AWS Machine Learning

NOVEMBER 14, 2024

Customers can use the SageMaker Studio UI or APIs to specify the SageMaker Model Registry model to be shared and grant access to specific AWS accounts or to everyone in the organization. We will start by using the SageMaker Studio UI and then by using APIs.

Government 88

Government Management APIs Accountability 88

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.

Scripts 96

Scripts APIs Metrics Best practices 96

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.

Metrics 73

Metrics APIs Scripts Wireless 73

AWS Machine Learning

NOVEMBER 15, 2024

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

APIs 107

APIs Government Best practices Metrics 107

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.

Management 126

Management APIs Engineering Government 126

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.

Personalization 112

Personalization APIs Scripts Technical Support 112

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.

APIs 116

APIs Metrics Accountability Management 116

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.

APIs 123

APIs Enterprise Scripts Accountability 123

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.

Biotechnology 101

Biotechnology Scripts Healthcare APIs 101

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.

APIs 115

APIs Scripts Entertainment Personalization 115

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.

Metrics 119

Metrics APIs Scripts Construction 119

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.

Healthcare 127

Healthcare APIs Scripts Metrics 127

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.

Scripts 115

Scripts APIs Entertainment Engineering 115

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.

Best practices 80

Best practices Scripts APIs Metrics 80

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 114

Engineering APIs Scripts Entertainment 114

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 109

Engineering APIs Scripts Enterprise 109

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.

APIs 120

APIs Healthcare Scripts Enterprise 120

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.

Scripts 112

Scripts Construction Engineering APIs 112

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 ).

Metrics 113

Metrics Scripts Benchmark Enterprise 113

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.

Construction 109

Construction Metrics Scripts Engineering 109

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.

APIs 75

APIs Scripts Metrics Benchmark 75

AWS Machine Learning

NOVEMBER 1, 2024

Dru on the backend decodes log data, deciphers error codes, and invokes API calls to troubleshoot. This approach allowed us to break the problem down into multiple steps: Identify the API route. Generate and invoke private API calls. Having similar names and synonyms in API routes make this retrieval problem more complex.

APIs 105

APIs Scripts Engineering Government 105

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.

Scripts 85

Scripts APIs Metrics Engineering 85

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.

Scripts 114

Scripts APIs Metrics Management 114

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.

Scripts 76

Scripts APIs Best practices Engineering 76

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")

APIs 102

APIs Scripts Engineering Accountability 102

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.

Scripts 82

Scripts Accountability Metrics Best practices 82

AWS Machine Learning

SEPTEMBER 30, 2024

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

APIs 83

APIs Real estate Accountability Metrics 83

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.

Scripts 89

Scripts Engineering Metrics APIs 89

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.

Scripts 77

Scripts Metrics Construction APIs 77

AWS Machine Learning

SEPTEMBER 18, 2023

Each stage in the ML workflow is broken into discrete steps, with its own script that takes input and output parameters. In the following code, the desired number of actors is passed in as an input argument to the script. Let’s look at sections of the scripts that perform this data preprocessing. get("OfflineStoreConfig").get("S3StorageConfig").get("ResolvedOutputS3Uri")

Scripts 117

Scripts APIs Government Construction 117

AWS Machine Learning

OCTOBER 2, 2024

In addition, they use the developer-provided instruction to create an orchestration plan and then carry out the plan by invoking company APIs and accessing knowledge bases using Retrieval Augmented Generation (RAG) to provide an answer to the user’s request. In Part 1, we focus on creating accurate and reliable agents.

Best practices 93

Best practices APIs Metrics Accountability 93

Totango

APRIL 26, 2021

Download Campaign Metrics for Targeted Users. System Browser Scripts settings – You can now enable/disable session recordings in Global Settings -> General. You can also enable a walkthrough script with a script UR L. . More details are ‍ here ‍ Collection Criteria Segmentation.

Scripts 62

Scripts APIs Customer retention Enterprise 62

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.

Scripts 80

Scripts APIs Metrics Construction 80

AWS Machine Learning

OCTOBER 18, 2024

Call the SageMaker control plane API using the SageMaker Python SDK for model training. Use Qualcomm AI Hub to compile and profile the model, running it on cloud-hosted devices to deliver performance metrics ahead of downloading for deployment across edge devices. We’ve prepared a script called train_yolov8.py environment.

Scripts 94

Scripts APIs Engineering Marketing 94

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.

Scripts 77

Scripts Engineering APIs Big data 77

AWS Machine Learning

MAY 8, 2023

To use TensorRT as a backend for Triton Inference Server, you need to create a TensorRT engine from your trained model using the TensorRT API. Inference requests arrive at the server via either HTTP/REST or by the C API , and are then routed to the appropriate per-model scheduler. script from the following cell.

Engineering 92

Engineering APIs Best practices Scripts 92