APIs, Construction, Metrics and Scripts - Customer Contact Central

Build an end-to-end MLOps pipeline for visual quality inspection at the edge – Part 2

AWS Machine Learning

OCTOBER 2, 2023

With this format, we can easily query the feature store and work with familiar tools like Pandas to construct a dataset to be used for training later. 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.

Scripts

Scripts Construction Engineering APIs

Amazon SageMaker with TensorBoard: An overview of a hosted TensorBoard experience

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

Scripts Metrics Construction APIs

Automatically generate impressions from findings in radiology reports using generative AI on AWS

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

Healthcare APIs Scripts Metrics

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Build a GNN-based real-time fraud detection solution using Amazon SageMaker, Amazon Neptune, and the Deep Graph Library

AWS Machine Learning

AUGUST 11, 2022

Additionally, it’s challenging to construct a streaming data pipeline that can feed incoming events to a GNN real-time serving API. It starts from a RESTful API that queries the graph database in Neptune to extract the subgraph related to an incoming transaction. FD_SL_Process_IEEE-CIS_Dataset.ipynb. next(dataProcessTask).next(hyperParaTask).next(trainingJobTask).next(runLoadGraphDataTask).next(modelRepackagingTask).next(createModelTask).next(createEndpointConfigTask).next(c

Construction

Construction APIs Accountability Scripts

Identify key insights from text documents through fine-tuning and HPO with Amazon SageMaker JumpStart

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.

Scripts

Scripts Metrics APIs Healthcare

The ChatGPT Revolution

The Northridge Group

MAY 2, 2023

ChatGPT’s state-of-the-art tools make it possible for users to easily construct sophisticated Conversational AI applications quickly and efficiently. It can also be easily integrated via APIs with popular apps such as Slack, Instacart, Snapchat, or Facebook Messenger for easy access across multiple platforms.

Contact Center

Contact Center B2C Chatbots B2B

FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning

SEPTEMBER 1, 2023

In this scenario, the generative AI application, designed by the consumer, must interact with the fine-tuner backend via APIs to deliver this functionality to the end-users. Some models may be trained on diverse text datasets like internet data, coding scripts, instructions, or human feedback. 15K available FM reference Step 1.

Engineering

Engineering Accountability Construction APIs

Create a document lake using large-scale text extraction from documents with Amazon Textract

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. The second approach is a turnkey deployment of various infrastructure components using AWS Cloud Development Kit (AWS CDK) constructs. We have packaged this solution in a.ipynb script and.py

Scripts

Scripts Construction APIs Healthcare

Build a robust text-based toxicity predictor

AWS Machine Learning

DECEMBER 6, 2022

compute metrics function def compute_metrics(pred): targets = 1 * (pred.label_ids >= 0.5) compute metrics function def compute_metrics(pred): targets = 1 * (pred.label_ids >= 0.5) The Trainer class provides an API for feature-complete training in PyTorch. outputs = 1 * (pred.predictions >= 0.5)

Engineering

Engineering APIs Construction Metrics

Build an air quality anomaly detector using Amazon Lookout for Metrics

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

Metrics APIs Scripts Wireless

Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning

DECEMBER 6, 2023

Another driver behind RAG’s popularity is its ease of implementation and the existence of mature vector search solutions, such as those offered by Amazon Kendra (see Amazon Kendra launches Retrieval API ) and Amazon OpenSearch Service (see k-Nearest Neighbor (k-NN) search in Amazon OpenSearch Service ), among others.

Analytics

Analytics APIs Engineering Construction

Scale AI training and inference for drug discovery through Amazon EKS and Karpenter

AWS Machine Learning

APRIL 19, 2024

The Kubernetes semantics used by the provisioners support directed scheduling using Kubernetes constructs such as taints or tolerations and affinity or anti-affinity specifications; they also facilitate control over the number and types of GPU instances that may be scheduled by Karpenter. A managed node group with two c5.xlarge

Metrics

Metrics APIs Scripts Construction

Securing MLflow in AWS: Fine-grained access control with AWS native services

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.

APIs

APIs Government Accountability Scripts

Orchestrate Ray-based machine learning workflows using Amazon SageMaker

AWS Machine Learning

SEPTEMBER 18, 2023

The combination of Ray and SageMaker provides end-to-end capabilities for scalable ML workflows, and has the following highlighted features: Distributed actors and parallelism constructs in Ray simplify developing distributed applications. In the following code, the desired number of actors is passed in as an input argument to the script.

Scripts

Scripts APIs Government Construction

Build a centralized monitoring and reporting solution for Amazon SageMaker using Amazon CloudWatch

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

Scripts Accountability Metrics APIs

Reduce inference time for BERT models using neural architecture search and SageMaker Automated Model Tuning

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

Metrics Scripts Benchmark Enterprise

Optimize hyperparameters with Amazon SageMaker Automatic Model Tuning

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

Scripts APIs Metrics Construction

Improve price performance of your model training using Amazon SageMaker heterogeneous clusters

AWS Machine Learning

OCTOBER 27, 2022

The quick way to identify a CPU bottleneck is to monitor CPU and GPU utilization metrics for SageMaker training jobs in Amazon CloudWatch. You can access these views from the AWS Management Console within the training job page’s instance metrics hyperlink. Pick the relevant metrics and switch from 5-minute to 1-minute resolution.

Scripts

Scripts Benchmark Metrics Transportation

Financial text generation using a domain-adapted fine-tuned large language model in Amazon SageMaker JumpStart

AWS Machine Learning

APRIL 18, 2023

Although these SEC filings are publicly available to anyone, downloading parsed filings and constructing a clean dataset with added features is a time-consuming exercise. We make this possible in a few API calls in the JumpStart Industry SDK. 0-9]+)"}, {'Name': 'eval:loss', 'Regex': "'eval_loss': ([0-9]+.[0-9]+)"},

Finance

Finance Scripts APIs Sales

Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data

AWS Machine Learning

APRIL 18, 2023

Although these SEC filings are publicly available to anyone, downloading parsed filings and constructing a clean dataset with added features is a time-consuming exercise. We make this possible in a few API calls in the JumpStart Industry SDK. 0-9]+)"}, {'Name': 'eval:loss', 'Regex': "'eval_loss': ([0-9]+.[0-9]+)"},

Finance

Finance Scripts APIs Sales

Operationalize LLM Evaluation at Scale using Amazon SageMaker Clarify and MLOps services

AWS Machine Learning

NOVEMBER 29, 2023

Furthermore, these data and metrics must be collected to comply with upcoming regulations. They need evaluation metrics generated by model providers to select the right pre-trained model as a starting point. Lastly, evaluation logics define the criteria and metrics used to assess the model’s performance.

Benchmark

Benchmark Metrics Engineering APIs

Hosting ML Models on Amazon SageMaker using Triton: XGBoost, LightGBM, and Treelite Models

AWS Machine Learning

MAY 2, 2023

The FIL backend utilizes many of these techniques by using cuML constructs and is built on C++ and the CUDA core library to optimize inference performance on GPU accelerators. The bash script create_prep_env.sh Triton supports various backends as engines to support the running and serving of various ML models for inference.

Scripts

Scripts Engineering Enterprise Construction

Best practices and design patterns for building machine learning workflows with Amazon SageMaker Pipelines

AWS Machine Learning

SEPTEMBER 7, 2023

Query the property file eta = JsonGet( step_name=step_process.name, property_file=hyperparam_report, json_path="hyperparam.eta.value", ) Parameterize a variable in pipeline definition Parameterizing variables so that they can be used at runtime is often desirable—for example, to construct an S3 URI. 1", instance_type="ml.m5.xlarge",

Best practices

Best practices Scripts Construction APIs

Dialogue-guided visual language processing with Amazon SageMaker JumpStart

AWS Machine Learning

NOVEMBER 1, 2023

However, like other nascent technologies, obstacles remain in managing model intricacy, harmonizing diverse modalities, and formulating uniform evaluation metrics. The TGI framework underpins the model inference layer, providing RESTful APIs for robust integration and effortless accessibility.

APIs

APIs Engineering Construction Scripts

Customer Contact Central

Build an end-to-end MLOps pipeline for visual quality inspection at the edge – Part 2

Amazon SageMaker with TensorBoard: An overview of a hosted TensorBoard experience

Webinars

Trending Sources

Automatically generate impressions from findings in radiology reports using generative AI on AWS

Webinars

Build a GNN-based real-time fraud detection solution using Amazon SageMaker, Amazon Neptune, and the Deep Graph Library

Identify key insights from text documents through fine-tuning and HPO with Amazon SageMaker JumpStart

The ChatGPT Revolution

FMOps/LLMOps: Operationalize generative AI and differences with MLOps

Create a document lake using large-scale text extraction from documents with Amazon Textract

Build a robust text-based toxicity predictor

Build an air quality anomaly detector using Amazon Lookout for Metrics

Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

Scale AI training and inference for drug discovery through Amazon EKS and Karpenter

Securing MLflow in AWS: Fine-grained access control with AWS native services

Orchestrate Ray-based machine learning workflows using Amazon SageMaker

Build a centralized monitoring and reporting solution for Amazon SageMaker using Amazon CloudWatch

Reduce inference time for BERT models using neural architecture search and SageMaker Automated Model Tuning

Optimize hyperparameters with Amazon SageMaker Automatic Model Tuning

Improve price performance of your model training using Amazon SageMaker heterogeneous clusters

Financial text generation using a domain-adapted fine-tuned large language model in Amazon SageMaker JumpStart

Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data

Operationalize LLM Evaluation at Scale using Amazon SageMaker Clarify and MLOps services

Hosting ML Models on Amazon SageMaker using Triton: XGBoost, LightGBM, and Treelite Models

Best practices and design patterns for building machine learning workflows with Amazon SageMaker Pipelines

Dialogue-guided visual language processing with Amazon SageMaker JumpStart

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