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    Generative AI Lab - Human-in-the-Loop AI Annotation, Tuning & Testing

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    Deployed on AWS
    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.
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    Overview

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    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.

    Technical Specifications Operating System:Ubuntu 20.04

    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;

    Details

    Delivery method

    Delivery option
    64-bit (x86) Amazon Machine Image (AMI)

    Latest version

    Operating system
    Ubuntu 24.04

    Deployed on AWS
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    Pricing

    Generative AI Lab - Human-in-the-Loop AI Annotation, Tuning & Testing

     Info
    Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
    Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator  to estimate your infrastructure costs.

    Usage costs (3)

     Info
    Dimension
    Cost/unit
    No-Code Generative AI Lab Instance, per processor per hour
    $0.07
    Medical Model Usage for Annotation or Training, per processor per min
    $0.099
    Visual Document Import, Annotation, or Training, per processor per min
    $0.099

    Vendor refund policy

    Users need to pay price to Amazon according to the EC2 instances/servers used.

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    Legal

    Vendor terms and conditions

    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) .

    Content disclaimer

    Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.

    Usage information

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    Delivery details

    64-bit (x86) Amazon Machine Image (AMI)

    Amazon Machine Image (AMI)

    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 8.0.0

    Generative AI Lab 8.0 delivers a major advancement in healthcare annotation and document ingestion workflows, introducing native Medical Terminology support and built-in PDF text extraction for NER projects.

    The headline feature is integrated Medical Terminology support, enabling automated code resolution and manual lookup across standards such as ICD-10, LOINC, SNOMED CT, CPT, and more directly within annotation workflows.

    This release also introduces built-in PDF text extraction with structure preservation, allowing teams to process complex documents without relying on external licensed OCR server.

    In addition, the Cluster Management dashboard has been redesigned to provide improved visibility into system resources and deployments, and the platform now runs on PostgreSQL 17.6 to deliver enhanced performance, stability, and long-term support.

    Medical Terminology Integration Medical code resolution is now native to annotation workflows. Download ICD-10, LOINC, CPT, SNOMED CT, RxNorm, and MeSH terminologies from the Hub and link them to labels during project configuration. The system automatically resolves medical codes during pre-annotation and supports manual lookup directly within the annotation interface. Entity, label, and standardized code travel together through the workflow and export.

    Built-In PDF Text Extraction for NER Projects NER text-based projects can now import PDFs and extract text directly within the platform, no OCR server deployment or licensing cost required. The built-in extraction preserves document structure including reading order, paragraph boundaries, and sections. For standard clinical documents, it handles text extraction reliably with zero setup. Licensed OCR capabilities remain available as an upgrade for enterprise-grade precision requirements.

    Improvements:

    Redesigned Cluster Management Dashboard The Cluster page now displays real-time system resource usage (Memory, CPU, Storage) through visual summary cards at the top of the dashboard. Server configurations are displayed with inline tags showing deployed models, rules, prompts, and medical terminologies. Enhanced license information provides contextual details for Airgap, Floating, and Universal licenses. Detailed resource allocation is visible for each server entry.

    PostgreSQL 17.6 Upgrade The underlying database engine has been upgraded to PostgreSQL 17.6 for improved performance, stability, and long-term support. This is a backend enhancement with no impact on installation, upgrade workflows, or existing application behavior. The upgrade is fully backward compatible.

    Selective Medical Terminology Deployment A dedicated deployment button on the Task page allows users to deploy Medical Terminology servers independently without initiating pre-annotation servers. This enables faster setup for terminology-based manual annotation workflows and reduces resource consumption.

    Bug Fixes:

    Discrepancy in Regions When Multiple Models are Added Inconsistent display of annotated regions when multiple models were used within a project has been corrected. All annotated regions are now shown with accurate counts.

    Import Button Responsiveness During Document Import The Import button now remains responsive during ongoing document imports, with a loading indicator providing clear feedback during the import process.

    Pre-Annotate Button Loading State After Re-Deployment UI feedback has been updated so the Pre-Annotate button transitions instantly to a loading state after re-deploying the pre-annotation server.

    Annotation Discrepancy with "Label All Occurrence" in Side-by-Side Projects Annotation behavior has been corrected to ensure consistent application of the Label All Occurrence option across multi-page documents in Side-by-Side projects.

    Model Availability in Image Text Side-by-Side Projects Successfully deployed models now appear as expected in the Pre-Annotation section for Image Text Side-by-Side projects.

    Pre-Annotation Server Auto-Deployment After Training Pre-annotation servers now deploy automatically after training completion, ensuring models are available for immediate use.

    Lookup Dropdown in Pre-Annotation Completions The lookup dropdown no longer appears in pre-annotation completions, correcting unintended UI interaction.

    Full release notes: https://nlp.johnsnowlabs.com/docs/en/alab/release_notes 

    Additional details

    Usage instructions

    Ensure the IAM role attached to the AMI machine has access to both aws-marketplace:MeterUsage and ec2:DescribeInstanceTypes permission.

    Launch the AMI Generative AI Lab will then be served on http://<public ip of instance>

    To login use the following credentials

    • username: admin
    • password: <instance-id from AWS EC2>

    Support

    Vendor support

    Upcoming Live Training : Try Before You Buy!

    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 

    Technical support for Generative AI Lab by Development Team 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

    Additional Resources:

    AWS Marketplace Slack Channel: https://spark-nlp.slack.com/archives/C064YR9NLBX 

    End-to-End No-Code Development of NER model for Text with Generative AI Lab: https://www.youtube.com/watch?v=jgUylZlz3uA&ab_channel=JohnSnowLabs 

    Generative AI Lab Release Notes: https://nlp.johnsnowlabs.com/docs/en/alab/release_notes  AWS-sales-support@johnsnowlabs.com 

    AWS infrastructure support

    AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.

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    Ratings and reviews

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    2 external reviews
    External reviews are from G2 .
    Ashpreet S.

    Must try Annotating Tool

    Reviewed on Nov 25, 2023
    Review provided by G2
    What do you like best about the product?
    It is very user friendly and easy to understand in camparison to other competetive products.
    What do you dislike about the product?
    As my use case I did like all the features.
    What problems is the product solving and how is that benefiting you?
    I am using this as an Annotating Tool for image datasets
    Eric L.

    Great annotation tool

    Reviewed on Oct 06, 2022
    Review provided by G2
    What do you like best about the product?
    It makes the annotation process very simple and efficient. Easy to use. Easy to manage the work.
    What do you dislike about the product?
    The review process is a little buggy and non-intuitive. That part of the workflow should be improved.
    What problems is the product solving and how is that benefiting you?
    It solves the problem of establishing ground truth when training or testing a model.
    View all reviews