
Sign up to save your podcasts
Or


Mircea Neagovici is VP, AI and Research at UiPath, where his team works on task mining and other ways of combining robotic process automation (RPA) with machine learning for their B2B products.
Mircea and Lukas talk about the challenges of allowing customers to fine-tune their models, the trade-offs between traditional ML and more complex deep learning models, and how Mircea transitioned from a more traditional software engineering role to running a machine learning organization.
Show notes (transcript and links): http://wandb.me/gd-mircea-neagovici
---
⏳ Timestamps:
0:00 Intro
1:05 Robotic Process Automation (RPA)
4:20 RPA and machine learning at UiPath
8:20 Fine-tuning & PyTorch vs TensorFlow
14:50 Monitoring models in production
16:33 Task mining
22:37 Trade-offs in ML models
29:45 Transitioning from software engineering to ML
34:02 ML teams vs engineering teams
40:41 Spending more time on data
43:55 The organizational machinery behind ML models
45:57 Outro
---
Connect with Mircea:
📍 LinkedIn: https://www.linkedin.com/in/mirceaneagovici/
📍 Careers at UiPath: https://www.uipath.com/company/careers
---
💬 Host: Lukas Biewald
📹 Producers: Cayla Sharp, Angelica Pan, Sanyam Bhutani, Lavanya Shukla
By Lukas Biewald4.8
6868 ratings
Mircea Neagovici is VP, AI and Research at UiPath, where his team works on task mining and other ways of combining robotic process automation (RPA) with machine learning for their B2B products.
Mircea and Lukas talk about the challenges of allowing customers to fine-tune their models, the trade-offs between traditional ML and more complex deep learning models, and how Mircea transitioned from a more traditional software engineering role to running a machine learning organization.
Show notes (transcript and links): http://wandb.me/gd-mircea-neagovici
---
⏳ Timestamps:
0:00 Intro
1:05 Robotic Process Automation (RPA)
4:20 RPA and machine learning at UiPath
8:20 Fine-tuning & PyTorch vs TensorFlow
14:50 Monitoring models in production
16:33 Task mining
22:37 Trade-offs in ML models
29:45 Transitioning from software engineering to ML
34:02 ML teams vs engineering teams
40:41 Spending more time on data
43:55 The organizational machinery behind ML models
45:57 Outro
---
Connect with Mircea:
📍 LinkedIn: https://www.linkedin.com/in/mirceaneagovici/
📍 Careers at UiPath: https://www.uipath.com/company/careers
---
💬 Host: Lukas Biewald
📹 Producers: Cayla Sharp, Angelica Pan, Sanyam Bhutani, Lavanya Shukla

537 Listeners

1,089 Listeners

302 Listeners

334 Listeners

226 Listeners

211 Listeners

95 Listeners

511 Listeners

131 Listeners

227 Listeners

610 Listeners

33 Listeners

35 Listeners

21 Listeners

40 Listeners