Gary Drenik is a seasoned writer for Forbes, focusing on innovation in both large enterprises and small businesses through AI applications for consumer data. Since 2012, he has provided insights into innovative privacy-compliant predictive analytics and targeted marketing solutions.
There was a time, not so long ago, when it seemed like language models would only continue to grow larger. But today, organizations are recognizing the value of language models that are smaller, transparent, built-to-purpose — and capable of running on a developer’s workstation. Not coincidentally, developer workstations are becoming more powerful, and new tools are emerging that make AI more accessible to more developers.
Of course, what happens on the workstation can’t stay on the workstation. Organizations will also need to plan for moving applications from prototyping to production and beyond across the hybrid cloud using technologies like containers and Kubernetes.The use of open source-licensed models, development tools and platforms is an important consideration when it comes to local development of AI-enabled applications.
Further, the ability to freely and economically test and iterate AI-enabled applications on local workstations, combined with the transparency and accessibility of open systems, will help organizations position themselves for alignment with current and future regulations and directives. For example, the
Post-Pandemic Consumer Machine Learning Workstations AI Development Platforms Language Models Intelligent Applications Technology Compliance
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