Unpacking Parallelism: Practical Strategies for Scaling AI Workflows
Webinar Details TopicUnpacking Parallelism: Practical Strategies for Scaling AI WorkflowsSpeakerShashank Kapadia (Staff ML Engineer at Walmart Global Tech)DateFebruary 25, 2025Webinar LinkRegister NowOrganizerADaSci The increasing complexity of AI models and datasets has made parallelism an essential technique for optimising performance and scalability. The webinar ‘Unpacking Parallelism: Practical Strategies for Scaling AI Workflows’, hosted by ADaSci and delivered by Shashank Kapadia, staff machine learning engineer at Walmart Global Tech, provides an in-depth exploration of how to implement and leverage parallelism effectively. This 1.5-hour session will equip participants with practical knowledge to enhance AI workflows using distributed training, cloud infrastructure, and advanced computational strategies. What Will It Cover? The webinar is structured to provide a clear and actionable understanding of parallelism in AI. The key topics include: Introduction to Parallelism in AI Workflows — Understanding the role of parallelism in AI model training and inference; benefits of breaking tasks into concurrent operations for improved efficiency. Challenges in Scaling AI Workflows — Identifying common bottlenecks in large-scale AI applications; addressing memory constraints, communication overhead, and computational load. Key Strategies for Implementing Parallelism in AI Systems — Effective methods to distribute workloads across multiple processing units; techniques to optimise system performance through parallel execution. Distributed Training: Techniques and Tools — Utilising distributed frameworks to accelerate model training; best practices for balancing workloads and minimising inefficiencies. Scaling AI Workflows with Cloud Computing and GPUs — Leveraging cloud infrastructure to access scalable resources on demand; using GPU acceleration to enhance deep-learning performance. Real-World Case Studies and Applications — Examining industry use cases where parallelism has significantly improved AI systems; insights into how leading organisations optimise their AI workflows. Book your spot What Will You Gain? By attending this webinar, participants will acquire: A deep understanding of parallelism and its role in AI scalability. Practical strategies to implement distributed training and parallel computing techniques. Knowledge of how to integrate cloud-based solutions and GPU acceleration for AI workloads. Real-world insights from case studies demonstrating the impact of parallelism. Why You Must Attend This webinar is ideal for machine learning engineers, data scientists, AI researchers, and technology leaders looking to enhance their AI systems. Scaling AI workflows efficiently is a key challenge in modern data science, and mastering parallelism can provide a competitive advantage. Additionally, with an industry expert like Shashank Kapadia leading the session, attendees will gain first-hand insights from someone who has successfully implemented these techniques in large-scale AI solutions. Whether you are working on model training, inference optimisation, or AI infrastructure, this webinar will provide valuable strategies to enhance your approach. Final Words ‘Unpacking Parallelism: Practical Strategies for Scaling AI Workflows’ is a must-attend event for professionals looking to advance their AI expertise. By the end of the session, participants will be well-equipped with the knowledge and tools needed to scale their AI systems efficiently. Book your spot Register now to secure your spot and stay ahead in the rapidly evolving field of AI development.
Unpacking Parallelism: Practical Strategies for Scaling AI Workflows webinar is hosted by ADaSci and delivered by Shashank Kapadia on Feb 25, 2025 at 7:30 PM IST.
Unpacking Parallelism: Practical Strategies for Scaling AI Workflows webinar is hosted by ADaSci and delivered by Shashank Kapadia on Feb 25, 2025 at 7:30 PM IST.
["AI engineers", "data engineering career"]
["data science", "API", "machine learning", "AI", "cloud computing", "AI engineers", "ML", "data engineering career", "Scala", "RAG", "GAN", "R"]
["AI", "machine learning", "ML", "data science", "RAG", "cloud computing", "R", "Scala", "API", "GAN"]
Dr. Vaibhav Kumar is a seasoned data science professional with great exposure to machine learning and deep learning. He has good exposure to research, where he has published several research papers in reputed international journals and presented papers at reputed international conferences. He has worked across industry and academia and has led many research and development projects in AI and machine learning. Along with his current role, he has also been associated with many reputed research labs and universities where he contributes as visiting researcher and professor.