Generative AI Lab is the most highly used No-Code human-in-the-loop tool for AI teams. It offers End-to-End data labeling and DL model training features.
Generative AI Lab (previously known as NLP Lab and Annotation Lab) is an End-to-End No-Code platform for annotating text and training AI/ML models. It enables domain experts to extract meaningful facts from text documents, images or PDFs and train models to automatically predict those facts on new documents.
By offering out-of-the-box support for Large Language Model prompting, Zero-Shot prompting, Rules and state-of-the-art John Snow Labs pre-trained models, Generative AI Lab helps domain experts efficiently prepare training data for tuning custom AI models for specific tasks and use-cases.
The annotation tool also supports human-in-the-loop workflows. In industries like healthcare, in which regulatory-grade accuracy is a requirement, human validation is often a critical requirement. The tool supports task management, full audit trails, custom review and approval workflows, versioning, tuning, testing and analytics, fully supporting the human-in-the-loop needs of high-compliance industries.
About the offer:
Based on an auto-scaling architecture powered by Kubernetes, the annotation tool can scale to many teams and projects. Enterprise-grade security is included, with support for air-gap environments, zero data sharing, role-based access, full audit trails, MFA, and identity provider integrations. Generative AI Lab allows powerful experiments for model training and finetuning, testing, and deployment as API endpoints. There is no limitation on the number of users, projects, tasks, models, or trainings that can be run with this subscription.
This product includes a Pay-As-You-Go license key for John Snow Labs libraries and models, that offers access to 40.000+ models and pipelines for healthcare, legal, finance, downloadable from the NLP Models Hub and with access to OCR and Visual Document understanding features.
Designed to take advantage of GPU architecture, the product offers a boost in performance for model training and preannotation tasks - https://nlp.johnsnowlabs.com/docs/en/CPUvsGPUbenchmark_healthcare
You will be charged ONLY as long as you use the product. Simply stop your instance and restart it when needed it so you get charged only based on what you consume.
Included Features:
Prompt engineering for Large Language and Zero-Shot Models - entity recognition, relation extraction, classification.
AI-Assisted Annotation: never start from scratch but reuse existing resources to pre-annotate tasks with the latest models for classification, NER, assertion status, entity resolution, relation detection;
High productivity annotation UI with keyboard shortcuts and pre-annotations;
Annotation support for Text, Image, Audio, Video and HTML;
Text annotation in 250+ languages;
Projects and teams: 30+ project templates; unlimited projects and users, project import, export , cloning, grouping;
Task assignment, tagging, and comments; deduplication, searching and filtering;
Inter Annotator Agreement charts;
Enterprise-level security and privacy: role-based views and access control, annotation versioning, full audit trail, SSO;
Full NLP Models Hub integration: explore and download models and embeddings, to reuse those in your projects.
Train Classification, NER, and Assertion Status models: use default parameters, tune them on the UI for your experiments;
Active Learning automatically trains new model versions once new annotations are available;
Playground - deploy, test, and update prompts, rules and models before including them in your project;
API access to all features for easy integration into custom pipelines;
Who is this offer for
Domain experts (e.g. nurses, doctors, lawyers, accountants, investors, etc.) who want to test DL models on their data or/and tune/train new models via an easy-to-use UI, without writing a line of code;
Data labeling teams who want to optimize the efficiency and speed of their day-to-day work with preannotations;
Machine Learning engineers who need to test/train/tune NLP models;
Researchers who need to extract meaning from unstructured, natural language documents;
And anyone else interested in text and image analysis, image digitization, data extraction, document labeling and/or NLP model training.
Target verticals
Its integration with the NLP Models Hub facilitates access to over 40k pre-trained models for general-purpose text documents as well as 2000+ pre-trained models covering 400+ clinical and biomedical entity types.
3 Easy Steps to get started
Subscribe to the product on the AWS Marketplace.
Deploy it on a new machine.
Access the login page for a guided experience on http://INSTANCE_IP. For the first login use the following credentials:
Username: admin
Password: INSTANCE_ID
Highlights
Includes everything:
- Model Hub Integration
- Project Management
- Role Based Access
- Workflows
- Analytics
- Model Training and Testing
- Preannotations
- Security and Privacy
Unlimited everything:
- Users
- Projects
- Models
- Tasks
- Annotations
- Pre-annotations
- Training
Healthcare Resources
- Access to 2000+ Healthcare pre-trained models covering Clinical and Biomedical NER for 400+ entity types; Assertion Status detection (positive, negative, possible, past and future facts), Clinical Relation Extraction;
- De-identification NER Models
- Model tuning - Build your models on existing pre-trained models
- Programmatic labeling via dictionary and regex-based rules;
Visual Document Understanding
- Pre-annotate PDF and image tasks with Visual NER models;
- Tune Visual NER models for your data;
- Sticky and custom annotations;
- Automatic text recognition;
- Support for relation annotation on top of images;
- Text-based search on the image/PDF;
- Zoom features;
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
You pay based on usage, with three metered dimensions. The base charge covers the No-Code Generative AI Lab instance, billed per processor per hour while it runs. On top of that, two feature-specific charges apply per processor per minute of active use. One covers Medical Model usage for annotation or training. The other covers Visual Document import, annotation, or training. These feature charges add to the instance rate only when you use those capabilities. All usage is metered and reflected on your AWS bill.
Top-of-mind questions for buyers
What does 'per processor' mean for billing, and does a stopped instance still accrue charges?
Billing meters per vCPU. The instance dimension charges per processor for each hour it runs. When you stop the instance, hourly software charges stop, since usage is metered and reflected on your AWS bill. Underlying AWS infrastructure costs may still apply separately based on your deployment.
Which dimension drives most of my cost, and how do the three combine?
The instance charge accrues continuously per hour while the Lab runs. The two feature charges bill per processor per minute, but only during active Medical Model use or Visual Document work. All three add together on your bill. The instance rate dominates for continuous operation; feature charges rise with heavy annotation or training.
Does processing more documents, users, or models raise my bill beyond the metered rates?
No. You face no limit on projects, users, documents, models, prompts, or trainings. Cost tracks compute time only, metered per processor. Running more feature-based work extends billed minutes, and running the instance longer extends billed hours. Adding users or documents alone does not add separate charges.
www.johnsnowlabs.com
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Vendor refund policy
Users need to pay price to Amazon according to the EC2 instances/servers used.
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Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA).
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An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Generative AI Lab 8.2.1
Bug Fixes
Terminology Server Deployment Stability
Deploying a Terminology Server after an existing Healthcare model could result in orphaned server deployments. Server lifecycle management has been corrected to ensure Healthcare and Terminology Server deployments are associated and managed correctly.
Connected Word Selection in Visual NER
Selecting previously created connected words in Visual NER projects could redirect users to the "Something Went Wrong" page, interrupting annotation workflows. Connected word selection now functions correctly, allowing annotations to be reviewed and edited without errors.
Request Center Notification Reliability
Request Center notifications could behave inconsistently across supported workflows. Notification handling has been improved to ensure requests and related updates are displayed reliably.
Medical Terminology Lookup Positioning
The Medical Terminology lookup dropdown could appear in the incorrect location instead of next to the selected annotation. Dropdown positioning has been corrected, providing a more consistent annotation experience.
Reuse Resource Rule Visibility
The Reuse Resource page displayed only the first 15 available rules, preventing users from accessing larger rule collections. Rule listing has been updated so all available rules are displayed correctly.
Join our free, hands-on training sessions on April 7th and 8th and experience how Generative AI Lab can streamline your annotation and model training workflows, no commitment required!
During these sessions, you will learn how to quickly annotate data using AI powered pre-annotation, explore de identification workflows for compliance and data privacy, train and deploy custom AI models with one click, get answers to your questions from product experts in real-time.
This is a great opportunity to test-drive the platform and experience the value of Gen AI Lab firsthand.
Reach out to us at AWS-sales-support@johnsnowlabs.com
John Snow Labs also offers professional services to deliver custom data science work that is specific to your needs. Our team of experts is ready to assist you with various tasks, including training custom AI models, developing machine learning pipelines, annotating documents, creating Python notebooks, generating insightful reports, and much more. Our professional services are specifically designed to help you achieve remarkable results without the steep learning curve or overwhelming workload.
In addition, when you opt for an annual NLP Libraries prepaid subscription you gain access to a host of exclusive benefits:
A dedicated customer success manager
A dedicated account manager
Four hours of personalized onboarding from our data scientists
Year-long customer support on a dedicated Slack channel
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