Despite the hype around AI, many ML projects fail, with only 15% of businesses' ML projects succeeding, according to McKinsey. Particularly with the significant investments in large language models and generative AI, only a small portion of companies have managed to realize their true value.

Drawing from her 10 years of experience at the forefront of bringing ML to production at various companies, Wenjie will explore common pitfalls that cause these failures, such as the inherent uncertainty of machine learning, misaligned optimization objectives, and skill gaps among practitioners. She will also highlight critical factors companies must consider when shaping and developing their ML strategies and execution plans. Attendees will gain actionable insights to increase their chances of success in the ever-evolving landscape of machine learning.

Main Takeaways:

• Walk through common pitfalls in ML projects: misaligned objectives, skill gaps, and inherent uncertainty.
• Share experiences for performance monitoring, continuous skill development, and effective team collaboration.
• Understanding the important factors to help develop AI or GenAI strategies can help harness the true value of machine learning and achieve tangible business outcomes.