Title: "Efficient AI Modeling for Production: A Practical Guide" Keywords: #AI, #MachineLearning, #Efficiency, #Production, #Scalability, #ModelOptimization.

Join Shelby, lead researcher at Salesforce, as she explores the power of small AI models and efficient inference techniques to revolutionize the way we deploy AI for customers.

  • 1. The conference is focused on builders and techniques for getting AI into the hands of customers, with an emphasis on efficiency to bridge the gap between demos and production-ready models.
  • 2. Shelby leads the AI research team at Salesforce, which delivers AI solutions like LLMs and ships over 15 cutting-edge research papers in areas such as agents, LLMs, on-device AI, and more.
  • 3. Salesforce has deployed AI for 10 years, with over 300 AI patents and 227 research papers in the last decade.
  • 4. Trust is a key value at Salesforce, with participation in six ethical AI councils and involvement in the White House commitment to advancing AI.
  • 5. The conference discusses five dimensions of AI efficiency: software, hardware, network, people, and energy.
  • 6. Software optimization can be achieved through techniques like pruning, quantization, and knowledge distillation.
  • 7. Hardware optimization includes using specialized chips like GPUs and TPUs, as well as optimizing for memory and power consumption.
  • 8. Network optimization involves strategies like efficient routing algorithms, caching, and resource allocation.
  • 9. People optimization focuses on effective collaboration, communication, and education within AI teams.
  • 10. Energy optimization aims to reduce the carbon footprint of AI systems by improving their energy efficiency.
  • 11. The conference highlights the importance of responsible AI, addressing potential issues like bias, security, privacy, and transparency.
  • 12. Smaller language models (LLMs) with fewer parameters are emerging as viable alternatives for deployment on various platforms, including cloud, mobile, laptops, and edge devices.
  • 13. Small state-of-the-art LLMs to consider include LaMDA 53 (3.8 billion parameters), MobileBERT (350 million parameters), and Octopus (fine-tuned on Android tasks with 2 billion parameters).
  • 14. Quantization is a process that reduces the precision of weights in AI models, leading to massive efficiency gains, reduced resource consumption, and faster inference times.
  • 15. When quantizing LLMs, negligible effects on performance are typically observed.
  • 16. Llama CBP is an open-source framework for quantizing models from 16 bits to as low as 1.5 bits, with wide adoption and support for both Python and Java.
  • 17. Mobile AI Bench is an open-source repository developed by Salesforce's AI research team for evaluating quantized models before deployment.
  • 18. Responsible AI practices include ensuring trust and safety are maintained during model quantization, streamlining evaluation across various tasks, and checking hardware usage on devices.

Source: AI Engineer via YouTube

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