AI Transformation with ModelOps

ModelOp Announces ModelOp Center Release 2.3

ModelOp Extends Automation and Governance for Artificial Intelligence and Machine Learning Models

ModelOp Center integrates with DataOps and SecOps Systems

ModelOp, the pioneer of ModelOps software for large enterprises, today announced important upgrades to its ModelOp Center platform, extending automation for governing and monitoring artificial intelligence (AI) and machine learning (ML) models, making it easier and faster to securely get models into production and recognize business benefit and revenue contribution.

“Enterprises have substantial investments in their existing IT and governance processes and systems. To capitalize on the value that ModelOps delivers, it is essential to integrate with these processes and systems to put models into business faster and ensure they continue to produce optimal business outcomes,” says Dave Trier, Vice President of Product at ModelOp. “Integration with existing systems eliminates technical and process redundancy and allows enterprises to leverage the substantial investments they have already made in their data and analytics platforms, infrastructure, stores and security systems.”

In the State of ModelOps 2021 Report, respondents identified lack of integration with existing systems and applications as the most challenging barrier to operationalizing models. New capabilities included in the ModelOp Center upgrade include:

● Integration with Snowflake, SQL Server, PostgreSQL, IBM DB2, Spark, and HDFS provides direct data consumption for these widely used data sources. ModelOp Center uses an abstraction layer that allows models to connect to a variety of data sources and model execution platforms for both batch and online business use cases.
● Integration with Veracode enables automated code scanning for verification of code completeness for each and every model when deployed.
● Integration with Vault ensures there is centralized access control to models and the applications, data sources and systems that they interconnect with throughout their operational life.
● An enhanced user interface with guided task lists that enables a broader range of users with varying levels of expertise to easily monitor and govern models throughout their operational life.

As enterprises increase their use of AI models, they quickly realize they need ModelOps automation to grow and scale their AI efforts. According to Gartner, more than 50 percent of AI models never go into production fully due to insufficient model operational processes.

ModelOp Center addresses this challenge by automating the governance, monitoring and orchestration of post-development AI/ML models across platforms and teams, accelerating time to production, and reducing costs and business risks.

A comprehensive process library, custom metadata and monitoring models accelerate time to operationalization by as much as 50 percent. Real-time monitoring and integration with development platforms, data sources, IT systems, Model Risk Management systems and business applications helps customers automate and scale AI and ML models, reducing costs by up to 30 percent.

Additional resources:
● Watch a short video about ModelOp Center v2.3 integrations
● Read the ModelOps guide, 4 Steps to Successful Model Operations
● Download the State of ModelOps 2021 Report
● Learn more about ModelOp Center

About ModelOp:
ModelOp, the pioneer of ModelOps software, enables large enterprises to address the critical governance and scale challenges necessary to fully unlock the transformational value of enterprise AI and Machine Learning investments. Core to any AI orchestration platform, G2000 companies use ModelOp Center to govern, monitor and orchestrate models across the enterprise and deliver reliable, compliant and scalable AI initiatives.

PRWeb

All ModelOp Blog Posts 

Five ways to mitigate the risk of AI models

Five ways to mitigate the risk of AI models

In recent years, the banking industry has been at the forefront of AI and ML adoption. A recent survey by Deloitte Insights shows 70% of all financial services firms use machine learning to manage cash flow, determine credit scores, and protect against cybercrime.