APIs, Benchmark and Construction - Customer Contact Central

GraphStorm 0.3: Scalable, multi-task learning on graphs with user-friendly APIs

AWS Machine Learning

AUGUST 2, 2024

adds new APIs to customize GraphStorm pipelines: you now only need 12 lines of code to implement a custom node classification training loop. Based on customer feedback for the experimental APIs we released in GraphStorm 0.2, introduces refactored graph ML pipeline APIs. Specifically, GraphStorm 0.3 In addition, GraphStorm 0.3

APIs

APIs Benchmark Construction Enterprise

Build a secure enterprise application with Generative AI and RAG using Amazon SageMaker JumpStart

AWS Machine Learning

SEPTEMBER 6, 2023

These SageMaker endpoints are consumed in the Amplify React application through Amazon API Gateway and AWS Lambda functions. To protect the application and APIs from inadvertent access, Amazon Cognito is integrated into Amplify React, API Gateway, and Lambda functions. You access the React application from your computer.

Enterprise

Enterprise APIs Real estate Construction

Build a multilingual automatic translation pipeline with Amazon Translate Active Custom Translation

AWS Machine Learning

JUNE 15, 2023

We demonstrate how to use the AWS Management Console and Amazon Translate public API to deliver automatic machine batch translation, and analyze the translations between two language pairs: English and Chinese, and English and Spanish. In this post, we present a solution that D2L.ai

APIs

APIs Benchmark Best practices Engineering

Build RAG applications using Jina Embeddings v2 on Amazon SageMaker JumpStart

AWS Machine Learning

JUNE 6, 2024

Jina Embeddings v2 is the preferred choice for experienced ML scientists for the following reasons: State-of-the-art performance – We have shown on various text embedding benchmarks that Jina Embeddings v2 models excel on tasks such as classification, reranking, summarization, and retrieval.

Benchmark

Benchmark Enterprise Construction APIs

eSentire delivers private and secure generative AI interactions to customers with Amazon SageMaker

AWS Machine Learning

JUNE 21, 2024

The application’s frontend is accessible through Amazon API Gateway , using both edge and private gateways. When a SageMaker endpoint is constructed, an S3 URI to the bucket containing the model artifact and Docker image is shared using Amazon ECR. The following diagram visualizes the architecture diagram and workflow.

Engineering

Engineering Construction Benchmark APIs

Build a RAG-based QnA application using Llama3 models from SageMaker JumpStart

AWS Machine Learning

SEPTEMBER 12, 2024

On Hugging Face, the Massive Text Embedding Benchmark (MTEB) is provided as a leaderboard for diverse text embedding tasks. It currently provides 129 benchmarking datasets across 8 different tasks on 113 languages. medium instance to demonstrate deploying the model as an API endpoint using an SDK through SageMaker JumpStart.

APIs

APIs Benchmark Enterprise Construction

Fast-track graph ML with GraphStorm: A new way to solve problems on enterprise-scale graphs

AWS Machine Learning

JUNE 9, 2023

The pre-trained GNN embeddings show a 24% improvement on a shopper activity prediction task over a state-of-the-art BERT- based baseline; it also exceeds benchmark performance in other ads applications.” Basically, by using the API of this layer, you can focus on the model development without worrying about how to scale the model training.

Enterprise

Enterprise Construction Engineering Metrics

14 Best Java Courses

JivoChat

JUNE 20, 2022

You will learn the best practices and coding conventions for writing Java code, and how to program using Java 8 constructs like Lambdas and Streams. Data and time API. Rest API Testing (Automation) from Scratch-Rest Assured Java. Main topics: Rest API basics and terminology. API testing using Postman. Input-output.

APIs

APIs Benchmark Best practices Construction

From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 2

AWS Machine Learning

NOVEMBER 15, 2024

An alternative approach to routing is to use the native tool use capability (also known as function calling) available within the Bedrock Converse API. In this scenario, each category or data source would be defined as a ‘tool’ within the API, enabling the model to select and use these tools as needed.

APIs

APIs Engineering Chatbots Construction

Maximize Stable Diffusion performance and lower inference costs with AWS Inferentia2

AWS Machine Learning

JULY 26, 2023

We compile the UNet for one batch (by using input tensors with one batch), then use the torch_neuronx.DataParallel API to load this single batch model onto each core. The directory path for the compiled model is constructed by joining COMPILER_WORKDIR_ROOT with the subdirectory text_encoder : emb = torch.tensor([.])

Scripts

Scripts APIs Benchmark Engineering

Evaluate large language models for quality and responsibility

AWS Machine Learning

NOVEMBER 30, 2023

Customers have to leave their development environment to use academic tools and benchmarking sites, which require highly-specialized knowledge. We surveyed existing open-source evaluation frameworks and designed FMEval evaluation API with extensibility in mind.

Construction

Construction Metrics industry standards Benchmark

How Patsnap used GPT-2 inference on Amazon SageMaker with low latency and cost

AWS Machine Learning

JULY 24, 2023

A recent initiative is to simplify the difficulty of constructing search expressions by autofilling patent search queries using state-of-the-art text generation models. In this section, we show how to build your own container, deploy your own GPT-2 model, and test with the SageMaker endpoint API. model_fp16.onnx gpt2 and predictor.py

APIs

APIs Engineering Construction Benchmark

How CCC Intelligent Solutions created a custom approach for hosting complex AI models using Amazon SageMaker

AWS Machine Learning

JANUARY 20, 2023

After cycles of research and initial benchmarking efforts, CCC determined SageMaker was a perfect fit to meet a majority of their production requirements, especially the guaranteed uptime SageMaker provides for most of its inference components. Step-by-step solution Step 1 A client makes a request to the AWS API Gateway endpoint.

APIs

APIs Engineering Telecommunications Construction

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. If an organization has no AI/ML experts in their team, then an API service might be better suited for them. 15K available FM reference Step 1.

Engineering

Engineering Accountability Construction APIs

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

AWS Machine Learning

JANUARY 19, 2024

We use the Recognizing Textual Entailment dataset from the GLUE benchmarking suite. Phrase 2: A bearded man pulls a rope We load the textual recognizing entailment dataset from the GLUE benchmarking suite via the dataset library from Hugging Face within our training script (./training.py training.py ).

Metrics

Metrics Scripts Benchmark Enterprise

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

AWS Machine Learning

OCTOBER 27, 2022

Our benchmarks show up to 46% price performance benefit after enabling heterogeneous clusters in a CPU-bound TensorFlow computer vision model training. Performance benchmark results. For more information, refer to Using the SageMaker Python SDK and Using the Low-Level SageMaker APIs. Heterogeneous clusters at Mobileye.

Scripts

Scripts Benchmark Metrics Transportation

Build a robust text-based toxicity predictor

AWS Machine Learning

DECEMBER 6, 2022

The Trainer class provides an API for feature-complete training in PyTorch. To answer this question, we select an attack recipe from the TextAttack library and use it to construct perturbed adversarial examples to fool our target toxicity filtering model. outputs = 1 * (pred.predictions >= 0.5) Model performance evaluation.

Engineering

Engineering APIs Construction Benchmark

Face-off Probability, part of NHL Edge IQ: Predicting face-off winners in real time during televised games

AWS Machine Learning

OCTOBER 5, 2022

We also share the key technical challenges that were solved during construction of the Face-off Probability model. To make an informed decision, we performed a series of benchmarks to verify SageMaker latency and scalability, and validated that average latency was less than 100 milliseconds under the load, which was within our expectations.

Calibration

Calibration Engineering Automotive Analytics

Information extraction with LLMs using Amazon SageMaker JumpStart

AWS Machine Learning

MAY 7, 2024

To deploy a model from SageMaker JumpStart, you can use either APIs, as demonstrated in this post, or use the SageMaker Studio UI. The following section details the benchmark’s performance overall, and against each intent. In this example, we use Llama-2-70b-chat, but you might use a different model depending on your use case.

Engineering

Engineering Technical Support Chatbots Best practices

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

AWS Machine Learning

NOVEMBER 29, 2023

Each trained model needs to be benchmarked against many tasks not only to assess its performances but also to compare it with other existing models, to identify areas that needs improvements and finally, to keep track of advancements in the field. These benchmarks have leaderboards that can be used to compare and contrast evaluated models.

Benchmark

Benchmark Metrics Engineering APIs

Speed up your time series forecasting by up to 50 percent with Amazon SageMaker Canvas UI and AutoML APIs

AWS Machine Learning

SEPTEMBER 28, 2023

In this post, we describe the enhancements to the forecasting capabilities of SageMaker Canvas and guide you on using its user interface (UI) and AutoML APIs for time-series forecasting. While the SageMaker Canvas UI offers a code-free visual interface, the APIs empower developers to interact with these features programmatically.

APIs

APIs Construction Finance Enterprise

Cepsa Química improves the efficiency and accuracy of product stewardship using Amazon Bedrock

AWS Machine Learning

AUGUST 2, 2024

We partnered with Keepler , a cloud-centered data services consulting company specialized in the design, construction, deployment, and operation of advanced public cloud analytics custom-made solutions for large organizations, in the creation of the first generative AI solution for one of our corporate teams.

APIs

APIs Analytics Chatbots Engineering

Model Hosting Patterns in SageMaker: Best practices in testing and updating models on SageMaker

AWS Machine Learning

NOVEMBER 9, 2022

Your application simply needs to include an API call with the target model to this endpoint to achieve low-latency, high-throughput inference. To deploy, use the endpoint_from_production_variant construct to create the endpoint. Deploying an MVE is also very straightforward. This implies each variant receives 50% of the total traffic.

Best practices

Best practices Construction Metrics Enterprise

Customer Contact Central

GraphStorm 0.3: Scalable, multi-task learning on graphs with user-friendly APIs

Build a secure enterprise application with Generative AI and RAG using Amazon SageMaker JumpStart

Trending Sources

Build a multilingual automatic translation pipeline with Amazon Translate Active Custom Translation

Build RAG applications using Jina Embeddings v2 on Amazon SageMaker JumpStart

eSentire delivers private and secure generative AI interactions to customers with Amazon SageMaker

Build a RAG-based QnA application using Llama3 models from SageMaker JumpStart

Fast-track graph ML with GraphStorm: A new way to solve problems on enterprise-scale graphs

14 Best Java Courses

From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 2

Maximize Stable Diffusion performance and lower inference costs with AWS Inferentia2

Evaluate large language models for quality and responsibility

How Patsnap used GPT-2 inference on Amazon SageMaker with low latency and cost

How CCC Intelligent Solutions created a custom approach for hosting complex AI models using Amazon SageMaker

FMOps/LLMOps: Operationalize generative AI and differences with MLOps

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

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

Build a robust text-based toxicity predictor

Face-off Probability, part of NHL Edge IQ: Predicting face-off winners in real time during televised games

Information extraction with LLMs using Amazon SageMaker JumpStart

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

Speed up your time series forecasting by up to 50 percent with Amazon SageMaker Canvas UI and AutoML APIs

Cepsa Química improves the efficiency and accuracy of product stewardship using Amazon Bedrock

Model Hosting Patterns in SageMaker: Best practices in testing and updating models on SageMaker

Stay Connected

GraphStorm 0.3: Scalable, multi-task learning on graphs with user-friendly APIs

Build a secure enterprise application with Generative AI and RAG using Amazon SageMaker JumpStart

Trending Sources

Build a multilingual automatic translation pipeline with Amazon Translate Active Custom Translation

Build RAG applications using Jina Embeddings v2 on Amazon SageMaker JumpStart

eSentire delivers private and secure generative AI interactions to customers with Amazon SageMaker

Build a RAG-based QnA application using Llama3 models from SageMaker JumpStart

Fast-track graph ML with GraphStorm: A new way to solve problems on enterprise-scale graphs

14 Best Java Courses

From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 2

Maximize Stable Diffusion performance and lower inference costs with AWS Inferentia2

Evaluate large language models for quality and responsibility

How Patsnap used GPT-2 inference on Amazon SageMaker with low latency and cost

­­How CCC Intelligent Solutions created a custom approach for hosting complex AI models using Amazon SageMaker

FMOps/LLMOps: Operationalize generative AI and differences with MLOps

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

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

Build a robust text-based toxicity predictor

Face-off Probability, part of NHL Edge IQ: Predicting face-off winners in real time during televised games

Information extraction with LLMs using Amazon SageMaker JumpStart

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

Speed up your time series forecasting by up to 50 percent with Amazon SageMaker Canvas UI and AutoML APIs

Cepsa Química improves the efficiency and accuracy of product stewardship using Amazon Bedrock

Model Hosting Patterns in SageMaker: Best practices in testing and updating models on SageMaker

Stay Connected

How CCC Intelligent Solutions created a custom approach for hosting complex AI models using Amazon SageMaker