SageMaker Studio as a key AI platform.

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AWS Machine Learning

JULY 25, 2023

With recent advancements in generative AI , there are lot of discussions happening on how to use generative AI across different industries to solve specific business problems. Generative AI is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music.

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AWS Machine Learning

AUGUST 4, 2023

This post is co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI. 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.

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AWS Machine Learning

SEPTEMBER 19, 2023

In this post, we share how VirtuSwap used the bring-your-own-container feature in Amazon SageMaker Studio to build a robust environment to host their GPU-intensive simulations to solve linear optimization problems. In CloudShell, we used SageMaker Docker Build , a CLI for building Docker images for and in SageMaker Studio.

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AWS Machine Learning

AUGUST 8, 2023

You can run Spark applications interactively from Amazon SageMaker Studio by connecting SageMaker Studio notebooks and AWS Glue Interactive Sessions to run Spark jobs with a serverless cluster. Solution overview The solution integrates Spark History Server into the Jupyter Server app in SageMaker Studio.

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AWS Machine Learning

AUGUST 4, 2023

This post is co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI. In this post, we expand on this topic to demonstrate how to use Einstein Studio for product recommendations. Train a recommendation model in SageMaker Studio using training data that was prepared using SageMaker Data Wrangler.

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AWS Machine Learning

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

AUGUST 28, 2023

The students used Amazon SageMaker Studio Lab , which is a free platform that provides a JupyterLab environment with compute (CPU and GPU) and storage (up to 15GB). AI is becoming increasingly important in the workplace, and 82% of companies need employees with machine learning skills.

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AWS Machine Learning

APRIL 8, 2024

RAG introduces an effective approach for building conversational agents and AI assistants with contextualized, high-quality responses. Building the solution consists of the following steps: Set up Amazon SageMaker Studio as the development environment and install the required dependencies.

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AWS Machine Learning

MAY 31, 2023

The SDK also supports multiple configuration files, allowing admins to set a configuration file for all users, and users can override it via a user-level configuration that can be stored in Amazon Simple Storage Service (Amazon S3), Amazon Elastic File System (Amazon EFS) for Amazon SageMaker Studio , or the user’s local file system.

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AWS Machine Learning

OCTOBER 17, 2022

Amazon SageMaker lets ML teams choose to work from fully managed, cloud-based environments within Amazon SageMaker Studio , SageMaker Notebook Instances , or from your local machine using local mode. In this post, we cover installation for both Studio environments (Option A), and notebook instances (Option B). Solution overview.

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AWS Machine Learning

DECEMBER 13, 2023

AWS CodeStar connection – We use AWS CodeStar to establish a link with the GitHub repository and utilize it as code repo integration with AWS resources, like SageMaker Studio. A SageMaker Studio domain The AWS Command Line Interface (AWS CLI) installed and configured. On the Launch menu, choose Studio. An AWS account.

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TechSee

JULY 20, 2022

With Visual Intelligence (or VI for short), we are bringing the latest computer vision AI technology out of the lab and into the real-world. This is practical AI , or to put it another way, AI for Real Business. Introducing VI Studio. This is AI built for practical impact. And it’s about to change everything. .

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AWS Machine Learning

AUGUST 2, 2023

We are now natively providing that image in Amazon SageMaker Studio so that you gain the high performance, compute, and security benefits of running your experiments on Amazon SageMaker. Use SageMaker Distribution in SageMaker Studio If you have access to an existing Studio domain, you can launch SageMaker Studio.

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AWS Machine Learning

NOVEMBER 8, 2023

Amazon CodeWhisperer is a generative AI coding companion that speeds up software development by making suggestions based on the existing code and natural language comments, reducing the overall development effort and freeing up time for brainstorming, solving complex problems, and authoring differentiated code.

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AWS Machine Learning

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. We decided SageMaker Studio would be a better solution for Boomi’s team to scale this solution quickly to their customers. Why SageMaker?

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AWS Machine Learning

JUNE 8, 2023

The full notebook for this example is available in the SageMaker Studio Lab examples repository. We start experimentation on a local laptop using the open-source distribution, move it to Amazon SageMaker Studio for using a larger instance, and then schedule the notebook as a notebook job.

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AWS Machine Learning

DECEMBER 12, 2022

The Amazon SageMaker Studio landing page provides the option to use JumpStart. To get started with JumpStart, go to the Amazon SageMaker console and open Studio. Refer to Get Started with SageMaker Studio for instructions on how to get started with Studio. He helps hi-tech strategic accounts on their AI and ML journey.

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AWS Machine Learning

OCTOBER 31, 2023

In early 2023, when Schneider Electric decided to automate part of its accounts linking process using artificial intelligence (AI), the company partnered with the AWS Machine Learning Solutions Lab (MLSL). JumpStart provides convenient deployment of this model family through Amazon SageMaker Studio and the SageMaker SDK.

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AWS Machine Learning

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. Both JupyterLab and Code Editor can be launched using a flexible workspace called Spaces.

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AWS Machine Learning

JANUARY 17, 2023

Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning (ML) partly based on JupyterLab 3. Studio provides a web-based interface to interactively perform ML development tasks required to prepare data and build, train, and deploy ML models. Studio construct file. Studio stack file.

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AWS Machine Learning

NOVEMBER 13, 2023

One of the most common applications of generative AI and large language models (LLMs) is answering questions based on a specific external knowledge corpus. To experiment with the sample front end shared with the code, you can use Amazon SageMaker Studio to run a local deployment of the Streamlit app.

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AWS Machine Learning

APRIL 11, 2023

With the integrated development environment (IDE) of Amazon SageMaker Studio with Jupyter Lab 3 , we can build, run, and serve Streamlit web apps from within that same environment for development purposes. A user first accesses Studio through the browser. Inside the Studio EC2 instance exists the example code and dependencies list.

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AWS Machine Learning

FEBRUARY 13, 2024

Self-checkout process BigBasket introduced an AI-powered checkout system in their physical stores that uses cameras to distinguish items uniquely. The BigBasket team was running open source, in-house ML algorithms for computer vision object recognition to power AI-enabled checkout at their Fresho (physical) stores.

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AWS Machine Learning

DECEMBER 1, 2023

In the Who will use Studio? For SageMaker Studio , select the updated or classic version. You may also want to isolate a few subnets and disassociate these subnets from SageMaker Studio and therefore want to remove the subnets from your domains. For this post, we select AWS Identity Center. Choose Next.

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AWS Machine Learning

NOVEMBER 16, 2023

One of the key drivers of Philips’ innovation strategy is artificial intelligence (AI), which enables the creation of smart and personalized products and services that can improve health outcomes, enhance customer experience, and optimize operational efficiency.

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AWS Machine Learning

NOVEMBER 24, 2023

To pave the way for the growth of AI, BMW Group needed to make a leap regarding scalability and elasticity while reducing operational overhead, software licensing, and hardware management. It is powered by Amazon SageMaker Studio and provides JupyterLab for Python and Posit Workbench for R.

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AWS Machine Learning

JUNE 30, 2022

You can access Amazon SageMaker Studio notebooks from the Amazon SageMaker console via AWS Identity and Access Management (IAM) authenticated federation from your identity provider (IdP), such as Okta. The Lambda service role has the source VPC endpoint conditions defined for the SageMaker API and Studio.

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AWS Machine Learning

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

DECEMBER 1, 2022

Recently, the Amazon SageMaker Studio launched an easy way to run notebooks as batch jobs that can run on a recurring schedule. Amazon SageMaker Studio Lab also supports this feature, enabling you to run notebooks that you develop in SageMaker Studio Lab in your AWS account. Solution overview. Prerequisites. Choose Create role.

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AWS Machine Learning

NOVEMBER 30, 2022

We’re thrilled to announce that Stability AI has selected AWS as its preferred cloud provider to power its state-of-the-art AI models for image, language, audio, video, and 3D content generation. Stability AI is a community-driven, open-source artificial intelligence (AI) company developing breakthrough technologies.

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AWS Machine Learning

AUGUST 1, 2023

Advertising agencies can use generative AI and text-to-image foundation models to create innovative ad creatives and content. With the power of state-of-the-art techniques, the creative agency can support their customer by using generative AI models within their secure AWS environment.

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AWS Machine Learning

OCTOBER 13, 2022

AWS offers a pre-trained and fully managed AWS AI service called Amazon Rekognition that can be integrated into computer vision applications using API calls and require no ML experience. Create a Studio project. For detailed instructions on how to set up Studio, refer to Onboard to Amazon SageMaker Domain Using Quick setup.

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AWS Machine Learning

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. Amazon Lex provides the framework for building AI based chatbots. Amazon SageMaker Studio for hosting the Streamlit application.

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APIs Enterprise Accountability Chatbots 78