AWS Partner Network (APN) Blog
Accelerate AI/ML Integration into Digital Content Workflows with Kablamo Digital Asset Management
By Kiran Kumar Ballari, Principal Solutions Architect – AWS
By Ian McKay, Cloud Principal – Kablamo
By Varun Mehta, Principal Business Development Manager – AWS
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| Kablamo |
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Internal and external customers are creating more data, faster than ever before. Organizations are dealing with petabytes of content clogging up storage—physically and digitally—that is strewn throughout the organization.
More and more customers are finding no consistency, governance, cataloging, searchability, and distribution capabilities for their digital content. Getting the right digital assets in the right hands at the right time is a mission-critical function; hence, customers are looking for a readily available digital asset management (DAM) solution.
Traditional content process workflows are expensive and complex to enhance with artificial intelligence (AI) and machine learning (ML) technologies. They usually involve customers conducting manual reviews of the content, and older content doesn’t have rich and relevant metadata that enables automation.
Kablamo’s Digital Asset Management solution on Amazon Web Services (AWS) leverages leading AI/ML services to enable customers to secure, index, cold-store, and access digital assets across the enterprise.
Kablamo DAM is right-sized for all customer industries and integrates into existing workflows with ease. It supports customers with archiving, searching, sorting, transcribing, and managing digital assets such as videos, audio recordings, pictures, and documents.
In this post, you’ll learn how the Kablamo DAM speeds up a centralized digital asset management environment setup, search and discovery, digital capture ingestion, and enriches digital assets by integrating with workflows.
Kablamo is an AWS Advanced Tier Services Partner and platform-led services company specializing in cloud, machine learning, digital transformation, product development, DevOps, automation and user experience (UX). Kablamo holds the AWS Machine Learning Competency.
Solution Overview
Many organizations don’t have access to the time and resources to create, enhance, and integrate digital workflows. Kablamo DAM helps with pre-built capabilities and automated workflows to free up time and resources.
The Kablamo DAM is an AWS cloud-native platform that can store, catalogue, meta tag, and search digital assets at scale. It’s built as a secure cloud-based portal, providing a centralized digital asset management environment.
Enterprise Digital Repository
Kablamo DAM creates a central place to house all digital files used across an organization. Anyone who needs to access business-critical assets needs only one search and retrieval platform. Digital files include videos, images, and documents like legal, financial, and domain-specific file types interpreted by AI/ML services.
Integrated AI/ML Services
Kablamo DAM extracts metadata from digital content through use of artificial intelligence and machine learning, enriching metadata such as audio-to-text transcriptions, sentiment analysis, and object recognition. It consumes the latest in cloud AI/ML services, including Amazon Rekognition for object and face detection, Amazon Transcribe for speech-to-text, Amazon Textract to extract digital content, and Amazon Comprehend for sentiment analysis.
APIs
The Kablamo DAM exposes a suite of RESTful API interfaces and cloud-native messaging capabilities, allowing customers to build new workflows and integrate into existing content workflows. Users can use a concise set of easy-to-understand, cloud-based, and secure API endpoints in their business applications to consume all functions they can do within the web interface.
Cloud-Native Technology
Using the best tools for the job is key to building a flexible and stable DAM platform. Modern cloud application development principles are followed for greater adaptability.
Workflows
A workflow in-built engine that allows the asset to transition through many states throughout its lifetime allows user-defined actions to occur when transitioned. This enables bespoke approval procedures, publishing pipelines, and third-party integrations.
Scalability
Kablamo DAM scales gracefully and with minimal cost or risk. You can achieve open-ended scalability without re-architecting the software or putting customers through the pain of a “rip and replace” scenario. Scalability is not just the number of users—it’s the size of the dataset, the complexity of the application, and the number of developers and users working with it.
Extensibility
The solution was built with expansion and integration as primary features. Customers can adapt the latest AWS technologies as they become available or integrate third-party data sources. Customers can define their own metadata fields to be displayed at certain points of an asset’s lifetime, including text fields, drop-downs, toggles, user pickers, and date pickers.
Kablamo DAM Architecture
The Kablamo DAM solution architecture provides a simple interface that’s easy to navigate and use by content consumers and producers. It also allows users to run in a completely headless mode, providing programmatic access to DAM features and functionality via RESTful API.
The solution is built on top of AWS-native services using serverless technology to keep performance and scalability high and running costs low.
Figure 1 – Kablamo DAM architecture.
Kablamo DAM is an extensible open platform, built to be changed as new AWS technologies are available. As illustrated in the diagram above, the solution is entirely composed of serverless components and AWS managed services.
- Users can access the React-based web interface of DAM portal using Amazon CloudFront and store application artefacts in Amazon Simple Storage Service (Amazon S3) buckets. Built to embrace change, Kablamo streamlined the user experience (UX) down to a set of most-used use cases, and the solution offers visual customizations for logos, colors, and fonts. CloudFront distribution is used to serve and cache publicly available assets.
. - With a simple interface, content administration, UX, and role-based access interface are implemented using Amazon Cognito. User sign-up, authentication, and federation are provided by Cognito and third-party identity providers (IdPs). Where existing identity directories are in place, such as Auth0 or Azure AD, Kablamo DAM refers to those directories for authentication.
. - The web interface makes calls to the REST API endpoint on behalf of the user and represents the data back to them. All actions performed from the web interface are performed via the REST API. The REST API uses Amazon API Gateway to serve all API methods used by the user interface.
. - Once a record is created via the user interface (UI) or API, the solution stores this metadata data in Amazon DynamoDB, and rapid search-based retrieval content is made available in Amazon OpenSearch Service.
. - The solution has digital asset storage, backed by Amazon S3 for uploading digital assets videos, images, audio, or documents. This means customers can onboard large on-premises datasets using AWS DataSync or upload files using the drag-and-drop interface. The record creation also returns a pre-signed URL where the user may begin their resumable upload of their asset/file.
. - Kablamo DAM provides in-built workflows to archive to low-cost and long-term cloud storage using Amazon S3 Glacier within the solution, which provides significant cost savings to the customer.
. - Once digital assets are ingested, the bucket uses an Amazon SNS > Amazon SQS > AWS Lambda pattern to have the pre-processing function analyze the file and store initial metadata such as file type and size in the data stores, and decide to invoke downstream pipelines. The solution makes use of this pattern to notify events within solution and external components.
. - Downstream pipelines are triggered conditionally based upon the file type detected, and customers can configure these events based on their digital workflow requirements. For example, a video may trigger both the Amazon Rekognition and Amazon Transcribe pipelines, whereas a PDF may only invoke the document pipeline using Amazon Textract. The solution allows for additional custom or bespoke pipelines that may use specific processing capabilities to match the use case.
. - A downstream pipeline typically features a start component, which processes assets at a predictable pace; an enrichment component, such as Amazon Rekognition or Amazon Comprehend; and a post-processing component using AWS Lambda, which transforms the raw data from the enriched component and places it into the data stores. Kablamo DAM provides the ability to integrate with Amazon SageMaker to onboard custom ML models.
. - Besides pre-built workflow templates, the solution provides the ability to add actions with custom templates. This allows integrations, such as sending an email or SMS or a message via Slack or Teams, to an approver with the title and direct link to the asset.
. - A Lambda ingestion function is implemented to ingest content from Google Drive and OneDrive, making the files accessible within the solution and instantly searchable.
. - All data stored or transiting via Kablamo DAM is encrypted, using AWS Key Management Service (AWS KMS) customer managed key for stored data within Amazon S3, SQS, and TLS for in-flight. Amazon CloudFront is used to improve performance by lowering the latency between the keyboard and the web server.
. - Kablamo DAM breathes operational data and exposes key metrics, connected traces, and alerts to drive performance improvements and business success. Platform monitoring, governance, and observability are implemented through Amazon CloudWatch and AWS CloudTrail with the ability to integrate with customer enterprise systems. All actions are recorded on an immutable audit log for auditing and compliance purposes.
. - The solution implements cost transparency and spend controls using AWS Budgets and AWS Cost Explorer to track digital asset consumption at the enterprise level.
Digital Asset Management Workflow
Kablamo DAM is the first step in a digital transformation for industries that have a requirement to keep and assess large amounts of digital assets—particularly in video, audio, and document formats—that may need to refer to those assets in the longer term.
The solution comes with in-built foundational workflows for Police & Justice, Education, Research, and Galleries, Libraries, Archives and Museums (GLAM).
Focusing on a single use case and industry here, the following walkthrough dives into the standard Police & Justice workflow. Both have a requirement to record many of their interactions with the public, such as police interviews and court hearings.
Additionally, an officer may need to analyze a lot of video and audio footage, from recordings to CCTV footage.
Figure 2 – User workflow details.
With the Kablamo DAM solution, officers interview candidates, and audio/video interviews are recorded and uploaded. Users can select readily available workflows through the web interface and ingest raw data to trigger the workflow content processing steps.
Workflow actions may trigger either on-demand or event-based to convert uploaded raw digital content to standard formats, and then store them in a central repository with role-based access controls.
The solution automatically extracts keywords and named entities from content and makes it available for prosecutors to review and complete. Finalized digital content is securely accessible, indexed, and searchable, and analysis is ready for various personas.
Step 1: Configure User Types and Global Features
To get started, sign in to the Kablamo DAM portal with your login details. The solution has two main user types: users and admins.
Admins have escalated rights and may view all assets, regardless of their permissions. By default, users may only view and search on assets they have created themselves, or other assets explicitly shared with them. Users can change this behavior using the workflow engine.
Select the required ML features and click Save.
Figure 3 – Kablamo persona-driven user interface.
Step 2: Define a Workflow and Implement
Log in to the portal as a system administrator and perform the steps to add and edit processing workflow to integrate into existing content flows.
In the UI, define a role-based workflow that enables investigating officers to upload the audio. Configure the workflow to convert voice-to-text conversion and automatic transcription.
Figure 4 – Workflow configuration.
Step 3: Content Creation and Processing
In this example, an investigating officer plans and runs an interview with the relevant people and captures digital content in audio/video/document format.
To upload input data to the content store, click the Upload button along with metadata information to start the workflow for further processing.
Figure 5 – Content ingestion and processing.
Step 4: Review Digital Content
Once digital content uploaded, transcribers or administration officers review the accuracy of the generated transcript and make any required changes via the in-built transcript editor.
The web application includes inline previews for browser-compatible videos, audio files, images, and documents such as PDFs. It also provides a detailed asset viewing page which includes the user-defined sections and fields and enrichment data, such as transcriptions and detected objects/celebrities.
Figure 6 – Review, edit, and approve enhanced content.
Step 5: Metadata Enrichment and Content Finalization
Immediately after uploading content, the solution will automatically perform metadata extraction based on field mapping and store processed output. Content is then indexed automatically for officers to search for files and sends to other users or downstream systems.
The unified free-text search provides results of objects or persons found via object recognition within an asset, where available. Outbound webhooks also allow for easy integration with external systems.
Figure 7 – Metadata enrichment and content publishing.
Summary
While there are many digital asset management (DAM) solutions, many are aimed at the media and entertainment industry or content producers, and can take months and years to configure and implement.
Other challenges for traditional DAM services include a lack of AI/ML automation that require tedious manual metadata tagging and/or transcription.
In this post, we explored how Kablamo’s Digital Asset Management solution is powered by AWS AI/ML services and is designed to get the most out of digital assets such as video, audio, documents and images.
We walked through the steps a user would take to access the DAM solution, ingest digital assets, configure context-specific workflows, run metadata enrichment, and enable users to search for content. Kablamo DAM is available on AWS Marketplace.
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Kablamo – AWS Partner Spotlight
Kablamo is an AWS Advanced Tier Services Partner and platform-led services company specializing in cloud, machine learning, digital transformation, product development, DevOps, automation and user experience.