I don’t think everyone can afford a GPU to train generative models which require tons of data and compute to even get started. "The biggest roadblock, in my opinion, is Compute. Ravi Dalal, Senior Computer Vision Engineer, Walmart This constantly evolving process also leads to skillset issues in industry since large data scientist populations have to constantly evolve at a faster pace and new entrants have to catch up a lot with new advancements." There are also many pieces of the data science puzzle which are yet to be matured like - model interpretability, model/data bias tracking, auto deployment metrics, AutoML frameworks etc. " Constantly evolving landscape of tools, algorithms and practices makes it hard for any enterprise to adopt and deliver on Machine Learning. Mis-communication between those in and those outside the technical teams of AI-led companies.Data standardization, data bias, and data silos are all problems to be solved. The need for the highest skill sets available in combination with humongous amounts of money for infrastructure purposes.Data collection without harbouring data privacy in certain countries.The lack of training data widely available.The level of ML and AI hardware in certain locations, specifically in India and China, is not high enough for the training of the next generation of developers.The levels of compute available to companies with lower budgets harbouring the levels of ML development actually available in CPU and GPU.Poor standards of education in the areas needed to develop AI skills at early school age.Obviously, COVID has played a huge part in blocking potential AI development, but pandemic aside, we wanted to know what the experts think we need to overcome for development in 2021. With the success of our previous expert-led AI roadblocks blog, we decided to reach out to a new group of experts working in the field, and ask them what they think are the biggest challenges faced in 2021.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |