If you’ve been here — manual model monitoring for quant trading systems that doesn’t detect issues early enough Or here — deafening noise from alerts that makes it difficult to actually identify root causes and resolve issues efficiently, this one's for you. In our upcoming webinar ?????????? ?????????????????????? ???????????????????? ?????? ?????????? ?????????????? ?????????????? you’ll hear: + How to get granular insights and systematically reduce noise to minimize false positives and alert fatigue. + Best practices for tailoring monitoring approaches to your specific needs, ensuring that critical issues are detected without overwhelming your team. + Real-world examples of how data science teams have successfully navigated this conundrum to improve model performance and operational efficiency. ??? Date: October 10th, 2024 ?? Time: 11am ET | 8am PT ?? Learn more and register: https://lnkd.in/eUNiD8Mh #QuantTrading #DataScience #ModelMonitoring #QuantModels #MLOps #Webinar #AlgorithmicTrading #AlgoTrading #Mona
Mona
软件开发
Gain complete visibility into your data, models and processes with the most flexible monitoring solution
关于我们
Mona's intelligent monitoring solution enables teams to gain complete visibility into the performance of your data, models and processes. Automatically surface and resolve performance issues within your AI / ML or intelligent automation processes to avoid negative impacts to both your business and customers.
- 网站
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https://www.monalabs.io
Mona的外部链接
- 所属行业
- 软件开发
- 规模
- 11-50 人
- 类型
- 私人持股
- 创立
- 2018
产品
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Mona
数据科学与机器学习平台
Mona provides data scientists and machine learning engineers an end-to-end monitoring solution that boosts visibility in their AI systems. It starts with ensuring there’s a single source of information for the systems’ behavior over time, and continues with ongoing tracking of key performance indicators, and proactive insights about pockets of misbehavior - enabling teams to take preemptive, efficient corrective measures. By providing real-time insights, Mona enables teams to detect issues weeks or even months before they come to the surface, allowing them to quickly troubleshoot and resolve the anomalies. This level of visibility and control is essential for organizations to build trust in their AI systems and avoid costly AI catastrophes. Mona’s AI fairness feature allows teams to gain a deep understanding of the behavior of their machine learning models across various protected segments of the data and in the context of the business function that the models serve.
Mona员工
动态
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In the fast-paced world of quant trading, model performance isn’t just about what you see on the surface—it’s about catching issues before they impact returns. Standard monitoring practices focus on high-level metrics or system health, but in quant trading, these don’t tell the full story. Subtle data shifts, anomalies in smaller segments, or unexpected third-party data changes can quietly erode model accuracy and, ultimately, profitability. That’s why we advocate for a more proactive, specialized approach to monitoring quant models. Instead of reacting to issues after they hit your bottom line, specialized monitoring empowers teams to detect and address changes in real-time across key areas, whether it’s geography, data source, or market conditions. Check out our latest blog post to dive into why specialized monitoring matters in automated trading systems. After all, when you're handling complex, high-stakes models, there’s no room for blind spots. #QuantTrading #DataScience #ModelMonitoring #MLOps #QuantFinance
The Need for Specialized Monitoring in Quantitative Trading Models
monalabs.io
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Join us today for ?????????? ?????????????????????? ???????????????????? ?????? ?????????? ?????????????? ?????????????? as we dive into best practices for implementing deep monitoring practices that provide early detection and resolution of issues - without noise. Our Co-Founder & CEO, Yotam Oren, and Chief Product Officer, Itai Bar-Sinai, are diving into practical steps to transform your model monitoring practices with real-word examples from the field. Register to join us live, or catch the session on demand. See you out there! #modelperformance #modelmonitoring #quanttradingsystems #quantresearch
[WEBINAR] Model Performance Monitoring for Quant Trading Systems
us02web.zoom.us
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Mona转发了
Looking forward to discussing the challenges and solutions around model and data monitoring in quant/algo-trading systems on 10/10/2024!
If you’ve been here — manual model monitoring for quant trading systems that doesn’t detect issues early enough Or here — deafening noise from alerts that makes it difficult to actually identify root causes and resolve issues efficiently, this one's for you. In our upcoming webinar ?????????? ?????????????????????? ???????????????????? ?????? ?????????? ?????????????? ?????????????? you’ll hear: + How to get granular insights and systematically reduce noise to minimize false positives and alert fatigue. + Best practices for tailoring monitoring approaches to your specific needs, ensuring that critical issues are detected without overwhelming your team. + Real-world examples of how data science teams have successfully navigated this conundrum to improve model performance and operational efficiency. ??? Date: October 10th, 2024 ?? Time: 11am ET | 8am PT ?? Learn more and register: https://lnkd.in/eUNiD8Mh #QuantTrading #DataScience #ModelMonitoring #QuantModels #MLOps #Webinar #AlgorithmicTrading #AlgoTrading #Mona
Mona | Webinar | Performance Monitoring for Quant Trading Systems
monalabs.io
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???? ?????????? ??????????????, ?????????? ?????????????????????? ????????????. What if you could catch issues with your trading models before they impact your bottom line? Discover how model performance monitoring tools are changing the game for quant hedge funds, ensuring consistent performance in even the most volatile markets. ?? https://lnkd.in/efD_yEWv #QuantHedgeFunds #QuantitativeTrading #MLOps #TradingSystems #FinancialMarkets #AlgoTrading #TradingTechnology
How to Ensure Consistent Performance in Quant Trading Systems
monalabs.io