Artificial intelligence operates within a structured two-step framework, commencing with AI training followed by the pivotal second phase, AI inference. This sequential process, fundamental to contemporary machine learning and deep learning paradigms, underpins the adaptability of AI, allowing it to proficiently address a spectrum of tasks ranging from content creation to guiding autonomous vehicles.
A pre-trained AI model encounters novel, unlabeled data during the intricate inference process, leveraging the insights acquired during its training phase. This interaction draws upon the meticulously cultivated database to analyze the fresh input, producing precise and contextually relevant outputs. When ChatGPT responds to queries or Stable Diffusion generates imagery upon request, these instances exemplify the AI model actively engaged in inferencing. The human-like quality of these responses underscores the thorough training undergone by the model.
However, the narrative extends beyond mere inference. Simultaneously immersed in inferencing, the AI is a discerning observer, systematically cataloging user feedback for subsequent training iterations. It meticulously captures instances of commendation and critique, establishing a perpetual loop of training and inference that propels AI towards heightened realism. This nuanced process constitutes the focal point of any forthcoming exploration into the domain of AI inference within the NVIDIA-hosted webinar series. Understand and discover the intricacies and potentials inherent in this dynamic second phase of the AI journey.
The primary objective behind AI model training is to enable them to engage in inference—effectively interacting with novel data in real-world scenarios to enhance human productivity and comfort. The spectrum of capabilities demonstrated by advanced AI products is extensive, encompassing tasks such as interpreting human handwriting, facial recognition, autonomous vehicle navigation, and content generation—all of which exemplify the application of AI inference. When encountering terminologies such as computer vision, natural language processing (NLP), or recommendation systems, one essentially refers to distinct manifestations of AI inference.
NVIDIA’s Webinar Series
NVIDIA emerges as a beacon of knowledge with webinars that promise to be more than just an exploration of artificial intelligence; they are a journey into the heart of AI inference. Mark your calendars for December 5, 7, and 14, 2023, as NVIDIA invites AI inference platform is a fusion of cutting-edge hardware and sophisticated software.
This free digital series is not merely a showcase of NVIDIA’s capabilities; it’s a thought-provoking gateway into the dynamic realm of AI inference. Designed to be an immersive experience, the series aims to equip participants with a profound understanding of how AI inference functions as the bedrock of modern artificial intelligence.
Varied Dimensions of AI Inference
December 5, 10:00–11:00 a.m. PT: Move Enterprise AI Use Cases From Development to Production With Full-Stack AI Inferencing
Embark on a journey led by industry experts Amr Elmeleegy and Phoebe Lee. Tailored for AI executives and team leaders across all industries, this session delves into deploying enterprise AI use cases and trained models, bridging the gap from development to production.
December 7, 10:00–11:00 a.m. PT: Harness the Power of Cloud-Ready AI Inference Solutions for Large Language Models
Amr Elmeleegy and Neal Vaidya as they navigate the intricacies of building and deploying cloud-ready AI-inferencing solutions. This session, designed for AI practitioners and infrastructure professionals, explores the seamless integration of the NVIDIA AI inference platform with leading cloud service providers.
December 14, 10:00–11:00 a.m. PT: Accelerate AI Model Inference at Scale for Financial Services
In the final installment, Shankar Chandrasekaran and Pahal Patangia take a technical deep dive into NVIDIA AI inference software’s benefits, specifically tailored for the financial services sector. This session is ideal for AI practitioners, infrastructure specialists, and team leaders in the financial services industry.
Featured Speakers for the Event
The series showcases a distinguished lineup of seasoned experts from NVIDIA:
- Amr Elmeleegy, Principal Product Marketing Manager
- Phoebe Lee, Product Marketing Manager
- Neal Vaidya, Technical Product Marketing Manager
- Pahal Patangia, Global Developer Relations Lead for Consumer Finance
- Shankar Chandrasekaran, Senior Product Marketing Manager
These industry leaders bring a wealth of insights, experiences, and expertise, offering a comprehensive understanding of the pivotal role played by AI inference in shaping the future of technology.
AI inference, a pervasive technological force, holds considerable power. Its applications range from diagnosing diseases in hospitals and detecting flaws on factory production lines to monitoring coral reef degradation and calculating space-based weather predictions. With this technology, participants in the exclusive webinar series explore the domain of AI inference. Whether an individual is an AI executive, practitioner, or involved in infrastructure management, the series serves as a gateway into the transformative power encapsulated within NVIDIA’s AI inference platform.
- What is the significance of the two-step framework in artificial intelligence?The two-step framework in artificial intelligence begins with AI training and is followed by the pivotal second phase, AI inference. This sequential process is fundamental to contemporary machine learning and deep learning paradigms, enabling AI to adapt and proficiently address tasks ranging from content creation to guiding autonomous vehicles.
- How does AI inference operate?AI inference involves the AI model interacting with novel, unlabeled data, leveraging insights acquired during training to produce precise outputs.
- What is the relationship between AI training and user feedback?While immersed in inferencing, AI is a discerning observer, systematically cataloging user feedback for subsequent training iterations. This meticulous process establishes a perpetual loop of training and inference, propelling AI towards heightened realism by capturing instances of commendation and critique.
- What are the varied dimensions of AI inference?AI inference showcases various capabilities, including interpreting human handwriting, facial recognition, autonomous vehicle navigation, and content generation.
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