AWS Machine Learning Blog

OCTOBER 26, 2023

Our innovative new A-POPs (or vending machines) deliver enhanced customer experiences at ten times lower cost because of the performance and cost advantages AWS Inferentia delivers. Unlocking high-performance and cost-effective inference using AWS Inferentia As retailers look to scale operations, cost of A-POPs becomes a consideration.

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AWS Machine Learning Blog

FEBRUARY 23, 2023

You can use the SageMaker Python SDK to trigger a job with data parallelism with minimal modifications to the training script. Data parallelism supports popular deep learning frameworks PyTorch, PyTorch Lightening, TensorFlow, and Hugging Face Transformers.

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Mlearning.ai

JANUARY 28, 2023

From Sale Marketing Business 7 Powerful Python ML For Data Science And Machine Learning need to be use. This post will outline seven powerful python ml libraries that can help you in data science and different python ml environment. A python ml library is a collection of functions and data that can use to solve problems.

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AWS Machine Learning Blog

SEPTEMBER 20, 2023

For example, you might have acquired a company that was already running on a different cloud provider, or you may have a workload that generates value from unique capabilities provided by AWS. We show how you can build and train an ML model in AWS and deploy the model in another platform.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

Amazon Elastic Compute Cloud (Amazon EC2) DL2q instances, powered by Qualcomm AI 100 Standard accelerators, can be used to cost-efficiently deploy deep learning (DL) workloads in the cloud. Set up the environment and install required packages Install Python 3.8. Set up the Python 3.8 This is a guest post by A.K

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AWS Machine Learning Blog

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. With the DJL, integrating this deep learning is simple. Our data scientists train the model in Python using tools like PyTorch and save the model as PyTorch scripts.

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AWS Machine Learning Blog

NOVEMBER 21, 2023

Amazon SageMaker provides an end-to-end set of services that allow Amazon Music to build, train, and deploy on the AWS Cloud with minimal effort. By taking care of the undifferentiated heavy lifting, SageMaker allows you to focus on working on your machine learning (ML) models, and not worry about things such as infrastructure.

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AWS Machine Learning Blog

MAY 2, 2024

For Recipe , choose the new aws-user-personalization-v2 recipe. You can delete campaigns, datasets, and dataset groups via the Amazon Personalize console or using the Python SDK. About the Authors Jingwen Hu is a Senior Technical Product Manager working with AWS AI/ML on the Amazon Personalize team. Choose your dataset group.

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AWS Machine Learning Blog

FEBRUARY 20, 2024

With an aim to accelerate the localization of content workflows through machine learning, ZOO Digital engaged AWS Prototyping, an investment program by AWS to co-build workloads with customers. This S3 bucket was configured to emit an event when new files are detected within it, triggering an AWS Lambda function.

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AWS Machine Learning Blog

MARCH 8, 2023

The workflow steps are as follows: Set up a SageMaker notebook and an AWS Identity and Access Management (IAM) role with appropriate permissions to allow SageMaker to access Amazon Elastic Container Registry (Amazon ECR), Secrets Manager, and other services within your AWS account. AWS Region Link us-east-1 (N.

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AWS Machine Learning Blog

MARCH 20, 2023

In the context of deep learning, the predominant numerical format used for research and deployment has so far been 32-bit floating point, or FP32. However, the need for reduced bandwidth and compute requirements of deep learning models has driven research into using lower-precision numerical formats.

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AWS Machine Learning Blog

JANUARY 16, 2024

Additionally, you can list the required Python packages in a requirements.txt file. During the model’s deployment, these Python packages are automatically installed in the initialization phase. Then we select either the PyTorch or Hugging Face deep learning containers (DLC) provided and maintained by AWS.

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Towards AI

APRIL 19, 2024

Amazon (AWS) 👉Industry domain: Online retail and web services provider 👉Location: Over 175 Amazon fulfillment centers globally 👉Year founded: 1994 👉Key Products developed: Amazon Bedrock, Q, Code Whisperer, Sage Maker 👉Benefits: Fully managed generative AI service options, AWS free tier for experimentation 7.

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AWS Machine Learning Blog

AUGUST 2, 2023

SageMaker Distribution is a pre-built Docker image containing many popular packages for machine learning (ML), data science, and data visualization. This includes deep learning frameworks like PyTorch, TensorFlow, and Keras; popular Python packages like NumPy, scikit-learn, and pandas; and IDEs like JupyterLab.

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AWS Machine Learning Blog

DECEMBER 12, 2023

The launch of ChatGPT and rise in popularity of generative AI have captured the imagination of customers who are curious about how they can use this technology to create new products and services on AWS, such as enterprise chatbots, which are more conversational. Optionally, deploy the application using AWS Amplify. Choose Deploy.

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ODSC - Open Data Science

FEBRUARY 17, 2023

Developing NLP tools isn’t so straightforward, and requires a lot of background knowledge in machine & deep learning, among others. Machine & Deep Learning Machine learning is the fundamental data science skillset, and deep learning is the foundation for NLP.

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AWS Machine Learning Blog

APRIL 29, 2024

For AWS and Outerbounds customers, the goal is to build a differentiated machine learning and artificial intelligence (ML/AI) system and reliably improve it over time. First, the AWS Trainium accelerator provides a high-performance, cost-effective, and readily available solution for training and fine-tuning large models.

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AWS Machine Learning Blog

FEBRUARY 29, 2024

Knowledge Bases for Amazon Bedrock allows you to build performant and customized Retrieval Augmented Generation (RAG) applications on top of AWS and third-party vector stores using both AWS and third-party models. You can also use the StartIngestionJob API to trigger the sync via the AWS SDK.

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Flipboard

NOVEMBER 30, 2023

Although it provides various entry points like the SageMaker Python SDK, AWS SDKs, the SageMaker console, and Amazon SageMaker Studio notebooks to simplify the process of training and deploying ML models at scale, customers are still looking for better ways to deploy their models for playground testing and to optimize production deployments.

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AssemblyAI

AUGUST 27, 2023

Let’s look at three of the most popular Speech-to-Text APIs and AI models with a free tier: AssemblyAI, Google, and AWS Transcribe. You can even copy/paste code examples in your preferred language directly from the AssemblyAI Docs or use the AssemblyAI Python SDK.

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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. We demonstrate how you can combine well-known ML solutions with postprocessing to address this problem on the AWS Cloud. We use deep learning models to solve this problem.

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ODSC - Open Data Science

FEBRUARY 2, 2023

While knowing Python, R, and SQL are expected, you’ll need to go beyond that. As you’ll see in the next section, data scientists will be expected to know at least one programming language, with Python, R, and SQL being the leaders. This will lead to algorithm development for any machine or deep learning processes.

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AWS Machine Learning Blog

JULY 24, 2023

Recently, the AWS Generative AI Innovation Center collaborated with Patsnap to implement a feature to automatically suggest search keywords as an innovation exploration to improve user experiences on their platform. Install the required Python packages. 2458 277 1172 AWS TensorRT version (on p3.2xlarge) 1 7 (+4.6) times faster).

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AWS Machine Learning Blog

DECEMBER 13, 2023

In this post, we showcase fine-tuning a Llama 2 model using a Parameter-Efficient Fine-Tuning (PEFT) method and deploy the fine-tuned model on AWS Inferentia2. We use the AWS Neuron software development kit (SDK) to access the AWS Inferentia2 device and benefit from its high performance.

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AWS Machine Learning Blog

MARCH 23, 2023

This is joint post co-written by Leidos and AWS. Leidos has partnered with AWS to develop an approach to privacy-preserving, confidential machine learning (ML) modeling where you build cloud-enabled, encrypted pipelines. In the following sections, we walk through the code to build this pipeline. resource("s3").Bucket

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AWS Machine Learning Blog

MAY 25, 2023

We use a combination of different AWS services, open-source foundation models ( FLAN-T5 XXL for text generation and GPT-j-6B for embeddings) and packages such as LangChain for interfacing with all the components and Streamlit for building the bot frontend. AWS Identity and Access Management roles and policies for access management.

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AWS Machine Learning Blog

JANUARY 9, 2024

When it comes to deploying models on SageMaker endpoints, you can containerize the models using specialized AWS Deep Learning Container (DLC) images available for popular open source libraries. The possible values include Python , DeepSpeed , FasterTransformer , and MPI. In this case, we set it to MPI.

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AWS Machine Learning Blog

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. About the authors Dr. Baichuan Sun is a Senior Data Scientist at AWS AI/ML.

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AWS Machine Learning Blog

DECEMBER 14, 2023

To reduce the effort of preparing training data, we built a pre-labeling tool using AWS Step Functions that automatically pre-annotates documents by using existing tabular entity data. Architecture The pre-labeling tool consists of multiple AWS Lambda functions orchestrated by a Step Functions state machine.

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AWS Machine Learning Blog

JUNE 8, 2023

LLMs are based on the Transformer architecture , a deep learning neural network introduced in June 2017 that can be trained on a massive corpus of unlabeled text. This enables you to begin machine learning (ML) quickly. It includes the FLAN-T5-XL model , an LLM deployed into a deep learning container.

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ODSC - Open Data Science

AUGUST 15, 2023

Build Classification and Regression Models with Spark on AWS Suman Debnath | Principal Developer Advocate, Data Engineering | Amazon Web Services This immersive session will cover optimizing PySpark and best practices for Spark MLlib. Free and paid passes are available now–register here.

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AWS Machine Learning Blog

AUGUST 14, 2023

SageMaker JumpStart SageMaker JumpStart serves as a model hub encapsulating a broad array of deep learning models for text, vision, audio, and embedding use cases. With over 500 models, its model hub comprises both public and proprietary models from AWS’s partners such as AI21, Stability AI, Cohere, and LightOn.

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ODSC - Open Data Science

APRIL 3, 2023

Data Science & Machine Learning There’s an increasing amount of overlap between data scientists and data analysts, as shown by the frameworks and tools noted in each chart. Cloud Services: Google Cloud Platform, AWS, Azure. Knowing the entire suite of Microsoft Office tools doesn’t hurt, either.

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Mlearning.ai

JUNE 14, 2023

Learning LLMs (Foundational Models) Base Knowledge / Concepts: What is AI, ML and NLP Introduction to ML and AI — MFML Part 1 — YouTube What is NLP (Natural Language Processing)? — YouTube Deploy LLMs in production Deploy Model Azure —  Use endpoints for inference — Azure Machine Learning | Microsoft Learn AWS + Huggingface —  Exporting ?

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AWS Machine Learning Blog

MAY 1, 2024

Llama2 by Meta is an example of an LLM offered by AWS. To learn more about Llama 2 on AWS, refer to Llama 2 foundation models from Meta are now available in Amazon SageMaker JumpStart. Virginia) and US West (Oregon) AWS Regions, and most recently announced general availability in the US East (Ohio) Region.

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AWS Machine Learning Blog

APRIL 10, 2023

We explained in a previous post how you can use Amazon SageMaker deep learning containers (DLCs) to deploy these kinds of large models using a GPU-based instance. In this post, we take the same approach but host the model on AWS Inferentia2. For benchmark performance figures, refer to AWS Neuron Performance.

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ODSC - Open Data Science

JANUARY 10, 2024

In this session, you’ll have the opportunity to explore examples of LLM-powered applications in Python using popular AI orchestrators, such as LangChain. I will also cover how smaller, domain-specific models can outperform general-purpose foundation models like ChatGPT on target use cases.

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