AWS Machine Learning

AUGUST 11, 2022

This post shows you how to use an integrated solution with Amazon Lookout for Metrics and Amazon Kinesis Data Firehose to break these barriers by quickly and easily ingesting streaming data, and subsequently detecting anomalies in the key performance indicators of your interest. You don’t need ML experience to use Lookout for Metrics.

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AWS Machine Learning

JULY 2, 2024

Image 2: Hugging Face NLP model inference performance improvement with torch.compile on AWS Graviton3-based c7g instance using Hugging Face example scripts. This section shows how to run inference in eager and torch.compile modes using torch Python wheels and benchmarking scripts from Hugging Face and TorchBench repos.

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AWS Machine Learning

MAY 30, 2024

AWS Prototyping successfully delivered a scalable prototype, which solved CBRE’s business problem with a high accuracy rate (over 95%) and supported reuse of embeddings for similar NLQs, and an API gateway for integration into CBRE’s dashboards. The following diagram illustrates the web interface and API management layer.

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AWS Machine Learning

JANUARY 8, 2024

The first allows you to run a Python script from any server or instance including a Jupyter notebook; this is the quickest way to get started. In the following sections, we first describe the script solution, followed by the AWS CDK construct solution. The following diagram illustrates the sequence of events within the script.

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AWS Machine Learning

APRIL 12, 2023

All the training and evaluation metrics were inspected manually from Amazon Simple Storage Service (Amazon S3). The code to invoke the pipeline script is available in the Studio notebooks, and we can change the hyperparameters and input/output when invoking the pipeline.

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AWS Machine Learning

MAY 16, 2024

The main AWS services used are SageMaker, Amazon EMR , AWS CodeBuild , Amazon Simple Storage Service (Amazon S3), Amazon EventBridge , AWS Lambda , and Amazon API Gateway. Real-time recommendation inference The inference phase consists of the following steps: The client application makes an inference request to the API gateway.

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Spearline

JULY 21, 2022

Consequently, no other testing solution can provide the range and depth of testing metrics and analytics. And testingRTC offers multiple ways to export these metrics, from direct collection from webhooks, to downloading results in CSV format using the REST API. Happy days! You can check framerate information for video here too.

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AWS Machine Learning

MAY 31, 2024

Lastly the model is tested against a set of known genome sequences using some inference API calls. Training on SageMaker We use PyTorch and Amazon SageMaker script mode to train this model. Script mode’s compatibility with PyTorch was crucial, allowing us to use our existing scripts with minimal modifications.

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AWS Machine Learning

APRIL 17, 2024

Metrics allow teams to understand workload behavior and optimize resource allocation and utilization, diagnose anomalies, and increase overall infrastructure efficiency. Metrics are exposed to Amazon Managed Service for Prometheus by the neuron-monitor DaemonSet, which deploys a minimal container, with the Neuron tools installed.

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AWS Machine Learning

SEPTEMBER 8, 2023

Amazon Rekognition makes it easy to add image analysis capability to your applications without any machine learning (ML) expertise and comes with various APIs to fulfil use cases such as object detection, content moderation, face detection and analysis, and text and celebrity recognition, which we use in this example.

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AWS Machine Learning

FEBRUARY 25, 2025

Amazon Q Business only provides metric information that you can use to monitor your data source sync jobs. We recommend running similar scripts only on your own data sources after consulting with the team who manages them, or be sure to follow the terms of service for the sources that youre trying to fetch data from.

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AWS Machine Learning

APRIL 23, 2024

Here are some features which we will cover: AWS CloudFormation support Private network policies for Amazon OpenSearch Serverless Multiple S3 buckets as data sources Service Quotas support Hybrid search, metadata filters, custom prompts for the RetreiveAndGenerate API, and maximum number of retrievals.

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AWS Machine Learning

JANUARY 10, 2023

From there, we dive into how you can track and understand the metrics and performance of the SageMaker endpoint utilizing Amazon CloudWatch metrics. Metrics to track. Before we can get into load testing, it’s essential to understand what metrics to track to understand the performance breakdown of your SageMaker endpoint.

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AWS Machine Learning

JANUARY 5, 2024

Continuous integration and continuous delivery (CI/CD) pipeline – Using the customer’s GitHub repository enabled code versioning and automated scripts to launch pipeline deployment whenever new versions of the code are committed. Wipro has used the input filter and join functionality of SageMaker batch transformation API.

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AWS Machine Learning

APRIL 19, 2024

Solution overview In this section, we present a generic architecture that is similar to the one we use for our own workloads, which allows elastic deployment of models using efficient auto scaling based on custom metrics. The reverse proxy collects metrics about calls to the service and exposes them via a standard metrics API to Prometheus.

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AWS Machine Learning

AUGUST 30, 2023

For a quantitative analysis of the generated impression, we use ROUGE (Recall-Oriented Understudy for Gisting Evaluation), the most commonly used metric for evaluating summarization. This metric compares an automatically produced summary against a reference or a set of references (human-produced) summary or translation.

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AWS Machine Learning

FEBRUARY 6, 2023

Together with the implementation details in a corresponding example Jupyter notebook , you will have tools available to perform model selection by exploring pareto frontiers, where improving one performance metric, such as accuracy, is not possible without worsening another metric, such as throughput.

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AWS Machine Learning

FEBRUARY 19, 2024

An asynchronous API and Amazon OpenSearch Service connector make it easy to integrate the model into your neural search applications. Before you can write scripts that use the Amazon Bedrock API, you need to install the appropriate version of the AWS SDK in your environment. The vectors power speedy, accurate search experiences.

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AWS Machine Learning

FEBRUARY 5, 2025

Lets delve into a basic Colang script to see how it works: define user express greeting "hello" "hi" "what's up?" define flow greeting user express greeting bot express greeting bot ask how are you In this script, we see the three fundamental types of blocks in Colang: User Message Blocks (define user ): These define possible user inputs.

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AWS Machine Learning

JULY 24, 2023

If the model changes on the server side, the client has to know and change its API call to the new endpoint accordingly. Based on these metrics an informed decision can be made. Clone the Github repository The GitHub repo provides all the scripts necessary to deploy models using FastAPI on NeuronCores on AWS Inferentia instances.

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AWS Machine Learning

NOVEMBER 1, 2022

Autopilot training jobs start their own dedicated SageMaker backend processes, and dedicated SageMaker API calls are required to start new training jobs, monitor training job statuses, and invoke trained Autopilot models. We use a Lambda step because the API call to Autopilot is lightweight. script creates an Autopilot job.

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AWS Machine Learning

OCTOBER 2, 2023

For this we use AWS Step Functions , a serverless workflow service that provides us with API integrations to quickly orchestrate and visualize the steps in our workflow. Use the scripts created in step one as part of the processing and training steps. We started by creating command line scripts from the experiment code.

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AWS Machine Learning

JUNE 27, 2023

The ML components for data ingestion, preprocessing, and model training were available as disjointed Python scripts and notebooks, which required a lot of manual heavy lifting on the part of engineers. This step produces an expanded report containing the model’s metrics.

Engineering 115

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AWS Machine Learning

MAY 10, 2023

It provides a suite of tools for visualizing training metrics, examining model architectures, exploring embeddings, and more. When they create a SageMaker training job, domain users can use TensorBoard using the SageMaker Python SDK or Boto3 API. is your training script, and simple_tensorboard.ipynb launches the SageMaker training job.

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AWS Machine Learning

APRIL 29, 2024

Dataset collection We followed the methodology outlined in the PMC-Llama paper [6] to assemble our dataset, which includes PubMed papers sourced from the Semantic Scholar API and various medical texts cited within the paper, culminating in a comprehensive collection of 88 billion tokens. Create and launch ParallelCluster in the VPC.

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AWS Machine Learning

AUGUST 10, 2023

SageMaker services, such as Processing, Training, and Hosting, collect metrics and logs from the running instances and push them to users’ Amazon CloudWatch accounts. One example is performing a metric query on the SageMaker job host’s utilization metrics when a job completion event is received.

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AWS Machine Learning

DECEMBER 21, 2022

A new optional parameter TableFormat can be set either interactively using Amazon SageMaker Studio or through code using the API or the SDK. The following code snippet shows you how to create a feature group using the Iceberg format and FeatureGroup.create API of the SageMaker SDK. You can find the sample script in GitHub.

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AWS Machine Learning

JULY 25, 2024

If it detects error messages specifically related to the Neuron device (which is the Trainium or AWS Inferentia chip), it will change NodeCondition to NeuronHasError on the Kubernetes API server. The node recovery agent is a separate component that periodically checks the Prometheus metrics exposed by the node problem detector.

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AWS Machine Learning

NOVEMBER 25, 2022

Defining the right objective metric matching your task. When our tuning job is complete, we look at some of the methods available to explore the results, both via the AWS Management Console and programmatically via the AWS SDKs and APIs. Collects metrics and logs. Amazon SageMaker Automatic Model Tuning. Runs the training.

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AWS Machine Learning

MAY 8, 2023

In this post, we address these limitations by implementing the access control outside of the MLflow server and offloading authentication and authorization tasks to Amazon API Gateway , where we implement fine-grained access control mechanisms at the resource level using Identity and Access Management (IAM). Adds an IAM authorizer.

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AWS Machine Learning

SEPTEMBER 26, 2024

This text-to-video API generates high-quality, realistic videos quickly from text and images. Customizable environment – SageMaker HyperPod offers the flexibility to customize your cluster environment using lifecycle scripts. Video generation has become the latest frontier in AI research, following the success of text-to-image models.

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AWS Machine Learning

JANUARY 19, 2024

The goal of NAS is to find the optimal architecture for a given problem by searching over a large set of candidate architectures using techniques such as gradient-free optimization or by optimizing the desired metrics. The performance of the architecture is typically measured using metrics such as validation loss. training.py ).

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AWS Machine Learning

MAY 5, 2023

By uploading a small set of training images, Amazon Rekognition automatically loads and inspects the training data, selects the right ML algorithms, trains a model, and provides model performance metrics. A Python script is used to aid in the process of uploading the datasets and generating the manifest file.

Engineering 104

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AWS Machine Learning

MAY 31, 2023

Triton with PyTorch backend The PyTorch backend is designed to run TorchScript models using the PyTorch C++ API. Alternatively, you can use ensemble models or business logic scripting. file in the workspace directory contains scripts to load and save a PyTorch model. client(service_name="sagemaker") runtime_sm_client = boto3.client("sagemaker-runtime")

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AWS Machine Learning

JULY 14, 2022

This post walks you through an example of how to track your experiments across code, data, artifacts, and metrics by using Amazon SageMaker Experiments in conjunction with Data Version Control (DVC). In each individual experiment, we track input and output artifacts, code, and metrics using SageMaker Experiments. SageMaker Experiments.

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AWS Machine Learning

NOVEMBER 21, 2022

Evaluate model performance on the hold-out test data with various evaluation metrics. This notebook demonstrates how to use the JumpStart API for text classification. After the fine-tuning job is complete, we deploy the model, run inference on the hold-out test dataset, and compute evaluation metrics. Text classification.

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AWS Machine Learning

AUGUST 29, 2023

The data scientist then needs to review and manually approve the latest version of the model in the Amazon SageMaker Studio UI or via an API call using the AWS Command Line Interface (AWS CLI) or AWS SDK for Python (Boto3) before the new version of model can be utilized for inference.

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