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FedML Nexus AI: Your Generative AI Platform at Scale // Salman Avestimehr // #230
May 7, 2024
52:33
Salman Avestimehr, CEO & Founder of FEDML, discusses FEDML Nexus AI, an enterprise AI platform enabling generative AI applications at scale. Topics include challenges in AI platform development, ownership, scalability, integrating with cloud infrastructure, evaluating language models, small scale foundation models in federated learning, and advantages of using small models for AI agents on mobile phones.
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Quick takeaways
- FEDML Nexus AI offers unique features in model deployment, serving, AI agents, training/inference on decentralized GPU cloud, and experimental tracking.
- FEDML focuses on ownership and offers smaller top-performing LLMs for easier maintenance, deployment, and cost-effective training.
- FEDML excels in federated learning with open-source library for privacy-preserving decentralized machine learning, targeting privacy-enhanced solutions.
- FedML anticipates on-device AI applications with AI agents for seamless multi-step task execution, enhancing user experience, and reducing cloud dependency costs.
- FedML provides multi-cloud deployment options for optimized AI model performance, connectivity, and scalability across different cloud providers, ensuring accessibility.
- FedML redefines evaluation metrics for vertical LLMs with focus on correctness, privacy, and response quality criteria, empowering users in informed model selection.
Deep dives
Embracing Small, Efficient Models for Ownership and Affordability
FedML is gearing up to release its own foundation model focused on smaller, high-performance LLMs, aiming to empower developers, startups, and enterprises with more accessible models. By prioritizing smaller models that offer top-tier performance, FedML aligns with the concept of ownership, making maintenance, deployment, and training more manageable and cost-effective. These models cater to the need for ownership and control, particularly for startups where cost and scalability are critical considerations.
Onboarding Federated Learning for Enhanced Privacy and Decentralized Training
FedML originally started with a focus on federated learning and continues to lead in this domain, offering an open-source library that ranks highly for privacy-preserving decentralized machine learning. Federated learning's appeal lies in training models across distributed data without data collection, a valuable approach for scenarios requiring privacy-enhanced machine learning solutions. The platform's federated learning offerings target users seeking privacy preservation and data access without centralizing sensitive information.
Pioneering On-Device AI Solutions for Streamlined Tasks
Anticipating the shift towards on-device AI applications, FedML envisions AI agents residing on mobile devices to execute multi-step tasks seamlessly. These on-device agents would streamline complex processes that span various APIs and apps, simplifying interactions for users. The focus is on enhancing user experience through task automation, providing immediate utility for content creation, image modification, and personalization without being cloud-dependent, thus reducing costs and ensuring data privacy.
Advancing Agents for Seamless User Experience and Task Automation
The integration of AI agents on mobile devices represents a significant step towards personalized assistance and efficient task management. By leveraging these agents, users can delegate multiple tasks to streamline workflow, from generating personalized ad content to editing images on-the-go. This mobile-centric approach caters to immediate user needs, offering a cost-effective and privacy-conscious alternative to cloud-based solutions, enhancing productivity and content creation capabilities.
Facilitating Ease of Access and Deployment through Multi-Cloud Capabilities
FedML's platform versatility extends to multi-cloud deployment options, allowing users to harness the power of AI models across various cloud providers. By enabling easy integration with existing infrastructure, including GKE, AWS EKS, and more, FedML ensures seamless connectivity and scalability. With a focus on optimizing model performance and deployment across clouds, FedML provides a comprehensive solution for enhanced accessibility and ease of AI integration.
Redefining Evaluation Benchmarks and Data Analysis for Model Enhancement
FedML is at the forefront of redefining evaluation metrics and benchmarks, especially for vertical LLMs, addressing the challenges of assessing model performance and credibility in industry-specific contexts. By introducing novel evaluation criteria focusing on correctness, privacy leakage, and response quality, FedML aims to enhance model assessment and selection processes. These innovative approaches empower users to make informed decisions when fine-tuning models and drive continuous improvement for personalized AI solutions.
Enabling Agile Model Deployment and A/B Testing for Iterative Development
FedML's platform streamlines the model deployment process by offering A/B testing capabilities and agile development methodologies. With features like API gateway for routing between multiple endpoints, developers can conduct comprehensive A/B tests and assess model performance under different scenarios. By facilitating iterative model development, streamlined deployment, and real-time feedback mechanisms, FedML empowers users to drive rapid innovation and optimize AI solutions for diverse use cases.
Empowering Startups with Scalable Solutions and Strong Infrastructural Support
Startups find a reliable partner in FedML, leveraging its scalable solutions and robust infrastructure to overcome deployment challenges and operational bottlenecks. By offering cost-effective, performance-driven models, FedML enables startups to scale their AI solutions without the need for extensive infrastructure teams, enhancing accessibility and affordability. The focus on deployment readiness, safety measures, and performance optimization equips startups with the essential tools to accelerate their AI development journeys.
Innovating with Maturity Levels in AI Solutions and Integration
FedML's commitment to innovation reflects in its approach towards different maturity levels in AI solutions, addressing the evolving needs of developers and enterprises. By providing tailored solutions for diverse user groups, FedML caters to individual developers, startups, and enterprises seeking to enhance their AI capabilities. The platform's emphasis on continuous improvement, fine-tuning maturity levels, and integrative development strategies showcases its adaptability and commitment to driving AI innovation.
Driving Transformation through Foundation Models and Model Federation
FedML's upcoming foundation model release heralds a new era in AI capabilities, focusing on smaller, efficient models tailored for diverse applications. By advocating for ownership, affordability, and accessibility, FedML aims to democratize AI development and deployment, ensuring that users can harness top-tier AI solutions at minimal cost. Additionally, the concept of model federation introduces innovative strategies for combining smaller models to create powerful, collective models, exploring new paradigms in model collaboration and performance optimization.
Navigating the Future of AI with Edge Computing and On-Device AI
As the AI landscape evolves, FedML foresees a shift towards edge computing and on-device AI solutions as key game-changers. By envisioning AI agents embedded in smartphones for seamless task execution and content generation, FedML anticipates a future where on-device AI becomes integral to user experiences. The focus on edge computing, federated learning, and decentralized training signifies FedML's forward-looking approach towards achieving AI transformation and enabling user-centric AI interactions.
Enhancing User Experience with AI Automation and Content Creation Capabilities
FedML's vision for advancing AI agents on mobile devices aligns with the growing demand for simplified, on-device AI solutions that enhance productivity and user interactions. By streamlining complex tasks, enabling task automation, and facilitating content creation on-the-go, FedML empowers users to leverage AI capabilities seamlessly. The integration of AI agents on smartphones signifies a shift towards personalized, efficient AI assistance, reshaping how users engage with AI technologies in everyday tasks.
Maximizing AI Potential through Multi-Cloud Integration and Seamless Connectivity
FedML's platform versatility extends to multi-cloud capabilities, allowing users to deploy AI models across diverse cloud infrastructures with ease. By enabling seamless integration with various cloud providers, FedML ensures optimized performance, scalability, and connectivity. The platform's focus on multi-cloud deployment options enhances accessibility, ensuring that users can leverage AI solutions across different cloud environments efficiently and effectively.
Revolutionizing Model Evaluation Metrics for Enhanced Model Performance
FedML leads the charge in redefining model evaluation benchmarks, focusing on industry-specific vertical LLMs to enhance performance assessment and optimization. By introducing novel evaluation criteria centered on correctness, privacy considerations, and model credibility, FedML empowers users to assess and refine AI models effectively. The platform's innovative approach to model evaluation enables users to make informed decisions, drive continuous improvement, and optimize AI solutions tailored to specific industry requirements.
Streamlining Model Deployment and A/B Testing for Agile Development
FedML's platform offers comprehensive model deployment solutions and A/B testing capabilities to facilitate iterative model development and performance assessment. By providing an API gateway for seamless routing between endpoints, developers can conduct thorough A/B tests, assess model performance, and optimize AI solutions based on real-time feedback. FedML empowers users to drive rapid innovation, streamline deployment processes, and enhance AI solutions through agile development methodologies and continuous experimentation.
Empowering Startups with Scalable AI Solutions and Infrastructure Support
FedML's scalable AI solutions and robust infrastructure support provide startups with essential tools to overcome deployment challenges and accelerate AI development. By offering cost-effective models, performance-driven solutions, and deployment readiness features, FedML enables startups to scale their AI solutions without extensive infrastructure requirements. The focus on optimization, scalability, and safety measures equips startups with the necessary resources to enhance their AI capabilities, driving growth and innovation in the industry.
Navigating Maturity Levels in AI Development for Diverse User Needs
FedML's innovative approach towards different maturity levels in AI solutions caters to the evolving needs of developers, startups, and enterprises. By providing tailored solutions and integration strategies, FedML empowers users to enhance their AI capabilities and drive continuous improvement. The platform's commitment to fine-tuning maturity levels, seamless deployment, and integrative development showcases its adaptability and dedication to advancing AI innovation for diverse user groups.
Driving Transformation through Foundation Models and Collaborative Model Federation
FedML's upcoming foundation model release heralds a new era in AI solutions, focusing on smaller, efficient models optimized for enhanced performance and affordability. By democratizing AI development, FedML empowers users to access high-performing AI solutions at minimal cost, driving innovation and accessibility in the AI landscape. Additionally, the concept of model federation introduces innovative strategies for combining smaller models to create powerful collective models, exploring new possibilities in model collaboration and performance optimization.
2.
Exploring the Maturity Levels and Pain Points of Customers in AI Platform Development
2min
6.
Empowering Ownership Through Small Scale Foundation Models in Federated Learning
5min
Salman Avestimehr is a Dean's Professor, the inaugural director of the USC-Amazon Center for Secure and Trusted Machine Learning (Trusted AI), and director of the Information Theory and Machine Learning (vITAL) research lab. He is also the CEO and co-founder of FedML. MLOps podcast #230 with Salman Avestimehr, CEO & Founder of FedML, FedML Nexus AI: Your Generative AI Platform at Scale.
A big thank you to FEDML for sponsoring this episode! // Abstract FedML is your generative AI platform at scale to enable developers and enterprises to build and commercialize their own generative AI applications easily, scalably, and economically. Its flagship product, FedML Nexus AI, provides unique features in enterprise AI platforms, model deployment, model serving, AI agent APIs, launching training/Inference jobs on serverless/decentralized GPU cloud, experimental tracking for distributed training, federated learning, security, and privacy. // Bio Salman is a professor, the inaugural director of the USC-Amazon Center for Secure and Trusted Machine Learning (Trusted AI), and the director of the Information Theory and Machine Learning (vITAL) research lab at the Electrical and Computer Engineering Department and Computer Science Department of the University of Southern California. Salman is also the co-founder and CEO of FedML. He received his Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley in 2008. Salman does research in the areas of information theory, decentralized and federated machine learning, secure and privacy-preserving learning, and computing. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links https://www.avestimehr.com/ https://fedml.ai/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Salman on LinkedIn: https://www.linkedin.com/company/fedml/ Timestamps: [00:00] AI Quality: First in-person conference on June 25 [01:28] Salman's preferred coffee [01:49] Takeaways [03:33] Please like, share, leave a review, and subscribe to our MLOps channels! [03:53] Challenges that inspired Salman's work [06:20] Controlled ownership [08:11] Dealing with data leakage and privacy problems [10:45] In-house ML Model Deployment [13:36] FEDML: Comprehensive Model Deployment [17:27] Integrating FEDML with Kubernetes [19:46] AI Evaluation Trends [24:37] Enhancing NLP with ML [25:48] FEDML: Canary, A/B, Confidence [29:36] FEDML customers [33:21] On-premise platform for secure data management
[37:16] Future prediction: data's crucial for better applications
[38:18] Maturity in evaluating and improving steps
[41:38] Focus on ownership
[45:12] Benefits of smaller models for specific use cases
[48:57] Verify sensitive tasks, trust quick, important mobile content creation
[51:50] Wrap up
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