AWS Machine Learning

FEBRUARY 15, 2023

Amazon SageMaker JumpStart is the machine learning (ML) hub of SageMaker that offers over 350 built-in algorithms, pre-trained models, and pre-built solution templates to help you get started with ML fast. Perform the training step to fine-tune the pre-trained model using transfer learning. Create the model. Register the model.

Scripts 87

Scripts APIs Engineering Enterprise 87

AWS Machine Learning

JUNE 9, 2022

SageMaker JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that solve common business problems. In this post, we’re excited to announce that all trainable JumpStart models now support incremental training.

Scripts 66

Scripts APIs Enterprise Accountability 66

AWS Machine Learning

DECEMBER 22, 2023

Large language model (LLM) training has surged in popularity over the last year with the release of several popular models such as Llama 2, Falcon, and Mistral. Training performant models at this scale can be a challenge. These features improve the usability of the library, expand functionality, and accelerate training.

Scripts 101

Scripts APIs Engineering Finance 101

AWS Machine Learning

JANUARY 10, 2024

When a model is trained and ready to be used, it needs to be approved after being registered in the Amazon SageMaker Model Registry. Overview of solution This post focuses on a workflow solution that the ML model development lifecycle can use between the training pipeline and inferencing pipeline.

APIs 102

APIs Construction Engineering Analytics 102

AWS Machine Learning

APRIL 5, 2024

Amazon Rekognition includes a simple, easy-to-use API that can quickly analyze any image or video file that’s stored in Amazon Simple Storage Service (Amazon S3). The following table shows the moderation labels, content type, and confidence scores returned in the API response. Graphic Violence L2 92.6% Explosions and Blasts L3 92.6%

APIs 108

APIs Advertising Enterprise Technology 108

AWS Machine Learning

AUGUST 3, 2023

Training ML algorithms for pose estimation requires a lot of expertise and custom training data. Therefore, we present two options: one that doesn’t require any ML expertise and uses Amazon Rekognition, and another that uses Amazon SageMaker to train and deploy a custom ML model. Both requirements are hard and costly to obtain.

APIs 63

APIs Scripts Engineering Technical Support 63

CSM Magazine

OCTOBER 28, 2020

API ( Application Programming Interface ) is considered an integral part of website and mobile app development. The API enables a mobile application to exchange data easily with other apps or platforms. The reasons for outsourcing API development. Outsourcing companies provide customized API development services.

APIs 40

APIs outsourcing Marketing Surveys 40

Nexmo

DECEMBER 12, 2018

Crucial to any agent’s success is the training he receives, not just on day one but throughout his tenure. Training shouldn’t stop with the close of the onboarding process and brands that focus on continual improvement for their staffs realize improved NPS scores, brand loyalty, […].

APIs 63

APIs Contact Center 63

AWS Machine Learning

NOVEMBER 6, 2023

Within hours, you can annotate your sample documents using the AWS Management Console and train an adapter. Adapters are components that plug in to the Amazon Textract pre-trained deep learning model, customizing its output based on your annotated documents. Adapters can be created via the console or programmatically via the API.

APIs 104

APIs Best practices Banking Accountability 104

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

AWS Machine Learning

OCTOBER 18, 2023

The solution focuses on the fundamental principles of developing an AI/ML application workflow of data preparation, model training, model evaluation, and model monitoring. It is already trained on tens of millions of images across many categories. API Gateway calls the Lambda function to obtain the pet attributes.

APIs 98

APIs Metrics Consulting Consulting 98

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 114

Management APIs Engineering Government 114

AWS Machine Learning

FEBRUARY 7, 2024

Select your options and train the model. Deploy the API to make predictions. Collect the historical fraud data to be used to train the fraud detector model, with the following requirements: Data must be in CSV format and have headers. Create the model. Review model performance. Deploy the model. Create a detector. Choose Upload.

APIs 111

APIs Accountability Metrics Engineering 111

AWS Machine Learning

FEBRUARY 13, 2023

With the increasing use of artificial intelligence (AI) and machine learning (ML) for a vast majority of industries (ranging from healthcare to insurance, from manufacturing to marketing), the primary focus shifts to efficiency when building and training models at scale.

Scripts 74

Scripts Best practices Transportation Construction 74

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

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 102

Engineering APIs Scripts Enterprise 102

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

NOVEMBER 7, 2023

However, their training on massive datasets also limits their usefulness for specialized tasks. Without continued learning, these models remain oblivious to new data and trends that emerge after their initial training. Furthermore, the cost to train new LLMs can prove prohibitive for many enterprise settings.

Enterprise 97

Enterprise APIs Engineering Accountability 97

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 117

APIs Healthcare Scripts Enterprise 117

AWS Machine Learning

NOVEMBER 17, 2023

In this post, we explain the common practice of live stream visual moderation with a solution that uses the Amazon Rekognition Image API to moderate live streams. There are charges for Amazon S3 storage, Amazon S3 API calls that Amazon IVS makes on behalf of the customer, and serving the stored video to viewers.

APIs 91

APIs Engineering Advertising Accountability 91

TechSee

JUNE 29, 2023

Training an LLM to Know and Write Standing up an LLM-powered chatbot requires training, tuning, testing, and validating the LLM’s knowledge and guidance. Like agents, the LLM must have visibility into system outage statuses and training on the latest agent guidance or best practices.

Chatbots 202

Chatbots Customer Service Wait times APIs 202

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 93

APIs Accountability Construction Management 93

AWS Machine Learning

MARCH 10, 2023

You can also use Amazon SageMaker to train a proprietary fraud detection model. For more information, refer to Train fraudulent payment detection with Amazon SageMaker. Call the Amazon Fraud Detector API using the GetEventPrediction action. The API returns one of the following results: approve, block, or investigate.

APIs 115

APIs Banking Accountability Analytics 115

AWS Machine Learning

APRIL 4, 2024

Amazon Personalize offers a variety of recommendation recipes (algorithms), such as the User Personalization and Trending Now recipes, which are particularly suitable for training news recommender models. Amazon Personalize has a fixed number of interactions and items dataset features that can be used to train a model.

Personalization 83

Personalization APIs Engineering Metrics 83

AWS Machine Learning

JULY 29, 2022

In this two-part series, we provide guidance on how organizations can build a modern data architecture using a data mesh design pattern on AWS and enable an analytics and ML CoE to build and train ML models with data across multiple LoBs. Data mesh overview. don’t cover the final steps (6 and 7 in the preceding diagram) in this series.

Government 78

Government Banking Analytics Accountability 78

AWS Machine Learning

DECEMBER 12, 2022

Amazon Comprehend provides customized features, custom entity recognition , custom classification , and pre-trained APIs such as key phrase extraction, sentiment analysis, entity recognition, and more so you can easily integrate NLP into your applications. You can train and tune these models. SageMaker JumpStart.

APIs 75

APIs Accountability Enterprise Metrics 75

AWS Machine Learning

OCTOBER 27, 2023

First, import your dataset of users’ interactions into Amazon Personalize for training. Amazon Personalize automatically trains a model using the Top Picks for You recipe. To compose one-to-many emails, first import your dataset of users’ interactions into Amazon Personalize for training. Dear , Desiring the cozy feel of fall?

Personalization 90

Personalization Marketing APIs Entertainment 90

AWS Machine Learning

SEPTEMBER 6, 2023

It’s powered by large language models (LLMs) that are pre-trained on vast amounts of data and commonly referred to as foundation models (FMs). These SageMaker endpoints are consumed in the Amplify React application through Amazon API Gateway and AWS Lambda functions.

Enterprise 99

Enterprise APIs Real estate Construction 99

AWS Machine Learning

APRIL 24, 2023

Amazon Comprehend is a fully managed and continuously trained natural language processing (NLP) service that can extract insight about the content of a document or text. accuracy by training on 800 data points and testing on 300 data points. API Gateway bypasses the request to Lambda.

APIs 62

APIs Customer Experience Feedback Chatbots 62

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

JULY 25, 2023

It is all backed by very large models that are pre-trained on vast amounts of data and commonly referred to as foundation models (FMs). Deploy the generative AI foundation model Flan T5-XXL using JumpStart We can deploy this model via Studio as well as via API. The APIs are part of the SageMaker Python SDK itself.

APIs 79

APIs Healthcare Accountability Engineering 79

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 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 90

APIs Scripts Engineering Best practices 90

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 105

Personalization Customer Experience APIs Entertainment 105

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 80

APIs Scripts Accountability Enterprise 80

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

Kayako

MARCH 21, 2018

Unfortunately, there was no meeting room where I could sit down for training sessions , or 24/7 mentors who can walk me through the topics in my onboarding sheet. I would have probably needed more time getting through the teething phase had it not been for Kayako’s training videos. What do you need to make great training videos?

Customer Service 56

Customer Service APIs Best practices Feedback 56