TechSee

APRIL 22, 2024

Furthermore, TechSee’s technology can be integrated anywhere through APIs or SDKs. To ensure that new technologies are embraced and utilized to their fullest, they must be integrated smoothly into the existing agent dashboards and scripts, requiring minimal shifts from established routines.

APIs 109

APIs Scripts Technology Best practices 109

AWS Machine Learning

JANUARY 5, 2024

The AWS portfolio of ML services includes a robust set of services that you can use to accelerate the development, training, and deployment of machine learning applications. Collaboration – Data scientists each worked on their own local Jupyter notebooks to create and train ML models.

Management 111

Management APIs Engineering Government 111

AWS Machine Learning

JULY 11, 2023

With Amazon SageMaker , you can use the @remote decorator to run a SageMaker training job simply by annotating your Python code with an @remote decorator. Train a classification model based on the MNIST dataset using an @remote decorator from the open-source SageMaker Python SDK. and model.py gamma: float = 0.7,

Scripts 57

Scripts APIs Healthcare Banking 57

NobelBiz

MAY 11, 2021

Writing a call script is a must for contact centers that want to excel in their prospecting effort. If you write it according to the rules of the game, the script is an observable, cost-effective, and efficient method of attracting and maintaining prospects and clients. What exactly is call scripting? Why do scripts exist?

Scripts 52

Scripts Contact Center Calibration CRM 52

AWS Machine Learning

JULY 29, 2022

The data scientists in this team use Amazon SageMaker to build and train a credit risk prediction model using the shared credit risk data product from the consumer banking LoB. Build and train ML models using a data mesh architecture on AWS. Part 1: Data mesh set up and data product registration. Data exploration.

Scripts 71

Scripts Analytics Accountability Engineering 71

AWS Machine Learning

AUGUST 4, 2023

In Part 1 , we show how the Salesforce Data Cloud and Einstein Studio integration with SageMaker allows businesses to access their Salesforce data securely using SageMaker and use its tools to build, train, and deploy models to endpoints hosted on SageMaker. frameworks to restrict client access to your APIs.

APIs 78

APIs Scripts Management Accountability 78

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. Clone the Github repository The GitHub repo provides all the scripts necessary to deploy models using FastAPI on NeuronCores on AWS Inferentia instances. code as the entry point. compiled-model-bs-{batch_size}.pt')

Scripts 73

Scripts APIs Engineering Best practices 73

AWS Machine Learning

FEBRUARY 29, 2024

The Retrieve and RetrieveAndGenerate APIs allow your applications to directly query the index using a unified and standard syntax without having to learn separate APIs for each different vector database, reducing the need to write custom index queries against your vector store.

APIs 114

APIs Healthcare Scripts Enterprise 114

AWS Machine Learning

MAY 31, 2023

The Amazon SageMaker Python SDK is an open-source library for training and deploying machine learning (ML) models on Amazon SageMaker. In such cases, data scientists have to provide these parameters to their ML model training and deployment code manually, by noting down subnets, security groups, and KMS keys.

APIs 79

APIs Scripts Accountability Enterprise 79

AWS Machine Learning

MARCH 23, 2023

Although this example shows how to perform this for inference operations, you can extend the solution to training and other ML steps. Endpoints are deployed with a couple clicks or lines of code using SageMaker, which simplifies the process for developers and ML experts to build and train ML and deep learning models in the cloud.

Scripts 96

Scripts APIs Government Analytics 96

AWS Machine Learning

OCTOBER 2, 2023

It is architected to automate the entire machine learning (ML) process, from data labeling to model training and deployment at the edge. In this post, we delve deep into how the labeling and model building and training parts of the pipeline are implemented. There are many ways to ensure good label quality.

Scripts 93

Scripts Construction Engineering APIs 93

AWS Machine Learning

MAY 5, 2023

It consists of pre-trained models that can be applied to analyze images and videos at scale. 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.

Engineering 101

Engineering APIs Scripts Enterprise 101

AWS Machine Learning

SEPTEMBER 28, 2022

Data scientists often train their models locally and look for a proper hosting service to deploy their models. Unfortunately, there’s no one set mechanism or guide to deploying pre-trained models to the cloud. In this post, we look at deploying trained models to Amazon SageMaker hosting to reduce your deployment time.

Scripts 75

Scripts APIs Engineering Consulting 75

AWS Machine Learning

OCTOBER 25, 2022

It contains a number of training- and inference-specific optimizations that take advantage of different memory/time trade-offs for different protein lengths based on model training or inference runs. For training, OpenFold supports FlashAttention optimizations that accelerate the multi-sequence alignment (MSA) attention component.

APIs 77

APIs Scripts Banking Engineering 77

AWS Machine Learning

DECEMBER 21, 2022

As feature data grows in size and complexity, data scientists need to be able to efficiently query these feature stores to extract datasets for experimentation, model training, and batch scoring. The offline store is primarily used for batch predictions and model training. You can also use the FeatureGroup().put_record

Scripts 73

Scripts Engineering APIs Big data 73

AWS Machine Learning

AUGUST 30, 2023

It often takes years of training for doctors to accumulate enough expertise in writing concise and informative radiology report summarizations, further highlighting the significance of automating the process. There are significant benefits to using a pre-trained model. For validation, 5% of the dataset was randomly selected.

Healthcare 112

Healthcare APIs Metrics Scripts 112

AWS Machine Learning

APRIL 19, 2023

Our data scientists train the model in Python using tools like PyTorch and save the model as PyTorch scripts. Ideally, we instead want to load the model PyTorch scripts, extract the features from model input, and run model inference entirely in Java. The following diagram outlines the workflow of the DJL solution.

Engineering 75

Engineering APIs Scripts Education 75

COPC

OCTOBER 26, 2023

Understanding AI Terminology Generative AI: These are algorithms trained to predict data sequences based on training information. LLMs are unique because they have been trained on enormous data sets. This training gives them capabilities beyond any AI we have seen before.

Technology 77

Technology Customer Experience APIs Consulting 77

AWS Machine Learning

MAY 10, 2023

Today, data scientists who are training deep learning models need to identify and remediate model training issues to meet accuracy targets for production deployment, and require a way to utilize standard tools for debugging model training. is your training script, and simple_tensorboard.ipynb launches the SageMaker training job.

Scripts 73

Scripts Metrics Construction APIs 73

Zingtree

APRIL 14, 2015

We’re proud to announce that we’ve “officially” launched our Agent Scripting for call centers. Zingtree Interactive Decision Tree System Redefines Call Center Agent Scripting with New App. New agent scripting tools aid in training and corporate compliance for call center applications.

Scripts 48

Scripts Call Center APIs Analytics 48

AWS Machine Learning

NOVEMBER 26, 2023

Gartner predicts that “by 2026, more than 80% of enterprises will have used generative AI APIs or models, or deployed generative AI-enabled applications in production environments, up from less than 5% in 2023.” First, import your datasets of users’ interactions and items into Amazon Personalize for training.

Personalization 103

Personalization Customer Experience APIs Entertainment 103

AWS Machine Learning

NOVEMBER 17, 2022

Beyond model accuracy, other potential metrics of importance are model training time and inference time. Given the iterative nature of ML model development, faster training times allow data scientists to quickly test various hypotheses. You can incrementally train and tune the ML models offered in JumpStart before deployment.

APIs 79

APIs Scripts Metrics 79

AWS Machine Learning

MAY 16, 2023

As a JumpStart model hub customer, you get improved performance without having to maintain the model script outside of the SageMaker SDK. Because these models are expensive to train, customers want to use existing pre-trained foundation models and fine-tune them as needed, rather than train these models themselves.

Chatbots 85

Chatbots Scripts APIs Feedback 85

AWS Machine Learning

NOVEMBER 1, 2022

Amazon SageMaker Autopilot automatically builds, trains, and tunes the best custom machine learning (ML) models based on your data. Amazon SageMaker Pipelines helps you automate the different steps of the ML lifecycle, including data preprocessing, training, tuning and evaluating ML models, and deploying them. SageMaker pipeline steps.

Scripts 77

Scripts APIs Metrics Engineering 77

AWS Machine Learning

MAY 23, 2023

Like all AI, generative AI is powered by ML models—very large models that are pre-trained on vast corpora of data and commonly referred to as foundation models (FMs). With tradition ML models, in order to achieve each specific task, you need to gather labeled data, train a model, and deploy that model.

APIs 92

APIs Accountability Construction Management 92

AWS Machine Learning

MAY 19, 2023

The DescribeForMe web app invokes the backend AI services by sending the Amazon S3 object Key in the payload to Amazon API Gateway Amazon API Gateway instantiates an AWS Step Functions workflow. A pre-signed URL with the location of the audio file stored in Amazon S3 is sent back to the user’s browser through Amazon API Gateway.

APIs 89

APIs Scripts Engineering Best practices 89

AWS Machine Learning

NOVEMBER 21, 2023

In this post, we bring MATLAB’s machine learning capabilities into Amazon SageMaker , which has several significant benefits: Compute resources : Using the high-performance computing environment offered by SageMaker can speed up machine learning training. We start by training a classifier model on our desktop with MATLAB.

Engineering 87

Engineering APIs Scripts Automotive 87

AWS Machine Learning

AUGUST 10, 2022

If the images are already labeled, you can begin training a model in just a few clicks. Rekognition Custom Labels uses transfer learning to automatically inspect the training data, select the right model framework and algorithm, optimize the hyperparameters, and train the model. Train the model. Solution overview.

Scripts 77

Scripts APIs Enterprise Analytics 77

AWS Machine Learning

MARCH 1, 2023

The ability to train custom models through the Custom classification and Custom entity recognition features of Comprehend has enabled customers to explore out-of-the-box NLP capabilities tied to their requirements without having to take the approach of building classification and entity recognition models from scratch.

APIs 68

APIs Metrics Accountability Scripts 68

AWS Machine Learning

MAY 25, 2023

Furthermore, FMs are trained with a point in time snapshot of data and have no inherent ability to access fresh data at inference time; without this ability they might provide responses that are potentially incorrect or inadequate. The Streamlit application invokes the API Gateway endpoint REST API.

APIs 77

APIs Enterprise Accountability Chatbots 77

AWS Machine Learning

AUGUST 21, 2023

With new additions to SageMaker Clarify, and the use of pre-trained models from Hugging Face that are easily used implemented in SageMaker, model training and explainability can all be easily done in AWS. You also use a custom inference script to do the predictions within the container.

Healthcare 77

Healthcare APIs Scripts Finance 77

AWS Machine Learning

APRIL 29, 2024

This often means the method of using a third-party LLM API won’t do for security, control, and scale reasons. First, the AWS Trainium accelerator provides a high-performance, cost-effective, and readily available solution for training and fine-tuning large models. The following figure illustrates this workflow.

APIs 97

APIs Engineering Scripts Management 97

AWS Machine Learning

MARCH 21, 2023

Instead of hardcoding the custom function into your custom transform step, you pull a script containing the function from CodeCommit, load it, and call the loaded function in your custom transform step. We use the get_file API function to pull files from the CodeCommit repository into the Data Wrangler environment. Choose Preview.

Scripts 62

Scripts Banking APIs Best practices 62

AWS Machine Learning

SEPTEMBER 28, 2022

Amazon SageMaker Pipelines allows data scientists and machine learning (ML) engineers to automate training workflows, which helps you create a repeatable process to orchestrate model development steps for rapid experimentation and model retraining. 2.76.0 – Availability of FailStep to conditionally stop a pipeline with a failure status.

Scripts 70

Scripts Engineering APIs Management 70

AWS Machine Learning

MAY 17, 2023

Generative AI has grown at an accelerating rate from the largest pre-trained model in 2019 having 330 million parameters to more than 500 billion parameters today. With JumpStart, you can access pre-trained models and foundation models from the Foundations Model Hub to perform tasks like article summarization and image generation.

APIs 70

APIs Scripts Engineering Management 70

AWS Machine Learning

NOVEMBER 21, 2022

For each of these NLP tasks, we demonstrate how to use Amazon SageMaker to perform the following actions: Deploy and run inference on a pre-trained model. Fine-tune the pre-trained model on a new custom dataset. Training uses five ml.g4dn.2xlarge This notebook demonstrates how to use the JumpStart API for text classification.

Scripts 72

Scripts Metrics APIs Healthcare 72

AWS Machine Learning

OCTOBER 4, 2022

Developers usually test their processing and training scripts locally, but the pipelines themselves are typically tested in the cloud. Model training and tuning. Writing the scripts to transform the data is typically an iterative process, where fast feedback loops are important to speed up development. Model evaluation.

Scripts 72

Scripts Engineering Management APIs 72