Show Notes
- (01:46) Itai reflected on his education at The Hebrew University of Jerusalem, studying Math and Computer Science.
- (04:18) Itai walked through his time as a software engineer at Google working in Google Trends.
- (06:56) Itai emphasized the importance of a software checklist within Google's engineering culture.
- (08:55) Itai explained how he became fascinated with AI/ML engineering.
- (10:31) Itai touched on his period working as an AI consultant.
- (13:28) Itai talked about his side hustle as a co-owner of Lia's Kitchen, a 100% vegan restaurant in Berlin.
- (16:13) Itai shared the founding story of Mona Labs, whose mission is to make AI and machine learning impactful, effective, reliable, and safe for fast-growth teams and businesses.
- (21:25) Itai unpacked the architecture overview of the Mona monitoring platform.
- (24:50) Itai talked about the early days of Mona finding design partners.
- (27:15) Itai dissected his perspective on a comprehensive monitoring strategy.
- (31:42) Itai explained why the secret to comprehensive monitoring lies in granular tracking and avoiding noise.
- (38:35) Itai explained how Mona can support real-time monitoring across the layers of the platform.
- (43:18) Itai mentioned the integration with New Relic to display the variability of use cases for Mona.
- (46:08) Itai discussed the shift for data science teams from being research-oriented to product-oriented.
- (51:03) Itai provided four tactics for data science teams to become "product-oriented."
- (58:04) Itai shared valuable hiring lessons to attract the right people who are excited about the mission of Mona Labs.
- (01:01:46) Itai provided his mental model for finding exceptional engineering talent.
- (01:03:38) Itai brought up again the importance of finding lighthouse customers.
- (01:05:46) Itai gave his thoughts on building the product to satisfy different customer needs.
- (01:07:55) Itai described the thriving ML engineering community in Israel.
- (01:09:42) Closing thoughts
Itai's Contact Info
Mona Labs' Resources
Mentioned Content
Blog Posts and Talks
- We are building Mona to bring ML observability to production AI
- The definitive guide to AI/ML monitoring
- The secret to successful AI monitoring: Get granular, but avoid noise
- Taking AI from good to great by understanding it in the real world (June 2022)
- Data drift, concept drift, and how to monitor for them
- The issues ML model retraining won't solve
- Common pitfalls to avoid when evaluating an ML monitoring solution
- Introducing automated exploratory data analysis powered by Mona
- Best practices for setting up monitoring operations for your AI team
- The challenges of specificity in monitoring AI
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