deep learning in the wild
Deep Learning in the Wild

Top Data ScienceAI and Machine Learning for Healthcare and Industrial Internet

Who are you and what’s your role in the company?

I am Oguzhan Gencoglu. I am the Co-founder and Head of Data Science of Top Data Science. My role is to lead the machine learning parts of the projects, guide our data scientists as well as implement ML/DL solutions on a daily basis.

How did you get in touch with deep or machine learning in general?

My machine learning journey started during the last year of my B.Sc. studies, accompanied with lots of signal processing. Then during my M.Sc. studies in 2012, I started getting involved in deep learning and eventually wrote my thesis on one of the first DL approaches to Sound Event Classification. During my Ph.D. studies in Finland, I have further utilized machine learning/deep learning techniques in numerous solutions involving multimodal (tabular, image, text, time-series) health data analysis. My journey with DL continues at Top Data Science where we solve prominent problems of the world with it.

Describe what your project/business does. Which problem are you solving?

At Top Data Science, we provide AI as a Service. The problems we solve vary from “prostate cancer detection from magnetic resonance images” to “industrial process optimization in forestry industry”.

Can you describe your tech stack, what libraries hardware / architecture are you using and why?

Our tech stack is strictly consisting of open source tools and libraries. Most of our problems are solved and delivered in Python and a small portion in R or C++. On a daily basis we utilize Numpy, Scipy, pandas, scikit-learn for generic ML and TensorFlow, PyTorch and keras for DL. We have our own workstations with GPUs but use AWS as well, especially for CPU-heavy analysis.

What were some of the biggest challenges technical or business related you encountered and how did you overcome them?

In my experience, the business challenges have been more difficult to overcome than the technical ones. Technical challenges are more predictable, i.e., one usually has an idea of which parts of the project may be a trouble-maker. On the other hand, business challenges such as establishing a clear communication (with the client) of what is possible to achieve with the data at hand in a given timeframe has been extremely time-consuming. The ultimate caveat to overcome these problems is to patiently explain the whole development pipeline and emphasize the difference of state-of-the-art machine learning solutions with standard software development projects over and over.

How has the industry response been to your solution?

It depends. Experts in the industry have been extremely open and positive on our solutions. They can immediately understand that AI can enhance their working pipelines and decrease their workload. Administrative and legal staff have been more resistant to these technologies involving AI, especially in healthcare, but we believe this is going to change.

What excites you most about the future of deep learning? Where do you hope the technology is going / where do you see the greatest opportunities?

Semi-supervised learning combined with deep architectures excite me the most. Most industrial problems rely heavily on lots of high quality, annotated data.

How do you keep up with new papers and publications?

I regularly follow the ML subreddit on reddit. We also have regular internal seminars in the company in which one of us presents interesting publications, libraries, blogs etc. at each session.

What do you wish to see on a candidate’s resume who is applying for an engineering position involving deep learning?

I really appreciate when a candidate goes beyond the standard benchmark tasks when it comes to experience. Object recognition from images and news article classification is all cool and jazz but how about tasks involving multi-modal data for example both images and text. These kinds of problems are extremely common in the industry.

Can you give us some facts about your current team and point out, why a candidate may want to join you?

We are a Helsinki-based AI startup of around 10 people with 6 different nationalities (so far). We are not a single-product company and our projects usually do not last for years. Therefore, you will not only gain the knowledge of applying ML/DL solutions in numerous industries but will also have the chance to acquire knowledge of that field itself from the experts. I did not know much about prostate cancer, production parameters affecting paper quality or route optimization of nautical vessels before working on those problems. There is a unique joy in that sort of work.