Datatron Releases New Governance Dashboard to Provide Trust, Transparency, Traceability, and Validation of AI/ML Solutions
With new offering, data scientists and analysts can monitor and optimize their AI solutions to produce predictable and reliable business outcomes.
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Datatron, a vendor specializing in AI ModelOps and governance at scale, released the Datatron Governance Dashboard, an adaptive, performance-based artificial intelligence (AI) governance solution. The product provides AI and machine learning (ML) model transparency risk management to comply with regulations while optimizing business outcomes.
Because many ML initiatives work in isolation from each other and don't directly support -- or can even hinder -- a company's broader business, regulatory, and privacy objectives, the Datatron Governance Dashboard provides model traceability and confidence in AI applications. The dashboard provides analytic leaders, business stakeholders, and data scientists a multi-level view of how their models perform in production via the smart visualization of key metrics.
Technologies such as ML and deep learning (DL) are becoming critically important for organizations looking to increase revenue and competitive advantage. However, along with these new technologies came new government guidelines and regulatory approaches because AI/ML models can pose risks to privacy, brand reputation, autonomy, and growth. Models must be carefully managed and appropriately governed because further AI/ML adoption and acceptance depend on trust, transparency, and validation. The new Datatron Governance Dashboard facilitates compliance with audits and regulations and delivers risk assessment and management analysis that should be the foundation of any AI/ML program.
The Datatron Governance Dashboard helps organizations reap the benefits of bringing trust and transparency to AI applications by providing the ability to:
- Improve productivity through automation and standardization of ML operations, such as deployment, management, monitoring, and validation.
- Drill down from high-level governance and risk metrics to root-cause analysis.
- Provide auditing and traceability of anomalies, bias, data drift, model drift, and performance, as AI models change over time.
- Configure and monitor their own governance metrics as targets.
- Establish a centralized model catalog that provides lineage.
- Facilitate compliance with regulations.
- Adopt a test-and-learn mindset to improve outcomes, and assure continued delivery of value over time.
“We consistently hear from prospects about poor model performance and a serious lack of trust in predictions from AI applications," said Harish Doddi, Datatron CEO. "The ability of large companies to operationalize and govern their AI/ML models has far exceeded their infrastructure and the bandwidth of their engineering and data science teams. We established Datatron to operationalize and govern AI model management at scale. Leveraging the new Datatron Governance Dashboard, organizations can monitor deployments, detect problems early, and increase the efficiency of managing multiple models at scale in order to maintain compliance and growth.”
For details, visit www.datatron.com.