True ML Talks #8 | Machine Learning @Intuit

Описание: True ML Talks #8 | Machine Learning  @Intuitin2it 

Today's TrueMLTalks, we welcome Vigith Maurice from Intuit, a global fintech platform that assists customers and communities in overcoming their most significant financial challenges.

Learn how they are solving a very niche observability and analytics problem as we talk to their Principal Engineer for the AI-Ops platform

In our conversation with Vigith, we covered the following aspects:
✅The kind of problems solved by the engineering team at Intuit
✅Example of ML use cases in terms of both customers and operations and how they differ
✅The scale of Intuit's Kubernetes clusters and the structure of the team working on them
✅The ML architecture of Intuit’s platform and the challenges faced at each layer
✅Resource management and training pipeline for the data scientists at Intuit
✅Architecture used for enabling Intuit's system to scale down to zero
✅Vigith’s take on the open-source ecosystem, particularly in relation to Argo workflows
✅Vigith’s point of view on Airflow and Argo workflows' suitability for ML workloads
✅Intuit's AI ops platform and how it differs from the serving systems used for the ML platform
✅Intuit's architecture flows from Kubernetes clusters to namespaces
✅Intuit's security and compliance measures for different platforms
✅Key differences between ML and AI operations and how they impact decision-making
✅An overview of their KubeCon presentation


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00:00 Start.
00:34 Overview of Vigith’s role at Intuit
03:13 Customer and Operations related ML use cases
04:43 The scale of Intuit’s Kubernetes clusters
07:16 Intuit’s platform architecture for ML
13:37 Intuit's resource management and training pipeline
19:51 Intuit's contributions to Argo Workflows
21:41 Differences between Airflow and Argo Workflows
29:20 ML engineer’s POV: What does the workflow look like?
35:30 How is the retraining system powered at Intuit
37:03 Differences in constraints between Flink and Intuit's platform
39:33 Intuit’s security and compliance measures for their platform
42:52 Key differences between ML and AI operations
45:09 A teaser of Vigith’s upcoming presentation at KubeCon
49:12 Top ML & Engineering resources for building platforms
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ABOUT OUR GUEST

Vigith Maurice is the principal engineer at Intuit for the AI-Ops platform. His role involves leading a team that solves the niche problem of observability and analytics. The problem is focused on reducing the mean time to detection and mean time to resolve issues within the operational systems. He plays a crucial role to make sure that the company's operational systems run smoothly and are delivering a seamless user experience to customers.

⚡Reach out to Vigith at the below link -   / vigith  


ABOUT OUR CHANNEL

TrueMLTalks is a video series 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.

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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.

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