Artificial intelligence (AI) has many different and growing
branches, such as machine learning, deep learning, and computer
vision. Given the complexity and fast-growing nature of ML
technologies, plan to hire specialists with the understanding that
additional training will be needed as ML evolves. Keep teams
learning new skills, engaged, and motivated while encouraging
accountability and empowerment at all times. Building ML models is
a complex and iterative process that can infuse bias or unfair
predictions against a certain entity. It’s important to promote
and enforce the ethical use of AI across enterprises. AWS provides
clear guidance to customers for
responsible
AI practices
Implementation plan
Develop skills - A key element in any organization’s strategy for employee engagement and business growth must be ongoing learning and development. Consider strategies to grow your ML-driven business outcomes through intentional workforce skills development. Building a successful ML workforce includes providing training on ML concepts and algorithms, end-to-end ML lifecycle processes (such as model training, tuning, and deployment on Amazon SageMaker AI), and efficient use of ML infrastructure with SageMaker AI and automation with MLOps tools, such as SageMaker AI Pipelines. Training people in different specialty areas of ML, such as computer vision, NLP, and reinforcement learning based on your business needs, can increase productivity.
-
Develop accountability and empowerment -AI applied through ML transforms the way business is run by tackling some of humanity’s most challenging problems, augmenting human performance, and maximizing productivity. Promoting responsible use of these technologies is key to fostering continued innovation. Eliminating bias in datasets and model predictions by using Amazon SageMaker AI Clarify can help you build fair and explainable models.
Documents
Blogs
