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محتوای ارائه شده توسط Ashish Jain. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Ashish Jain یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
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Ep # 30: The Practical Approach to Embracing Autonomous 5G Networks - with Aaron Boasman-Patel, TM Forum

33:11
 
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Manage episode 354171396 series 3443788
محتوای ارائه شده توسط Ashish Jain. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Ashish Jain یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal

AI Ops is a natural fit in the 5G era with software-defined networks that are increasingly programmable. Human supervision of 5G networks is a bit like trying to hop onto an electric train after it has left the station. AI-assisted automation is the holy grail for network managers rattled by a catastrophe precipitated by human errors. While early trials of AI Ops in private and public 5G networks are promising, data capture in network labyrinths is forbidding due to many silos, emergent data standards, and insufficient data to train AI models. How are telcos and enterprise CIOs balancing the risk and benefits of autonomous networks steered by AI? Let’s find out!
Our guest for the podcast is Aaron Boasman-Patel, Vice President of AI & Customer Experience at TM Forum. He is responsible for defining and executing the strategic vision for all AI, manages the cross-ecosystem collaboration projects, and helps to set industry standards.
In our discussion today, we will uncover a few things, such as:

- What are the early lessons from experimenting with autonomous networks?

- What are the challenges of setting data standards for AI Operations and automation?

- How does AI Ops benefit private 5G deployments? and

- How do CIOs prepare for autonomous networks and avoid errors without a way to learn from past mistakes?

So, let us welcome Aaron Boasman-Patel.

Contact PrivateLTEand5G

  continue reading

43 قسمت

Artwork
iconاشتراک گذاری
 
Manage episode 354171396 series 3443788
محتوای ارائه شده توسط Ashish Jain. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Ashish Jain یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal

AI Ops is a natural fit in the 5G era with software-defined networks that are increasingly programmable. Human supervision of 5G networks is a bit like trying to hop onto an electric train after it has left the station. AI-assisted automation is the holy grail for network managers rattled by a catastrophe precipitated by human errors. While early trials of AI Ops in private and public 5G networks are promising, data capture in network labyrinths is forbidding due to many silos, emergent data standards, and insufficient data to train AI models. How are telcos and enterprise CIOs balancing the risk and benefits of autonomous networks steered by AI? Let’s find out!
Our guest for the podcast is Aaron Boasman-Patel, Vice President of AI & Customer Experience at TM Forum. He is responsible for defining and executing the strategic vision for all AI, manages the cross-ecosystem collaboration projects, and helps to set industry standards.
In our discussion today, we will uncover a few things, such as:

- What are the early lessons from experimenting with autonomous networks?

- What are the challenges of setting data standards for AI Operations and automation?

- How does AI Ops benefit private 5G deployments? and

- How do CIOs prepare for autonomous networks and avoid errors without a way to learn from past mistakes?

So, let us welcome Aaron Boasman-Patel.

Contact PrivateLTEand5G

  continue reading

43 قسمت

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