Imagine venturing from the world of Biophysical Chemistry to becoming a respected leader in the realm of artificial intelligence. Dr. Ryan Ries, Chief AI and Data Scientist at Mission, has done just that. With a Ph.D. from UCLA and Caltech, his contributions to AI development and implementation over the past twenty years are notable. A glance at his portfolio reveals impressive collaborations with the U.S. Department of Defense and Fortune 500 giants. The team he spearheads at Mission is continually working towards developing complex, AWS-powered AI and data solutions.
The road to his current position was by no means a straight one. Dr. Ries attributes the explosion of AI development to what he terms as “AI renaissance”, sparked by game-changing infrastructure innovations like AWS and an increased access to scalable tech resources. In the early days, AI development was stunted by tedious coding processes and limited resources. Open-source libraries and Python did provide some relief, but the real acceleration arrived with the advent of hyperscalers like AWS.
At Mission, a strong emphasis on security and scale sets the tone for their cloud services. So infused is the concept of security in their work culture that there isn’t a standalone security team; instead, the responsibility is shared equally amongst all stakeholders. This attitude has earned the company AWS’s Security Partner of the Year title for two consecutive years. Mission makes use of AWS Bedrock for protecting sensitive data within the AWS ecosystem, including personally identifiable information (PII).
When it comes to AI scalability, Mission is adept at creating secure and powerful MLOps pipelines. Although generative AI has been associated with large-scale models like ChatGPT, Dr. Ries notes that most enterprise use cases are modest and internal. AWS Bedrock’s API layer is leveraged to support the flexibility and performance needed to address these real-world applications.
Each client engagement at Mission is unique, but the common thread is a deep dive into business objectives right at the start. This helps in identifying the workloads that need to be migrated, retired, or refactored and thus, ensuring cost efficiency and scalability during cloud migration. Especially with generative AI, Mission takes care of not just the design and pilot solutions, but also fine-tuning the prompts, addressing edge cases, and undertaking data migration for optimal outcomes.
Dr. Ries formulates an interesting connection between innovation and confidence. For him, the competence and trust ingrained in Mission’s team allow for not just bold innovation but also safeguards security and alignment with business aims. Talking about the impacts and limitations of AI, he focuses on areas where Generative AI has made a significant difference, like intelligent document processing (IDP) and chatbots, and where it falls a bit flat, for instance, in creating generative images and videos that only find application in creative and marketing industries.
With the evolution of AI, the role of AI officers in businesses has also changed. Dr. Ries argues against making symbolic titles without an actual mandate. Whether it is the Chief AI Officer, Chief Data Officer, or CTO, authority to drive a cross-functional AI strategy that spans data, infrastructure, and business outcomes is crucial.
The issue of building competent AI teams isn’t lost on him either. Genuine expertise and curiosity take precedence in high-stakes environments. Furthermore, organizations looking to move from proofs-of-concept to actual production need alignment across departments. Robust MLOps infrastructure is essential, and with generative AI, it also involves engineering, compliance, and pipeline challenges. Partnering with firms like Mission, AI-first startups can accelerate their development, secure strategic guidance, and be confident about validating their products.
Reflecting on AI’s fast-paced growth, Dr. Ries encourages startups to narrow their focus. Creating thin wrappers around ChatGPT may provide short-term results, but real success lies in identifying relevant problems, designing innovative solutions, and launching production-ready systems from the very beginning. Check the original interview hier.
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