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AutoML v.0.4.1: Interactive Insights Plots and About Pages

Claudio Bruderer

Written by:
Claudio Bruderer (Product Manager at Modulos)

Modulos AutoML version 0.4.1. is out. With this version, we are adding several new features and enhancing existing ones. The focus of the latest AutoML version is to give you more insight into the platform and the trained Solutions. The newest additions include interactive confusion matrix plots and feature importance analyses in the Solutions. There are also enhancements to the dataset upload and new information pages about the software.


Data Science Updates

Interactive Confusion Matrix Plots

Interactive confusion matrix for a classification ML trained on the MNIST dataset.

The Solution is the downloadable output of the Modulos AutoML platform. Besides the trained machine learning model, it contains various documentation on its deployment and plots. These plots give you more insight into the performance of the model.

One of these plots is the confusion matrix. It is an essential plot for classification tasks, as it summarizes the correctly and incorrectly classified samples by the model. The newest version of Modulos AutoML now also contains an interactive form of this plot. It is helpful for cases where a label has a large number of categories. Furthermore, it looks nice!

Permutation Feature Importance

Example Permutation Feature Importance plot for a classification task prediction the occurrence of diabetes in women.

While model performance is crucial, it is also important to understand the main input parameters which influence a prediction. Answering these questions is part of the subfield of machine learning interpretability.

Striving for fully interpretable ML models, we introduce the Permutation Feature Importance plot in the latest version of Modulos AutoML. For a limited set of datasets and ML workflows, the plot shows the importance of each input feature. We randomly shuffle the individual parameter values and assess the impact of the shuffling on the prediction performance. With a higher resulting prediction error, the importance of the corresponding feature increases. This plot makes the ML model more interpretable and allows focusing on the crucial features during data preparation.

Dataset Structure File Upgrade

To upload your data and train ML models using the latest version of AutoML, the data needs to include the dataset structure file (DSSF). This file describes the structure of your dataset and allows you to easily upload a collection of different tables and/or images. The dataset structure file also allows you to specify the exact properties of your features. These are otherwise inferred by the platform.

We have restructured and refined the DSSF to make it easier for you to define all the feature types. These changes are fully backwards compatible. They thus ensure that the platform works seamlessly even for datasets using the previous DSSF version.


Platform Updates

About Pages

About Pages as displayed in the latest AutoML version.

Another new feature of the latest AutoML version are the About Pages. They describe the legal aspects of the platform and the downloadable Solutions, the current state of your AutoML license, and our company. The About Pages now give you more insight into Modulos and our products in addition to the existing documentation materials.


Other Improvements and Bug Fixes

We additionally have made a range of other improvements and fixes to AutoML, which include:

  • Fully redesigned the schema matching pipeline. This pipeline is the core component of our platform. It infers the applicable feature extractors and models combinations (ML modules) for any of your ML problem statements.
  • Refined few minor aspects of the workflow creation as the text on the navigation buttons and the sorting in the applicable ML modules popup.
  • Adapted all search fields on the platforms to be case insensitive leading to a more intuitive user experience.
  • Improved the database commands, which no longer require the backend Docker container to be running.
  • Increased the robustness of the ML model training by preventing rare memory errors. These occurred for specific student t-test settings used in combination with XGBoost or random forest models.

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Jérôme Fischer

Sales Development

“The only way to do great work, is to love what you do.” – Steve Jobs

Jérome Fischer is an expert on Sales. Apart from the successful build up of several companies like the Ad Interim Sales GmbH and the Sales4IT GmbH, he passes on his experience in various Sales and Marketing coachings. Jérome now supports Modulos in establishing the first contact with our customers.

In his free time, Mr. Fischer is an ambitious athlete with numerous awards.

Dominic Stark

Dominic Stark

Data Scientist

Code quality correlates with food quality.

Dominic Stark studied physics at ETH Zürich. The transition of his career path to Data Science began when he was analyzing UV images of galaxies. Together with Kevin Schawinski an Ce Zhang, he worked on applying the latest advances of deep learning research to his problem. It turned out that the method itself was at least as interesting as the problem they designed it for. After publishing the results, his research project was about using Reinforcement Learning to develop novel ideas for data acquisition in astronomy. As a Data Scientist at Modulos, he keeps on solving problems, that require new ideas and technologies.

Kevin Schawinski

Kevin Schawinski

CEO / Co-Founder

Running a startup is super relaxing, right?

While a Ph.D student, he co-founded the Galaxy Zoo citizen science project involving more than a million members of the public in scientific research because machines weren’t quite good enough yet to go map the cosmos and classify galaxies. He stayed in Oxford as the Henry Skynner Junior Research fellow at Balliol College before moving to Yale as a NASA Einstein Fellow. In 2012, he started the galaxy and black hole research group at ETH Zurich as an assistant professor and began a close collaboration with Ce Zhang from computer science to work on the space.ml project. He is now the CEO of Modulos.

Ce Zhang

Ce Zhang

Co-Founder

Random is best.

He believes that by making data—along with the processing of data—easily accessible to non-computer scientists, we have the potential to make the world a better place. His current research focuses on building data systems to support machine learning and help facilitate other sciences. Before joining ETH, Ce was advised by Christopher Ré. He finished his PhD round-tripping between the University of Wisconsin-Madison and Stanford University, and spent another year as a postdoctoral researcher at Stanford. His PhD work produced DeepDive, a trained data system for automatic knowledge-base construction. He participated in the research efforts that won the SIGMOD Best Paper Award (2014) and SIGMOD Research Highlight Award (2015), and was featured in special issues including the Science magazine (2017), the Communications of the ACM (2017), “Best of VLDB” (2015), and the Nature magazine (2015).

Alexandra Arvaniti

Alexandra Arvaniti

Operations Manager

“You miss 100% of the shots you don’t take.” – Wayne Gretzky

During the last twenty years, she worked in different roles, setting up and running PMOs, supporting the Executive Management Team or as Operations Manager for the DACH region. She loves all organizational challenges, which she can use well at Modulos, like set up and establish administrative business processes.

Rudolf Bar

Rudolf Bär

Chairman of the Advisory Board

After initially working for Dow Corning International in Zurich and Brussels (1964 to 1969), he held various management functions in the Private Banking Group Julius Baer, Zurich, lastly as CEO from 1993 to 2000 and retired from its Board of Directors in 2005. Since 2014 he has been studying at the Institute for Particle Physics and Astrophysics at the ETH, Zurich.

Marianne Chiesi

Marianne Chiesi

Administration

Marianne has worked in administration of various companies and the ETH.

Marianne Chiesi worked in the administration of various companies before taking time off to raise her children. She translated text books and literary works into Braille and joined the ETH Zurich as an administrative assistant. At ETH, she worked with professorships and researchers in many areas, including astrophysicists, particle physicists and biochemists. She now runs the administration at Modulos.

Bojan Karlaš

Bojan Karlaš

Software Engineer

Real engineers must be a little bit lazy.

After getting a bachelor’s degree in software engineering at the University of Belgrade, Serbia, Bojan spent 2 years working as a developer at Microsoft building distributed database solutions. He then went to Switzerland to pursue a computer science master’s degree at EPFL. He did his master thesis with Ce Zhang at ETH Zürich on the topic of time series forecasting, after which he joined Ce’s group as a PhD student. His industry experience also includes internships at Microsoft, Oracle and Logitech. His research interests revolve around systems and abstractions for making machine learning accessible to non-experts.

Romain Lencou

Romain Lencou

Head of Engineering

Deleted code is debugged code. (Jeff Sickel)

Romain Lencou graduated from the Grenoble Institut National Polytechnique with M.Sc in Computer Science in 2008. Growing up in France in the 90’s, he developed an enthusiasm for pop culture, technology and food. Always eager for technological challenges, Romain worked for companies like VMware, Intel and Logitech, covering various topics including cryptography, virtualization and computer vision. Bitten by the machine learning bug, he is looking forward to apply his problem solving skills in Modulos.

Dennis Turp

Dennis Turp

Data Scientist

Dennis Turp is the first employee of Modulos.

Prior to his work at Modulos he studied physics at ETH Zurich. During his Master studies he worked together with Kevin Schawinski and Ce Zhang on exploring machine learning related topics in astronomy. In these one and a half years they published three scientific papers together. Dennis Turp is currently employed as a Data Scientist. His main expertise lies in the fields of generative modeling and anomaly detection.

Michael Röthlisberger

Michael Röthlisberger

Data Scientist

Data handling with structure

He started to take an interest in Data Science and Software Development during his master’s degree. For his master thesis he worked on the image reconstruction software for a new PET detector. Michael gained some first experience in an internship for Sensirion AG. There he was part of the R&D team, which was developing a new gas sensor. The participation of a machine learning hackathon was sparking the interest of Michael in ML and he decided to pursue a career in this field. He is now exited to face new challenges with modulos and experience working in a rising start-up.

Laura Guerrini

Data Science Intern

Laura Guerrini is the first intern of Modulos.

Laura is currently finishing her Master’s in Robotics, Systems and Control at ETH. During her studies, she focused on machine learning, control theory and optimization. She joined Modulos as a Data Science Intern to put theory into practice and boost her machine learning and programming skills.

Andrei Văduva

Andrei Văduva

Software Engineer

The trendsetter geek

He focused his attention on designing Architectures of Computer Systems. During university, he gained an excellent understanding of performance optimization and scalability on architectures such as distributed systems. Having a good experience in various Computer Science fields like big data analytics and Artificial Intelligence, he did his bachelor’s thesis designing a Machine Learning algorithm for social media platforms. After graduation, he joined the investment banking industry, in London, where he gained good experience in designing and building high-quality software. Andrei moved to Switzerland to explore new perspectives and found a great challenge in the startup world. Using his passion for technology and professional experience, he brings the best practices in software engineering to Modulos.

Modulos appoints Anna Weigel as CTO

Anna Weigel

Chief Technology Officer

After acquiring Bachelor and Master degrees in Physics, Anna completed her PhD in Astrophysics in Kevin Schawinski’s group at ETH. Her work on the relationship between supermassive black holes and their host galaxies is summarized in five first-author papers. After exploring the depths of our Universe, Anna joined Modulos as the Head of Data Science. She has since been appointed the role of CTO and is now leading the overall technology development.

Claudio Bruderer

Claudio Bruderer

Product Manager

Give me coffee to function.

After obtaining a BSc and a MSc degree in physics at ETH Zurich, Claudio decided to continue his studies of the Universe as a PhD student in Prof. Refregier’s Cosmology research group. He studied the gravitational lensing effect, whereby he measured the shapes of several billions of galaxy images (mostly synthetic ones). After acquiring his PhD, Claudio then joined the consulting company AWK Group AG and worked as a project manager and associate for IT and communications projects in the logistics and mobility sectors and for the federal government. Determined to create cutting-edge IT solutions, he decided to join Modulos as a product manager.

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Christoph Golombek

Christoph Golombek

Sales Manager

Happy customers, happy Christoph – or is it the other way around?

After finishing his master studies in Energy Technology at RWTH in Germany, Christoph started his professional career as an expert and Sales Support Engineer for wind turbines in cold climates in Canada. There he started seeing the benefits of machine help in tackling data-driven challenges. Having explored the great North, his passion for cutting edge technology drove him into the machine vision sector in Switzerland, where he has worked as a fusion of Sales Engineer and Tech Support, while also acting as a Team Leader of a team of four. At Modulos, he can now focus again on bringing state-of-the-art technology to happy customers.

Florian Marty

Florian Marty

Sales Manager

Putting Science into the Art of Sales.

As a Ph.D. in Molecular Biology from the University of Zurich, Florian Marty was, like most scientists, not a big fan of sales initially. But, over the years and with growing experience in different commercial roles, he learned that there is a lot of science in what makes good salespeople. Coupled with his open mindset to learn new things and a communicative personality, Florian is fascinated to explore and test new strategies, tactics, and expert moves in sales. As a Sales Manager, he will be joining the commercial team helping to grow the customer base and make Machine Learning accessible to everyone. Fun fact, as Florian has never written a single line of code in his life.

We believe he is the perfect fit to bring across the Modulos value proposition to our customers. Do not hesitate to reach out to Florian to engage in a discussion about Modulos AutoML.