10 Pitfalls in Building an AI/ML Startup: Best Practices to Build and Scale Your AI Business
Ali Arsanjani was CTO for Analytics and Machine Learning and a Distinguished Engineer at IBM for years. He was most recently Founder and CTO of DeepContext, an AI startup until last year when he joined AWS. He now leads the TechSector ML Specialist Solution Architecture Team.
As startups attempt to scale from 0 to 100 mph in a matter of months, you will need to overcome 10 key AI/ML pitfalls. In this session, we will discuss these pitfalls and show how to overcome each of them using the capabilities and services provided by the AWS platform, as we go through the stages and phases of the ML lifecycle.
Join top AI/ML leaders at Amazon as we outline strategies for successfully deploying AI/ML workloads on AWS. AI and ML Startups must navigate through turbulent waters as they deliver value in short time frames to multiple stakeholders. During this session, we will share common mistakes early-stage AI/ML startups make and tactical advice for managing large datasets, accelerating training times, curating training data, and architecting a scalable infrastructure. You'll also hear real-world stories from AWS customers about their experience using AWS, and lessons learned.
- Utilize strategies to successfully deploy AI/ML workloads
- Learn common mistakes early-stage AI/ML startups make.
- Avoid said mistakes and implement tactical advice for managing large datasets.