Dr. Ryan Ries, Practice Lead for Data, Analytics, & Machine Learning
In this blog article series, Mission Team Spotlight, we interview and chat with our knowledgeable team members about their careers, time at Mission, and trends in their respective fields.
In this feature, we get to know Dr. Ryan Ries, a renowned data scientist with more than 15 years of data and engineering leadership at fast-scaling technology companies. After earning his Ph.D. in Biophysical Chemistry at UCLA and Caltech, Dr. Ries has helped develop cutting-edge data solutions for the U.S. Department of Defense and a myriad of Fortune 500 companies.
This interview has been edited for length and clarity.
Your educational background is in chemistry and physics, and for your Ph.D., biophysical chemistry. Could you tell me how these fields brought you to your career in data, analytics, and machine learning?
I’ve always loved understanding how things work and how they were built, so I pursued physics and chemistry since they are the world's fundamental building blocks. As for my Ph.D. in biophysical chemistry, I have always liked physical chemistry because it’s a deeper dig into the world around us. With physical chemistry, you dive deep into understanding how different compounds work.
In grad school, I worked with a class of inorganic compounds called rotaxanes that had a ring of molecules around a rod, which formed the base switches for molecular computing. Much of this work centered around sensors and how we used those sensors to probe the compounds we made. Statistics, probabilities, and collecting data came in when we were trying to understand the output of our sensors. As we started to probe deeper into our compounds, we were essentially intaking microscopy data and images and had to write early computer vision algorithms to analyze them. Computer vision is the artificial intelligence field that deals with how computers can gain a high-level understanding of digital images and videos.
This was essentially my introduction to the data, analytics, and machine learning field. A lot of this code is now available for free as a Python library, making things easier for all the new kids. But back then, before there were open-source libraries, we had to write all of this code on our own to do these analyses and machine learning algorithms on the microscopy images we were getting. With this kind of collective work and documentation, data analysis has become much more than a practice just done in science and labs, it’s become a huge industry of its own.
It sounds like you were in data, analytics, and machine learning before it became the widely popular space that’s known today. Could you tell me more about what you enjoy most about being in data, analytics, and machine learning and also what’s kept you in it all these years?
In this field, you solve many interesting problems and do pretty cool things. For example, in a field within computer vision called multi-view geometry, you can take 2D images and build 3D images from them. This technology is what makes up 3D maps like Google Earth. The computational method for taking those 2D images and making 3D images is called SLAM (Simultaneous Localization and Mapping). It’s also the same method used for self-driving cars to build a map and localize themselves on that map. It’s so interesting and awesome to me that by collecting images, which is the data, you can make a 3D map.
Another example is augmented reality and virtual reality (AR/VR) devices. For most people, when you use an AR/VR device, you’ll get some sort of motion sickness if there isn’t an image update on the device (goggles), within eight milliseconds. To achieve super short refresh times, AI is used to make predictions with the images used to build the visual experience displayed within AR/VR devices.
These are just a few real-world applications of data intake and computing. That’s what’s kept me in the industry, all the real-world applications out there making things better for people and the cool interesting things being done with it.
What current challenges do you often see in your field?
One of the biggest challenges is getting people to truly understand their data and what it is that they are trying to get out of their data. There are so many more devices out in the world collecting data than there are people. The amount of data that everyone is collecting is more than anyone can ever conceivably use. It’s a kind of madness, the sheer amount of data out there, and it also doesn’t mean it’s all the right kind of data. You need to know what your goal is so that you can discern what’s useful data from all of the data that you’ve collected.
In addition to the data, it's important to evaluate and select the right techniques and methodologies to get to your answer or goal instead of immediately going with what is new and popular. A good example of this would be deep learning, which has gained significant popularity in recent years. However, the newest and most popular solution isn’t always the best or right one. Just going directly to the deep learning route could result in pure chaos along with you wasting tons of cycles and money just trying to get it done.
If you haven't figured out what data is relevant to your organization and how to apply machine learning correctly for the right use case, you'll have difficulties reaching your goals. That’s why it's important to first ask yourself, is it the right data, or is it the right interpretation?
What was a pivotal moment in your career, and how did you find yourself at Mission?
One of the pivotal moments for me was leaving the hardware space and going into the cloud computing space. For many businesses, moving to the cloud enables them to address problems more quickly and at a lower cost, without having to purchase hardware.
While looking to continue my journey in the cloud space, I connected with Mission about the possibility of starting a data and machine learning practice since there wasn’t one before me. As they say, the rest is history, and here I am today.
What do you most enjoy about working at Mission?
I decided to join Mission because I liked how open the organization is about everything and all the information it shares. I don't know of many other companies like Mission, where we have a weekly meeting that walks through the books and lets everyone know where we are as a company and how we're tracking towards our goals.
Other companies, especially larger ones, are so siloed. But at Mission, we do all kinds of cool things. We have a very open environment, where you can pursue what interests you versus at other companies, you’re only able to focus on your little peck on the wall. The transparency and openness at Mission are unique, and it's something that I appreciate at the end of the day.
How does Mission differentiate itself as a cloud managed services provider in the data, analytics, and machine learning space?
One of our differentiators over other partners is that we have gone through and built a true data, analytics, and machine learning team. We've also been validated by AWS to have relevant competencies. Many partners say they're doing the same things as us but don’t have the depth of capabilities, let alone a dedicated AWS-certified data, analytics, and machine learning team. Sometimes they refer to some staff doing a lift and shift around SQL query and call that a data, analytics, and machine learning team, which it isn’t.
At Mission, we prioritize and value collaboration and consultation with our customers on designing what needs to be done. We talk to customers closely, look at their data, and understand whether or not something is possible. We’re always collaborating and talking with them, asking, is there truly ROI for them? Does this make sense? Is it going to help their business at the end of the day? We're not just doing projects to do projects. We work closely with our customers to ensure it will be a valuable project.
For an organization operating in the cloud, looking to build data-driven processes, infrastructure, or culture, what would be something to keep in mind to set up those initiatives for success?
One of the mistakes we often see companies make is having a separate data and analytics department from the machine learning department. They're somewhat dependent upon each other, but the teams are overseen by different people with different goals. Both teams fail because of this misalignment. You need to have a consistent story between the two teams and ensure that each of them has common goals if you're going to be successful.
To learn more about our data, analytics, and machine learning services and expertise at Mission, check out our services page and blog articles. Mission Cloud Services is an AWS Data and Analytics Competency Partner.