AWS Machine Learning Blog

NOVEMBER 15, 2024

The integration of these multimodal capabilities has unlocked new possibilities for businesses and individuals, revolutionizing fields such as content creation, visual analytics, and software development. In the metadata.jsonl file, each example is a dictionary that contains three keys named file_name , prompt , and completion.

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AWS Machine Learning Blog

JULY 1, 2024

Create a knowledge base To create a new knowledge base in Amazon Bedrock, complete the following steps. For Data source name , Amazon Bedrock prepopulates the auto-generated data source name; however, you can change it to your requirements. You should see a Successfully built message when the build is complete. Choose Next.

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AWS Machine Learning Blog

MAY 9, 2023

In addition, you can now use Application Auto Scaling with provisioned concurrency to address inference traffic dynamically based on target metrics or a schedule. In this post, we discuss what provisioned concurrency and Application Auto Scaling are, how to use them, and some best practices and guidance for your inference workloads.

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AWS Machine Learning Blog

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 Blog

NOVEMBER 14, 2024

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts.

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AWS Machine Learning Blog

OCTOBER 19, 2023

With the Custom Moderation feature, you can tailor the Rekognition pre-trained moderation model for improved performance on your specific moderation use case, without any machine learning (ML) expertise. To create your project, complete the following steps: On the Amazon Rekognition console, choose Custom Moderation in the navigation pane.

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AWS Machine Learning Blog

AUGUST 5, 2024

Thus, customers often experiment with larger and newer models to build ML-based products that bring value. Next, we perform auto-regressive token generation where the output tokens are generated sequentially. This means we will be repeating this process more times to complete the response, resulting in slower overall processing.

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AWS Machine Learning Blog

OCTOBER 11, 2023

The decode phase includes the following: Completion – After the prefill phase, you have a partially generated text that may be incomplete or cut off at some point. The decode phase is responsible for completing the text to make it coherent and grammatically correct. The default is 32.

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AWS Machine Learning Blog

JUNE 23, 2023

Amazon SageMaker Data Wrangler is a single visual interface that reduces the time required to prepare data and perform feature engineering from weeks to minutes with the ability to select and clean data, create features, and automate data preparation in machine learning (ML) workflows without writing any code.

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AWS Machine Learning Blog

APRIL 8, 2024

This version offers support for new models (including Mixture of Experts), performance and usability improvements across inference backends, as well as new generation details for increased control and prediction explainability (such as reason for generation completion and token level log probabilities).

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AWS Machine Learning Blog

DECEMBER 13, 2023

We use the AWS Neuron software development kit (SDK) to access the AWS Inferentia2 device and benefit from its high performance. To deploy models on Inf2, we need AWS Neuron SDK as the software layer running on top of the Inf2 hardware. The complete code samples with instructions can be found in this GitHub repository.

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AWS Machine Learning Blog

DECEMBER 4, 2023

We capitalized on the powerful tools provided by AWS to tackle this challenge and effectively navigate the complex field of machine learning (ML) and predictive analytics. SageMaker is a fully managed ML service. The project was completed in a month and deployed to production after a week of testing.

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AWS Machine Learning Blog

MAY 31, 2023

Running machine learning (ML) workloads with containers is becoming a common practice. What you get is an ML development environment that is consistent and portable. In this post, we show you how to run your ML training jobs in a container using Amazon ECS to deploy, manage, and scale your ML workload.

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AWS Machine Learning Blog

JUNE 11, 2024

This allows machine learning (ML) practitioners to rapidly launch an Amazon Elastic Compute Cloud (Amazon EC2) instance with a ready-to-use deep learning environment, without having to spend time manually installing and configuring the required packages. You also need the ML job scripts ready with a command to invoke them.

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AWS Machine Learning Blog

JUNE 13, 2023

LMI DLCs are a complete end-to-end solution for hosting LLMs like Falcon-40B. Quotas for SageMaker machine learning (ML) instances can vary between accounts. You can monitor the status of the endpoint by calling DescribeEndpoint , which will tell you when everything is complete. code_falcon40b_deepspeed/model.py

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AWS Machine Learning Blog

AUGUST 4, 2023

Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare tabular and image data for machine learning (ML) from weeks to minutes. You can build an ML model with SageMaker Autopilot representing all your data using the manifest file and use that for your ML inference and production deployment.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

With SageMaker HyperPod, machine learning (ML) practitioners can train FMs for weeks and months without disruption, and without having to deal with hardware failure issues. Auto-resume and healing capabilities One of the new features with SageMaker HyperPod is the ability to have auto-resume on your jobs. pretrain-model.sh

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AWS Machine Learning Blog

JULY 9, 2024

When the job is complete, you can deploy to an endpoint and test the model predictions. After the optimization job is complete, you can deploy the model or run further evaluation jobs on the optimized model. If config name is lmi-optimized , that means the configuration is pre-optimized by SageMaker.

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AWS Machine Learning Blog

OCTOBER 8, 2024

In this blog post, we explore a comprehensive approach to time series forecasting using the Amazon SageMaker AutoMLV2 Software Development Kit (SDK). In the training phase, CSV data is uploaded to Amazon S3, followed by the creation of an AutoML job, model creation, and checking for job completion.

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AWS Machine Learning Blog

MAY 4, 2023

The world of artificial intelligence (AI) and machine learning (ML) has been witnessing a paradigm shift with the rise of generative AI models that can create human-like text, images, code, and audio. Compared to classical ML models, generative AI models are significantly bigger and more complex.

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AWS Machine Learning Blog

JULY 31, 2023

Although machine learning (ML) can provide valuable insights, ML experts were needed to build customer churn prediction models until the introduction of Amazon SageMaker Canvas. Additional key topics Advanced metrics are not the only important tools available to you for evaluating and improving ML model performance.

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The MLOps Blog

JANUARY 23, 2023

Just so you know where I am coming from: I have a heavy software development background (15+ years in software). Came to ML from software. Founded two successful software services companies. Founded neptune.ai , a modular MLOps component for ML metadata store , aka “experiment tracker + model registry”.

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The MLOps Blog

FEBRUARY 27, 2023

This article was originally an episode of the MLOps Live , an interactive Q&A session where ML practitioners answer questions from other ML practitioners. Every episode is focused on one specific ML topic, and during this one, we talked to Michal Tadeusiak about managing computer vision projects. Then we are there to help.

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AWS Machine Learning Blog

APRIL 17, 2023

Compilation or integration to optimized runtime ML compilers, such as Amazon SageMaker Neo , apply techniques such as operator fusion, memory planning, graph optimizations, and automatic integration to optimized inference libraries. A complete example that illustrates the no-code option can be found in the following notebook.

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Google Research AI blog

JANUARY 18, 2023

Language Models Computer Vision Multimodal Models Generative Models Responsible AI* Algorithms ML & Computer Systems Robotics Health General Science & Quantum Community Engagement * Other articles in the series will be linked as they are released. The pixels in the same colors are attended together.

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The MLOps Blog

JULY 18, 2023

This article was originally an episode of the MLOps Live , an interactive Q&A session where ML practitioners answer questions from other ML practitioners. Every episode is focused on one specific ML topic, and during this one, we talked to Jason Falks about deploying conversational AI products to production.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

Llama 2 stands at the forefront of AI innovation, embodying an advanced auto-regressive language model developed on a sophisticated transformer foundation. The complete example is shown in the accompanying notebook. He holds a master’s degree in Computer Science & Software Engineering from the University of Syracuse.

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AWS Machine Learning Blog

OCTOBER 18, 2023

Purina used artificial intelligence (AI) and machine learning (ML) to automate animal breed detection at scale. The solution focuses on the fundamental principles of developing an AI/ML application workflow of data preparation, model training, model evaluation, and model monitoring. DynamoDB is used to store the pet attributes.

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AWS Machine Learning Blog

FEBRUARY 19, 2024

As a result, an initial invocation to a model might see higher inference latency than the subsequent inferences, which are completed with low latency. To take advantage of automated model scaling in SageMaker, make sure you have instance auto scaling set up to provision additional instance capacity.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

Because FM outputs could range from a single sentence to multiple paragraphs, the time it takes to complete the inference request varies significantly, leading to unpredictable spikes in latency if the requests are routed randomly between instances. You can scale down to zero copies of a model to free up resources for other models.

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Unite.AI

MAY 28, 2024

Before MonsterAPI, he ran two startups, including one that developed a wearable safety device for women in India, in collaboration with the Government of India and IIT Delhi. Our Mission has always been “to help software developers fine-tune and deploy AI models faster and in the easiest manner possible.”

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AWS Machine Learning Blog

JANUARY 17, 2024

Llama 2 is an auto-regressive generative text language model that uses an optimized transformer architecture. Trainium and AWS Inferentia, enabled by the AWS Neuron software development kit (SDK), offer a high-performance, and cost effective option for training and inference of Llama 2 models.

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Flipboard

APRIL 13, 2023

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

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AWS Machine Learning Blog

JUNE 5, 2023

You can find our complete example notebook here. Qing Lan is a Software Development Engineer in AWS. He has been working on several challenging products in Amazon, including high performance ML inference solutions and high performance logging system. Xin Yang is a Software Development Engineer at AWS.

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AWS Machine Learning Blog

JULY 23, 2024

is an auto-regressive language model that uses an optimized transformer architecture. SageMaker Studio is a comprehensive integrated development environment (IDE) that offers a unified, web-based interface for performing all aspects of the machine learning (ML) development lifecycle. The Llama 3.1 At its core, Llama 3.1

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