Published Jan 17, 2025

Efficient Deployment of Models at the Edge // Krishna Sridhar // #284

Krishna Sridhar of Qualcomm delves into the efficient deployment of AI models at the edge, balancing hardware flexibility with performance, and exploring transformative real-time applications in security and creativity. Discover how Qualcomm's AI Hub is revolutionizing edge AI deployment, enhancing developer efficiency and accelerating innovation across devices.

Episode Highlights

Topics covered

Questions from this episode

- What are the latest breakthroughs in AI?
  Asked by 21 people
- What role can AI play in various fields?
  Asked by 20 people
- What advancements can we expect in AI in the future?
  Asked by 13 people
- What is the current sentiment around the maturation of AI technology?
  Asked by 12 people
- How will AI change software?
  Asked by 12 people
- What are the current trends in artificial intelligence?
  Asked by 9 people
- How does hardware production impact artificial intelligence?
  Asked by 8 people
- What's next in large language models (LLMs)?
  Asked by 7 people
- What are some applications of language models?
  Asked by 7 people
- What are the most recent developments and trends in AI, large language models (LLMs), retrieval-augmented generation (RAG), etc., as discussed in the episode #159 - Inflection-2.5, Devin, OpenAI board update, SIMA, EU AI Act passed and the clip Cohere's Command R?
  Asked by 5 people
- What is the current news around Generative AI?
  Asked by 5 people
- What are the challenges of deploying AI as discussed in the episode Social AI with Hugging Face and the clip AI Infrastructure Challenges?
  Asked by 5 people
- What are the next advancements in large language models (LLMs)?
  Asked by 4 people
- How has AI thinking been revolutionized?
  Asked by 4 people
- Is AI advancing too quickly?
  Asked by 4 people

Episode Highlights

Related Episodes

The Challenges of Deploying (many!) ML Models // Jason McCampbell // MLOps Podcast #149
Answers 383 questions
Scaling AI in production // Srivatsan Srinivasan // MLOps Coffee Sessions #40
Answers 383 questions
Extending AI: From Industry to Innovation // Sophia Rowland & David Weik // #246
Answers 383 questions
AI's Next Frontier // Aditya Naganath // #277
Answers 383 questions
How to Build Production-Ready AI Models for Manufacturing // [Exclusive] LatticeFlow Roundtable
Answers 383 questions
AI Operations Without Fundamental Engineering Discipline // Nikhil Suresh // #250
Answers 383 questions
Deploying Machine Learning Models at Scale in Cloud // Vishnu Prathish // MLOps Meetup #60
Answers 383 questions
MLOps for GenAI Applications // Harcharan Kabbay // #256
Answers 383 questions
AI Agents for Consumers // Shaun Wei // #244
Answers 383 questions
A BLUEPRINT FOR SCALABLE & RELIABLE ENTERPRISE AI/ML SYSTEMS // PANEL // AIQCON
Answers 383 questions
Retrieval Augmented Generation
Answers 383 questions
Pioneering AI Models for Regional Languages // Aleksa Gordić // #203
Answers 383 questions
Machine Learning, AI Agents, and Autonomy // Egor Kraev // #282
Answers 383 questions
Exploring the Impact of Agentic Workflows // Raj Rikhy // #268
Answers 383 questions
Meta GenAI Infra Blog Review // Special MLOps Podcast
Answers 383 questions

Efficient Deployment of Models at the Edge // Krishna Sridhar // #284

Topics covered

Popular Clips

Questions from this episode

Episode Highlights

Hardware Optimization

Innovative ApplicationsNext, Krishna Sridhar explores the innovative applications of AI in real-time tracking, security, and creative fields. He highlights how AI is transforming everyday technology, making advanced capabilities accessible and efficient.

Innovative Applications

Edge AI Deployment

Related Episodes