Dr. Ryan Ries is a famend information scientist with greater than 15 years of management revel in in information and engineering at fast-scaling generation corporations. Dr. Ries holds over two decades of revel in operating with AI and 5+ years serving to shoppers construct their AWS information infrastructure and AI fashions. After incomes his Ph.D. in Biophysical Chemistry at UCLA and Caltech, Dr. Ries has helped increase state-of-the-art information answers for the U.S. Division of Protection and a myriad of Fortune 500 corporations.
As Leader AI and Information Scientist for Undertaking, Ryan has constructed out a a hit workforce of Information Engineers, Information Architects, ML Engineers and Information Scientists to unravel one of the vital toughest issues on the earth using AWS infrastructure.
Mission is a number one controlled products and services and consulting supplier born within the cloud, providing end-to-end cloud products and services, cutting edge AI answers, and instrument for AWS shoppers. As an AWS Premier Tier Spouse, the corporate is helping companies optimize generation investments, fortify efficiency and governance, scale successfully, safe information, and include innovation with self belief.
You’ve had an outstanding adventure—from constructing AR {hardware} at DAQRI to changing into Leader AI Officer at Undertaking. What private stories or turning issues maximum formed your point of view on AI’s function within the endeavor?
Early AI building was once closely restricted via computing energy and infrastructure demanding situations. We continuously needed to hand-code fashions from analysis papers, which was once time-consuming and complicated. A significant shift got here with the upward thrust of Python and open-source AI libraries, making experimentation and model-building a lot sooner. Then again, the largest turning level befell when hyperscalers like AWS made scalable compute and garage broadly obtainable.
This evolution displays a chronic problem all the way through AI’s historical past—operating out of garage and compute capability. Those boundaries brought about earlier AI winters, and overcoming them has been basic to nowadays’s “AI renaissance.”
How does Undertaking’s end-to-end cloud carrier mannequin assist corporations scale their AI workloads on AWS extra successfully and securely?
At Undertaking, safety is built-in into the whole lot we do. We have now been the protection spouse of the yr with AWS two years in a row, however apparently, we don’t have a devoted safety workforce. That’s as a result of everybody at Undertaking builds with safety in thoughts at each and every section of building. With AWS generative AI, shoppers have the benefit of the use of the AWS Bedrock layer, which helps to keep information, together with delicate knowledge like PII, safe throughout the AWS ecosystem. This built-in means guarantees safety is foundational, no longer an afterthought.
Scalability may be a core focal point at Undertaking. We have now in depth revel in constructing MLOps pipelines that arrange AI infrastructure for coaching and inference. Whilst many affiliate generative AI with large public-scale methods like ChatGPT, maximum endeavor use circumstances are inside and require extra manageable scaling. Bedrock’s API layer is helping ship that scalable, safe efficiency for real-world workloads.
Are you able to stroll us via an ordinary endeavor engagement—from cloud migration to deploying generative AI answers—the use of Undertaking’s products and services?
At Undertaking, we start via figuring out the endeavor’s enterprise wishes and use circumstances. Cloud migration begins with assessing the present on-premise atmosphere and designing a scalable cloud structure. Not like on-premise setups, the place you will have to provision for height capability, the cloud means that you can scale assets in keeping with reasonable workloads, decreasing prices. Now not all workloads want migration—some can also be retired, refactored, or rebuilt for potency. After stock and making plans, we execute a phased migration.
With generative AI, we’ve moved past proof-of-concept levels. We assist enterprises design architectures, run pilots to refine activates and deal with edge circumstances, then transfer to manufacturing. For data-driven AI, we lend a hand in migrating on-premises information to the cloud, unlocking better worth. This end-to-end means guarantees generative AI answers are powerful, scalable, and business-ready from day one.
Undertaking emphasizes “innovation with self belief.” What does that imply in sensible phrases for companies adopting AI at scale?
It approach having a workforce with genuine AI experience—no longer simply bootcamp grads, however seasoned information scientists. Shoppers can believe that we’re no longer experimenting on them. Our folks know the way fashions paintings and the best way to put into effect them securely and at scale. That’s how we assist companies innovate with out taking needless dangers.
You’ve labored throughout predictive analytics, NLP, and pc imaginative and prescient. The place do you notice generative AI bringing essentially the most endeavor worth nowadays—and the place is the hype outpacing the truth?
Generative AI is offering vital worth in enterprises principally via clever report processing (IDP) and chatbots. Many companies combat to scale operations via hiring extra folks, so generative AI is helping automate repetitive duties and accelerate workflows. As an example, IDP has decreased insurance coverage utility assessment instances via 50% and progressed affected person care coordination in healthcare. Chatbots continuously act as interfaces to different AI equipment or methods, permitting corporations to automate regimen interactions and duties successfully.
Then again, the hype round generative pictures and movies continuously outpaces genuine enterprise use. Whilst visually spectacular, those applied sciences have restricted sensible programs past advertising and marketing and artistic tasks. Maximum enterprises to find it difficult to scale generative media answers into core operations, making them extra of a novelty than a basic enterprise device.
“Vibe Coding” is an rising time period—are you able to provide an explanation for what it approach for your international, and the way it displays the wider cultural shift in AI building?
Vibe coding refers to builders the use of massive language fashions to generate code primarily based extra on instinct or herbal language prompting than structured making plans or design. It’s nice for dashing up iteration and prototyping—builders can briefly take a look at concepts, generate boilerplate code, or offload repetitive duties. Nevertheless it additionally continuously ends up in code that lacks construction, is difficult to care for, and is also inefficient or insecure.
We’re seeing a broader shift towards agentic environments, the place LLMs act like junior builders and people tackle roles extra corresponding to architects or QA engineers—reviewing, refining, and integrating AI-generated elements into better methods. This collaborative mannequin can also be tough, however provided that guardrails are in position. With out correct oversight, vibe coding can introduce technical debt, vulnerabilities, or efficiency problems—particularly when rushed into manufacturing with out rigorous trying out.
What’s your take at the evolving function of the AI officer? How must organizations reconsider management construction as AI turns into foundational to enterprise technique?
AI officials can completely upload worth—however provided that the function is ready up for good fortune. Too continuously, corporations create new C-suite titles with out aligning them to current management constructions or giving them genuine authority. If the AI officer doesn’t percentage objectives with the CTO, CDO, or different pros, you chance siloed decision-making, conflicting priorities, and stalled execution.
Organizations must in moderation believe whether or not the AI officer is changing or augmenting roles just like the Leader Information Officer or CTO. The identify issues lower than the mandate. What’s vital is empowering any individual to form AI technique around the group—information, infrastructure, safety, and enterprise use circumstances—and giving them the power to pressure significant exchange. In a different way, the function turns into extra symbolic than impactful.
You’ve led award-winning AI and knowledge groups. What qualities do you search for when hiring for high-stakes AI roles?
The number 1 high quality is discovering any individual who in truth is aware of AI, no longer simply any individual who took some classes. You wish to have people who find themselves really fluent in AI and nonetheless care for interest and passion in pushing the envelope.
I search for people who find themselves all the time looking for new approaches and difficult the limits of what can and cannot be performed. This mix of deep wisdom and persevered exploration is very important for high-stakes AI roles the place innovation and dependable implementation are similarly vital.
Many companies combat to operationalize their ML fashions. What do you assume separates groups that be successful from those who stall in proof-of-concept purgatory?
The most important factor is cross-team alignment. ML groups construct promising fashions, however different departments don’t undertake them because of misaligned priorities. Transferring from POC to manufacturing additionally calls for MLOps infrastructure: versioning, retraining, and tracking. With GenAI, the space is even wider. Productionizing a chatbot approach suggested tuning, pipeline control, and compliance…no longer simply throwing activates into ChatGPT.
What recommendation would you give to a startup founder constructing AI-first merchandise nowadays that would have the benefit of Undertaking’s infrastructure and AI technique revel in?
When you find yourself a startup, it is tricky to draw best AI ability, particularly with out a longtime emblem. Even with a powerful founding workforce, it’s laborious to rent folks with the intensity of revel in had to construct and scale AI methods correctly. That’s the place partnering with a company like Undertaking could make an actual distinction. We will be able to can help you transfer sooner via offering infrastructure, technique, and hands-on experience, so you’ll validate your product faster and with better self belief.
The opposite vital piece is focal point. We see a large number of founders looking to wrap a elementary interface round ChatGPT and get in touch with it a product, however customers are getting smarter and be expecting extra. In case you are no longer fixing an actual drawback or providing one thing really differentiated, it is simple to get misplaced within the noise. Undertaking is helping startups assume strategically about the place AI creates genuine worth and the best way to construct one thing scalable, safe, and production-ready from day one. So you are no longer simply experimenting, you are constructing for enlargement.
Thanks for the good interview, readers who want to be informed extra must talk over with Mission.
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