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AutoML v.0.4.3: Resource Optimization and More Time Series

Claudio Bruderer

Written by:
Claudio Bruderer (Product Manager at Modulos)

Modulus AutoML v.0.4.3 is out! With this version, you can configure how long your Machine Learning workflow should run for to suit your needs. We are also significantly extending the capabilities of the Time Series workflow. Furthermore, we include a range of other enhancements and refinements.


Configurable Resource Optimization Strategy

Screenshot of the step in the workflow creation process, where the autopausing settings can be configured for a workflow.

To find the best ML models, we need to search the space of different ML model types, model architecture, and hyperparameter combinations. This space of possible Solutions is typically high-dimensional and can even be infinite. Hence, finding and training Solutions is best done systematically and in an automated fashion.

Modulos AutoML efficiently searches this space of possible combinations. Due to the nature of this task though, there can generally be no guarantee of finding the best Solution. This was the reason for us to introduce in AutoML v.0.3.4 the feature that ML workflows are automatically paused if the scores have not improved for a while.

We take it one step further with this release. You are now able to configure workflows with your own autopausing preferences. Should the workflow pause after not improving for X Solutions? Do you want it to pause after reaching a certain score? Do you have a fixed budget and it hence should pause after having computed X Solutions? Or do you want it to never pause automatically? You are now able to make this choice yourself and can configure it when creating a workflow!


Multi Step Time Series Forecasting

With the last release, Modulus AutoML v.0.4.2, we introduced Time Series ML workflows. This workflow type allows you to leverage the time dependency of your data and build ML models to tackle all sorts of forecasting challenges (e.g. forecast growth, demand, supply etc.).

For the previous release, the forecasting model was limited to predicting the immediate next time step. Now you can forecast further into the future and predict multiple steps. The platform builds and trains the ML models (Solutions) accordingly and optimizes them to do well for all forecasted steps.

In the figure above, we illustrate this new feature for the bike sharing use case we have presented here. In this example, a company renting out bicycles wants to forecast their demand for bikes one week from now. As you can see, we are doing quite well in predicting the demand for the next day (left plot), but could do better when forecasting the demand for bikes seven days in advance (right plot). These figures show for which forecast time scales you are meeting the requirements and for how far into the future the model is capable of forecasting reliably.

If you want to use Modulos AutoML with Time Series, we are happy to talk to you about your use case and enable this feature for you.


Other New Features

New Objective: Mean Absolute Percentage Error (MAPE)

Objectives are the key metrics to assess the performance of a ML model and determine what you are optimizing for. The choice of the objective depends on your use case and business requirements.

To broaden the selection, we are adding a new objective with this AutoML release: the Mean Absolute Percentage Error (MAPE). This metric computes the mean relative deviations between your true and predicted values. This objective is available for all regression tasks (predicting a number) like, for instance, Time Series forecasting.

Interactive Feature Importance Graphic

Example interactive Permutation Feature Importance plot for a classification task for predicting customer churn for a company in the telecommunications sector.

When training and building ML Solutions, Modulos AutoML optimizes the model to reach a good score. Besides the model performance, it is also important for the models to be interpretable, to understand how predictions are made, and what impacts them.

In Modulos AutoML v.0.4.1, we have introduced the permutation feature importance plot in the Solution. It is available for a select set of datasets and ML workflows. To compute this plot, we randomly shuffle input parameters and then apply the model. By assessing how much this shuffling affects the model performance, we can identify the crucial input parameters (shuffling an important input parameter leads to large prediction errors).

With this release we have made this plot interactive. This allows you to play around with it and it increases its readability.

AutoML Checker & Diagnosis

While we strive for adding more features with every release, enhancing the user experience platform maintenance is as important. To this end, we are adding the new “automl diagnosis” command. It gives the administrator of the platform the tools to diagnose the state of the platform. We are furthermore also adding additional checks, which are run in the background when executing any maintenance command. For instance, these will test the software versions of the Modulos AutoML prerequisites.


OTHER IMPROVEMENTS AND BUG FIXES

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

  • Separated the dependencies of the downloadable Modulos AutoML Solutions depending on the corresponding ML models. This means you avoid installing unused dependencies.
  • Added support for the latest version of Docker v.20.10, which is a prerequisite for installing and running our platform.
  • Replaced the summary of the workflow configuration on the “Configuration” tab for each workflow with the summary of the workflow creation step to show all configured fields.
  • Fixed a bug in the Inputs & Label Selection step within the creation process of workflows. For data with numerical input feature names (e.g. “1”, “2”, …), the displayed example values did not match the actual values.
  • Increased the number of displayed elements on overview tables, which list all the workflows or datasets.
  • Fixed a bug that the evaluation of models on validation data failed, if a validation dataset contained exactly 101 samples or a multiple thereof.
  • Updated various Modulos AutoML dependencies; among which Node (v.14.17), sklearn (v.0.24.2), and SQLAlchemy (v.1.4.0).

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Dominic Stark

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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.

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Kevin Schawinski

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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.

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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).

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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.

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Rudolf Bär

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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.

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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.

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Bojan Karlaš

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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

Sales Manager

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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.