AWS Machine Learning

MAY 23, 2023

With organizations increasingly investing in machine learning (ML), ML adoption has become an integral part of business transformation strategies. However, implementing ML into production comes with various considerations, notably being able to navigate the world of AI safely, strategically, and responsibly.

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AWS Machine Learning

NOVEMBER 8, 2023

Medical imaging AI researchers and developers need a scalable, enterprise framework to build, deploy, and integrate their AI applications. We have developed a MONAI Deploy connector to AHI to integrate medical imaging AI applications with subsecond image retrieval latencies at scale powered by cloud-native APIs. The model.tar.gz

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AWS Machine Learning

APRIL 19, 2023

The DJL is a deep learning framework built from the ground up to support users of Java and JVM languages like Scala, Kotlin, and Clojure. The DJL is a deep learning framework built from the ground up to support users of Java and JVM languages like Scala, Kotlin, and Clojure. With the DJL, integrating this deep learning is simple.

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AWS Machine Learning

NOVEMBER 17, 2022

Their production segment is therefore an integral building block for delivering on their mission—with a clearly stated ambition to become world-leading on metrics such as safety, environmental footprint, quality, and production costs. To lead this effort, Yara established a global unit called Digital Production.

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AWS Machine Learning

JANUARY 12, 2023

This is guest post by Andy Whittle, Principal Platform Engineer – Application & Reliability Frameworks at The Very Group. Overview of solution. The engineering team developed a schematic demonstrating how a PII redaction service could integrate with The Very Group’s logging.

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AWS Machine Learning

SEPTEMBER 20, 2023

Another example is independent software vendors (ISVs) that make their products and services available in different cloud platforms to benefit their end customers. Key concepts Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning.

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AWS Machine Learning

MAY 19, 2023

Solution Overview The following Reference Architecture shows the workflow of a user taking a picture with a phone and playing an MP3 of the captioning the image. To integrate OFA in our application we cloned the repo from Hugging Face and containerized the model to deploy it to a SageMaker endpoint.

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AWS Machine Learning

AUGUST 4, 2023

This post is co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI. This is the second post in a series discussing the integration of Salesforce Data Cloud and Amazon SageMaker. In this post, we expand on this topic to demonstrate how to use Einstein Studio for product recommendations.

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CafeX

FEBRUARY 21, 2022

Productive internal collaboration. Challo makes the transformation to digital engagement seamless and secure by integrating with. the bank’s larger digital framework. Banks increase customer satisfaction and lifetime value by providing more accessible and. personalized points of service. They can better serve.

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Banking Personalization Technology Customer Experience 71

AWS Machine Learning

MAY 25, 2023

This post was co-authored by Brian Curry (Founder and Head of Products at OCX Cognition) and Sandhya MN (Data Science Lead at InfoGain) OCX Cognition is a San Francisco Bay Area-based startup, offering a commercial B2B software as a service (SaaS) product called Spectrum AI. This reduced the need to develop new low-level ML code.

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AWS Machine Learning

SEPTEMBER 28, 2022

With SageMaker, you can quickly build and train ML models and directly deploy them into a production-ready hosted environment. You get an integrated Jupyter notebook environment with easy access to your data sources. Solution overview. It is not intended for production migrations.

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AWS Machine Learning

JUNE 8, 2023

AWS Deep Learning Containers already provides pre-built Docker images for training and serving models in common frameworks such as TensorFlow, PyTorch, and MXNet. Solution overview For our example, we showcase training an image classification model using PyTorch. We use the KMNIST dataset available publicly on PyTorch.

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AWS Machine Learning

FEBRUARY 29, 2024

OpenSearch uses a probabilistic ranking framework called BM-25 to calculate relevance scores. This framework, however, doesn’t consider user behavior like click-through or purchase data, which can further improve relevance for individual users. Solution overview The following diagram illustrates the solution architecture.

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AWS Machine Learning

JULY 6, 2023

A number of AWS independent software vendor (ISV) partners have already built integrations for users of their software as a service (SaaS) platforms to utilize SageMaker and its various features, including training, deployment, and the model registry. SageMaker Canvas provides a no-code visual interface for training ML models.

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AWS Machine Learning

MARCH 23, 2023

We’ve used the preceding architecture to set up an example using SageMaker endpoints, Pyfhel as an FHE API wrapper simplifying the integration with ML applications, and SEAL as our underlying FHE encryption toolkit. xlarge" framework version="0.23-1", xlarge" framework version="0.23-1", resource("s3").Bucket Bucket (bucket).Object

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AWS Machine Learning

NOVEMBER 29, 2023

How SageMaker smart sifting works We begin this post with an overview of how the smart sifting capability can accelerate your model training on SageMaker. Solution overview In the following sections, we walk through a practical example of enabling smart sifting with a PyTorch training job on SageMaker.

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AWS Machine Learning

FEBRUARY 15, 2023

JumpStart APIs unlock the usage of JumpStart capabilities in your workflows, and integrate with tools such as the model registry that are part of MLOps pipelines and anywhere else you’re interacting with SageMaker via SDK. SageMaker projects are provisioned using AWS Service Catalog products.

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AWS Machine Learning

MAY 10, 2023

Today, data scientists who are training deep learning models need to identify and remediate model training issues to meet accuracy targets for production deployment, and require a way to utilize standard tools for debugging model training. Manoj Ravi is a Senior Product Manager for Amazon SageMaker. x_test / 255.0 session.Session().region_name

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AWS Machine Learning

NOVEMBER 9, 2023

We build a model to predict the severity (benign or malignant) of a mammographic mass lesion trained with the XGBoost algorithm using the publicly available UCI Mammography Mass dataset and deploy it using the MLOps framework. The full instructions with code are available in the GitHub repository.

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AWS Machine Learning

OCTOBER 26, 2022

Capturing the data from inferences made in production can enable you to monitor your deployed models and detect deviations in model quality. Prior to this feature launch, the package size constraint made it difficult to deploy ML frameworks like TensorFlow or PyTorch to Lambda functions. Overview of solution.

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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. Once deployed, model performance is continuously monitored.

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AWS Machine Learning

APRIL 22, 2024

To solve this pain point, we can use Lightweight Directory Access Protocol (LDAP) and LDAP over TLS/SSL (LDAPS) to integrate with a directory service such as AWS Directory Service for Microsoft Active Directory. Basic configuration parameters class Config: : # Set true if you want to install SSSD for ActiveDirectory/LDAP integration. #

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AWS Machine Learning

JULY 29, 2022

For an overview of the business context and the steps to set up a data mesh with AWS Lake Formation and register a data product, refer to part 1. The platform team sets up the ML environment for the data scientists and helps them get access to the necessary data products in the data mesh.

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Peter Lavers

JULY 7, 2015

A holistic view of the inherent complexity of customer-centricity and the business model that is required for an integrated, systems-thinking approach to overcome this complexity. The next session covered the vital importance of taking a systemic approach to customer management to ensure end-to-end integration.

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AWS Machine Learning

JUNE 8, 2023

Solution overview The solution integrates an Amazon Lex bot with a popular open-source LLM from Amazon SageMaker JumpStart , accessible through an Amazon SageMaker endpoint. We also use LangChain, a popular framework that simplifies LLM-powered applications. Finally, we use a QnABot to provide a user interface for our chatbot.

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AWS Machine Learning

MAY 23, 2023

Original content production, code generation, customer service enhancement, and document summarization are typical use cases of generative AI. With over 600 pre-trained models available and growing every day, JumpStart enables developers to quickly and easily incorporate cutting-edge ML techniques into their production workflows.

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NobelBiz

FEBRUARY 13, 2024

This goes to show the importance for call centers to adapt and integrate these advancements into their processes. Furthermore, the NobelBiz OMNI+ Automatic Preview Dialer excels in integrating client records and relevant data, meticulously organizing them for agents’ review.

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AWS Machine Learning

APRIL 5, 2023

You can use CLIP to encode your products’ images or description into embeddings , and then store them into an OpenSearch Service k-NN index. Then your customers can query the index to retrieve products that they’re interested in. Solution overview OpenSearch Service provides text-matching and embedding k-NN search.

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AWS Machine Learning

FEBRUARY 23, 2023

AV systems fuse data from the devices that are integrated together to build a comprehensive perception. This integrated dataset is used to train the DNN. Some providers use a dormant model strategy and deploy candidate models (dormant) side by side with the production models.

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AWS Machine Learning

OCTOBER 4, 2022

Overview of the ML lifecycle. These templates are automatically available to your organization, and are provisioned using AWS Service Catalog products. You can use local mode training with multiple AWS-supported framework images (TensorFlow, MXNet, Chainer, PyTorch, and Scikit-Learn) as well as images you supply yourself.

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AWS Machine Learning

OCTOBER 6, 2023

The applications also extend into retail, where they can enhance customer experiences through dynamic chatbots and AI assistants, and into digital marketing, where they can organize customer feedback and recommend products based on descriptions and purchase behaviors.

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Nicereply

JUNE 18, 2020

Every business, product, and support team is bound to have some quirks and weak spots. Oftentimes your customers discover things about your product still hidden to your team. Once you know what the issue is, you can use this knowledge to improve your product, educate your agents, or tweak your support process to fix those issues.

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AWS Machine Learning

AUGUST 3, 2023

For example, on online shopping platforms, the angle at which products are shown in images has an effect on the rate of buying this product. Finally, we show how you can integrate this car pose detection solution into your existing web application using services like Amazon API Gateway and AWS Amplify.

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AWS Machine Learning

NOVEMBER 7, 2023

The model then skillfully integrates this outside information into its generated text. This integration provides precise and context-aware answers to complex queries by drawing from a diverse range of sources. RAG empowers LLMs by giving them the ability to retrieve and incorporate external knowledge.

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AWS Machine Learning

SEPTEMBER 28, 2022

Pipelines continues to innovate its developer experience, and with these recent releases, you can now use the service in a more customized way: 2.99.0 , 2.101.1 , 2.102.0 , 2.104.0 – Updated documentation on PipelineVariable usage for estimator, processor, tuner, transformer, and model base classes, Amazon models, and framework models.

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AWS Machine Learning

NOVEMBER 16, 2022

support all upstream open-source Kubeflow features and include many new integrations with the highly optimized, cloud-native, enterprise-ready AWS services that will help you build highly reliable, secure, portable, and scalable ML systems. Integration with AWS DLCs to run distributed training and inference workloads. Kubeflow on AWS.

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AWS Machine Learning

NOVEMBER 9, 2022

In 2021, AWS announced the integration of NVIDIA Triton Inference Server in SageMaker. By using an NVIDIA Triton container image, you can easily serve ML models and benefit from the performance optimizations, dynamic batching, and multi-framework support provided by NVIDIA Triton. Solution overview.

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