ai
Making artificial intelligence practical, productive & accessible to everyone. Practical AI is a show in which technology professionals, business people, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, GANs, MLOps, AIOps, LLMs & more). The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you!
CoreWeave is adopting an AI-centric approach to infrastructure, drawing parallels to the early days of cloud computing. Cory Sanders, Senior Vice President of Product at CoreWeave, explained that this involves building services with AI as a core component, rather than an add-on, to avoid hindering AI development.
Cory Sanders highlighted that CoreWeave focuses on optimizing AI training, a computationally intensive process requiring specialized infrastructure. The company identifies and addresses bottlenecks in this workflow, such as GPU failures or storage loading inefficiencies, offering unique services like an observability platform and a storage platform tailored for large-scale AI training workloads.
Cory Sanders discussed the challenge of adapting to new technological waves, referring to it as the 'innovator's dilemma.' He emphasized the importance of hiring people who can challenge existing assumptions and question the status quo. This mindset is crucial for evolving and building the next iteration of technology, especially in the rapidly changing AI landscape.
Hamza Tahir, co-founder of ZenML, discusses how core MLOps principles are being rediscovered and adapted for the emerging field of AI agents. He highlights that the fundamental concepts of writing reliable, safe, and retryable code are just as relevant for agents as they were for traditional machine learning pipelines.
Hamza Tahir explains that all software, including AI agents, can be viewed as a graph of execution steps. While traditional ML pipelines were more deterministic, agents involve dynamic, potentially cyclical workflows. ZenML's evolution to a dynamic mode addresses this by allowing graphs to be defined in real-time.
Hamza Tahir describes how AI "harnesses" or frameworks, like Claude Code, become deeply coupled with the specific large language models they are designed for. This coupling, driven by reinforcement learning and specialized tool use, means that models become 'self-aware' of the harness they are running within, leading to more accurate performance.
Hamza Tahir identifies a key industry tension between proprietary AI agent frameworks, like Claude Code, which are tightly coupled with specific models, and open frameworks like LangGraph or Pythontic AI. The latter aim for open standards, seeking to avoid vendor lock-in and reduce dependency on specific model providers.
Hamza Tahir points out that large language models are primarily designed for text processing, not direct real-world interaction. He notes that agents act as a bridge, but this requires significant scaffolding to handle issues like the LLM making incorrect tool calls or responding in unexpected formats, highlighting the need for robustness and error handling.