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#360 Bridging AI Development and Operations: Brad Micklea MLOps and Enterprise Strategy

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Contenuto fornito da Mehmet Gonullu. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Mehmet Gonullu o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.

In this episode of “The CTO Show with Mehmet,” we are joined by Brad Micklea, the CEO of Jozu. Brad shares his extensive experience in the tech industry, detailing his journey from leading the Amazon API Gateway to founding Jozu. Jozu is building what they believe is the first control plane for AI projects inside enterprises, aiming to help organizations accelerate their AI development and protect their operations.

Brad discusses the current challenges enterprises face in integrating AI projects into production. He emphasizes the importance of having the right strategy and organizational structure to execute AI initiatives effectively. Brad shares his insights on the need for a solid connection between AI teams and product teams, ensuring that data scientists are embedded within the product teams to better understand and address customer needs.

One of the core topics explored in this episode is KitOps, Jozu’s open-source project that aims to simplify the management of AI models and their deployment in production. Brad explains how KitOps provides a central registry for all AI project components, ensuring that organizations can manage dependencies and control versions effectively. This approach helps enterprises maintain control over their AI models, ensuring that sensitive data is protected and that AI-driven differentiation is retained within the organization.

Brad also highlights the similarities and differences between MLOps and DevOps, illustrating how KitOps bridges the gap between these two disciplines. He explains the importance of using existing tools while centralizing AI project artifacts to streamline deployment and management processes. This integration allows enterprises to maintain a high level of control and security over their AI operations.

Throughout the conversation, Brad offers valuable advice for tech founders and executives looking to integrate AI into their organizations. He emphasizes the importance of listening to customers and iterating on AI initiatives based on real-world usage and feedback. Brad shares his optimistic view of AI’s potential to unleash human creativity and transform various industries, drawing parallels to past technological disruptions like the internet and mobile computing.

More about Brad:

Brad is the Founder & CEO of Jozu and a project lead for the open source Kitops.ml project, a toolset designed to increase the speed and safety of building, testing, and managing AI/ML models in production. This is Brad’s second startup, his first (Codenvy, the market’s first container-based developer environment) was sold to Red Hat in 2017. In his 25 year career in the developer tools and DevOps software market, he’s been the GM for Amazon’s API Gateway, and built open- and closed-source products that have been leaders in Gartner Magic Quadrants. In his free time he enjoys cycling, reading, and vintage cars.

https://jozu.com/

https://www.linkedin.com/in/bradmicklea/

01:08 Brad Micklea's Background and Experience

02:41 Challenges in AI Implementation

04:16 Strategies for Effective AI Integration

04:54 The Role of Documentation in AI Strategy

05:48 Organizational Structure for AI Success

07:06 Moving AI Projects into Production

09:05 Managing AI Models in Production

10:22 Introduction to KitOps

12:27 Centralizing AI Project Components

20:37 KitOps and Enterprise Integration

21:37 Measuring AI Integration Success

25:45 Balancing Public and Private AI Models

27:12 Addressing the Skills Gap in AI

29:13 The Role of Open Source Models

30:41 Specialized AI Models: A Case Study

34:50 The Future of AI and Human Creativity

38:51 Advice for AI Startups

45:12 Building and Testing AI Prototypes

46:10 Conclusion and Final Thoughts

  continue reading

384 episodi

Artwork
iconCondividi
 
Manage episode 428518825 series 3506362
Contenuto fornito da Mehmet Gonullu. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Mehmet Gonullu o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.

In this episode of “The CTO Show with Mehmet,” we are joined by Brad Micklea, the CEO of Jozu. Brad shares his extensive experience in the tech industry, detailing his journey from leading the Amazon API Gateway to founding Jozu. Jozu is building what they believe is the first control plane for AI projects inside enterprises, aiming to help organizations accelerate their AI development and protect their operations.

Brad discusses the current challenges enterprises face in integrating AI projects into production. He emphasizes the importance of having the right strategy and organizational structure to execute AI initiatives effectively. Brad shares his insights on the need for a solid connection between AI teams and product teams, ensuring that data scientists are embedded within the product teams to better understand and address customer needs.

One of the core topics explored in this episode is KitOps, Jozu’s open-source project that aims to simplify the management of AI models and their deployment in production. Brad explains how KitOps provides a central registry for all AI project components, ensuring that organizations can manage dependencies and control versions effectively. This approach helps enterprises maintain control over their AI models, ensuring that sensitive data is protected and that AI-driven differentiation is retained within the organization.

Brad also highlights the similarities and differences between MLOps and DevOps, illustrating how KitOps bridges the gap between these two disciplines. He explains the importance of using existing tools while centralizing AI project artifacts to streamline deployment and management processes. This integration allows enterprises to maintain a high level of control and security over their AI operations.

Throughout the conversation, Brad offers valuable advice for tech founders and executives looking to integrate AI into their organizations. He emphasizes the importance of listening to customers and iterating on AI initiatives based on real-world usage and feedback. Brad shares his optimistic view of AI’s potential to unleash human creativity and transform various industries, drawing parallels to past technological disruptions like the internet and mobile computing.

More about Brad:

Brad is the Founder & CEO of Jozu and a project lead for the open source Kitops.ml project, a toolset designed to increase the speed and safety of building, testing, and managing AI/ML models in production. This is Brad’s second startup, his first (Codenvy, the market’s first container-based developer environment) was sold to Red Hat in 2017. In his 25 year career in the developer tools and DevOps software market, he’s been the GM for Amazon’s API Gateway, and built open- and closed-source products that have been leaders in Gartner Magic Quadrants. In his free time he enjoys cycling, reading, and vintage cars.

https://jozu.com/

https://www.linkedin.com/in/bradmicklea/

01:08 Brad Micklea's Background and Experience

02:41 Challenges in AI Implementation

04:16 Strategies for Effective AI Integration

04:54 The Role of Documentation in AI Strategy

05:48 Organizational Structure for AI Success

07:06 Moving AI Projects into Production

09:05 Managing AI Models in Production

10:22 Introduction to KitOps

12:27 Centralizing AI Project Components

20:37 KitOps and Enterprise Integration

21:37 Measuring AI Integration Success

25:45 Balancing Public and Private AI Models

27:12 Addressing the Skills Gap in AI

29:13 The Role of Open Source Models

30:41 Specialized AI Models: A Case Study

34:50 The Future of AI and Human Creativity

38:51 Advice for AI Startups

45:12 Building and Testing AI Prototypes

46:10 Conclusion and Final Thoughts

  continue reading

384 episodi

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