TrueML Talks #35 | Big Data and ML Practices at Palo Alto Networks

In this episode of #TrueMLtalks, Harsh from Palo Alto Networks shares insights into the Machine Learning processes at Palo Alto Networks.
The discussion with Harsh covered a wide array of topics, including:
✨ Harsh's background in ML and working at Palo Alto Networks
✨ The types of problems Palo Alto Networks solves
✨ The data pipeline and model training process
✨ Techniques like model versioning and serving models as a service
✨ Challenges around latency, cost, and prompt injection
✨ BThe rewarding impact of Palo Alto Networks' work
_____________________________________
About Harsh: / harshverma59
_____________________________________
ABOUT OUR CHANNEL
We already have a video series of TrueMLTalks in which we interview machine learning industry professionals from companies such as Gong, StichFix, SalesForce, Facebook, Simpl, and others. We provide an insightful understanding of their experiences managing complex ML pipelines and developing successful best practices, making it a valuable resource for professionals looking to stay current on the latest advances in the field. We’ll be covering more topics around ML, MLOps, and the future of AI in general while showcasing the use cases of our platform in this journey.
Subscribe to our channel for more such content -
www.youtube.co...
_____________________________________
ABOUT TRUEFOUNDRY
TrueFoundry is a cross-cloud machine learning deployment PaaS that enables enterprises to speed up model testing and deployment while maintaining full security and control for the Infra/DevSecOps team. We enable machine learning teams to deploy and monitor models in 15 minutes with 100% reliability and scalability, saving money and allowing models to be released into production faster, resulting in genuine business value. We deploy on the customer's infrastructure, taking data privacy and other security concerns into consideration.
⚡ A short ~4 min demo of the platform: t.ly/0fk1
Subscribe to our newsletter: t.ly/oORp