Summary Bullets:

• In mid-May, AWS highlighted its portfolio of AI tools and solutions during its AWS Summit Online for the Americas region and announced the general availability of Amazon Kendra for enterprises.
• Tools that support AI model development and management and pre-built solutions that can be easily deployed by developers who aren’t AI experts help streamline AI adoption.

AWS understands the challenges enterprises face when building their own machine learning models. The company notes that when scaling AI adoption, enterprises face wide-ranging complexities that can start as early as the data collection stage and continue throughout the model management lifecycle. At the beginning of a project, organizations face challenges related to data identification, storage, and curation as they pull together disparate data sources. Later, while building and training models, they need to manage numerous other complexities, such as sharing notebooks and pre-trained models. They need to ensure effective collaboration among what can be a growing number of individuals or teams, each with their own specializations. And, since machine learning models aren’t usually perfect the first time, team members need to communicate during the process of model tuning and optimization. They need to manage multiple versions of models, run experimental models in real time, and compare results. Even after deployment, machine learning algorithms need to be managed and monitored for concerns such as data drift, with newer versions deployed as additional data is collected or the factors that impact model results change. Managing these tasks can be challenging, and as AWS rightly points out, tools that help manage the complexities do much to streamline and speed AI deployments. Continue reading “AWS Aims to Make AI More Accessible for Both AI Specialists and Non-AI Experts” →