The future of Artificial Intelligence for the BioTech Big Data landscape - Artico, Fausto; Edge III, Arthur L; Langham, Kyle (Current opinion in biotechnology, 2022)
Recent Industry 4.0 advancements are making available massive amounts of data for the development of innovative BioTech solutions. However, several challenges need to be overcome to correctly use data and novel, non-pharma technologies to greatly speed up discovery, optimization and market delivery of products, and services. In this review, we bring your attention to the important aspects of Big Data and Artificial Intelligence (AI) that have an impact on the future of the field and briefly touch upon how disciplines such as Hyper-Automation, Infrastructure as Code (IaC) and DevOps — a set of practices that combines software development with Information Technology (IT) operations (Ops) — can accelerate Big Data and AI adoption in your Agile Digital Transformation journey. [Display omitted] •Important phases to execute in BioTech Big Data projects to prepare data for AI/ML work.•Brief description and advice on the two main types of data structures to use in BioTech.•Important aspects to consider when you want to use Artificial Intelligence in BioTech.•Technologies that can further accelerate value creation in BioTech with Big Data and AI.•Dynamics and benefits generated by adding Hyper-Automation, DevOps, and Agile methods.