Learning from AI leaders successes, failures, and lessons learned in productionalizing machine learning is essential in this increasingly AI-driven world. The problem is that this knowledge is often available across disparate sources or not at all. The mlcon 2.0 was started to break down these silos and have brought top speakers from leading companies like DeepMind, Spotfiy, Hugging Face, Disney, Twitter, Intel, and Dell Technologies to speak about their lessons learned, pro tips and proven strategies for building real world AI applications. Below are some topics that AI professionals can learn from this upcoming conference.
Building a Hardware + Software Machine Learning (ML) Strategy
It is rare to hear from a CTO of a large corporation like Intel talk about their ML strategy. Greg Lavendar will be doing a fireside chat about his solutions to the major problems holding back AI maturity and the latest strategies organizations can take to succeed in AI. This includes how to approach security for AI, and the key to building a hardware and software end-to-end strategy for a complete ML system.
Optimizing your System Architecture for AI workloads
The use of AI techniques to solve real life problems has been growing rapidly. The number of technologies to handle these AI workloads has also seen an exponential growth both in terms of variety of hardware and software available. This growth in use cases and available technology options brings complexity to system designs targeted towards AI workloads. When designing the on-prem IT infrastructure to run AI workloads, it is critical to understand the impact of applications on the compute, storage and networking subsystems and utilize the right technologies for the target workload. Onur Celebioglu, Sr. Director of Engineering at Dell Technologies will describe how they approach system design optimized for AI infrastructure at Dell. Through the use of specific project examples from Dell’s CTIO and AI Innovations labs, listeners will get an understanding of how hardware, orchestration software, MLOps tools and applications come together to form an integrated system.
Solving Complex Problems with Low-Code Machine Learning
Today’s reality is that data scientists are spending 80 percent of their time on non-data science tasks. This along with a shortage of experienced data scientists makes solving complex problems with sophisticated ML algorithms a challenge in many organizations. A way to bridge this gap is to use a low-code machine learning platform, which Orly Amsalem will speak about, are a tool that developers need in their toolbox and that every leader needs to be aware of.