Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Find out what companies such as Cisco, Reveal Data and Absolute Software already know about Catalyst, Catalyst, and others! Document Filters is the perfect toolkit to allow file inspection and processing functionality within applications for ediscovery, data protection prevention, text analytics and content management. It also allows you to search, archive, and search for files. Are your end users lost in file formats and document volume? We explain how Document Filters Drives Efficiency & Customer Value and how it can make a huge impact on all users. Document Filters allows software developers to integrate industry-leading file identification functionality in their solutions. File inspection and identification are essential first steps if your application relies upon processing files it didn't create. Document Filters uses intelligent file identification to inspect source content without relying only on the filename extension.
Description
KitOps serves as a robust system for packaging, versioning, and sharing AI/ML projects, leveraging open standards to seamlessly integrate with existing AI/ML, development, and DevOps tools, while also being compatible with your enterprise container registry. It has become the go-to choice for platform engineering teams in the AI/ML domain seeking a secure method for packaging and managing their assets.
With KitOps, you can create a comprehensive ModelKit for your AI/ML projects, encapsulating all elements necessary for local reproduction or production deployment. Additionally, the ability to selectively unpack a ModelKit allows team members to optimize their workflow by only accessing the components pertinent to their specific tasks, thereby conserving both time and storage resources. Given that ModelKits are immutable, can be signed, and reside within your established container registry, they provide organizations with an efficient means of tracking, controlling, and auditing their projects, ensuring a streamlined workflow. This innovative approach not only enhances collaborative efforts but also fosters consistency and reliability across AI/ML initiatives.
API Access
Has API
Yes
API Access
Has API
No
Screenshots View All
No images available
Integrations
.NET
Yes
C
Yes
C#
Yes
C++
Yes
Hyland RPA
Yes
Java
Yes
Python
Yes
Integrations
.NET
No
C
No
C#
No
C++
No
Hyland RPA
No
Java
No
Python
No
Pricing Details
No price information available.
Free Trial
Yes
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
Yes
iPad App
Yes
Android App
Yes
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
Yes
In Person
No
Types of Training
Training Docs
No
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Hyland
Founded
1991
Country
United States
Website
www.hyland.com/en/solutions/products/document-filters
Vendor Details
Company Name
KitOps
Founded
2024
Country
Canada
Website
kitops.ml
Product Features
Document Management
Access Controls
No
Archiving & Retention
Yes
Collaboration Tools
No
Compliance Tracking
No
Document Archiving
Yes
Document Assembly
No
Document Capture
No
Document Conversion
No
Document Delivery
No
Document Indexing
No
Document Retention
No
Electronic Signature
No
Email Management
No
File Recovery
No
File Type Conversion
No
Forms Management
No
Full Text Search
No
Offline Access
No
Optical Character Recognition
No
Print Management
No
Version Control
No
Product Features
DevOps
Approval Workflow
No
Dashboard
No
KPIs
No
Policy Management
No
Portfolio Management
No
Prioritization
No
Release Management
No
Timeline Management
No
Troubleshooting Reports
No
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No