Master Thesis Offer: Evaluation of AutoML Frameworks for Predictive Analytics Tasks

📌 Key facts

• 📌 Key facts
• 💡 Background
• 🦾Who We Are
• 🎯 Goals
• 🎓 Profile
• ⚠️ Essential Requirements
• 🟢 Additional Skills (Nice to Have)
• 👩🏻‍🎓👨🏽‍🎓 Personal Attributes
• 📚 Further Readings & Helpful Materials
• 📝 How to Apply / 📬 Contact

💡 Background

Automated Machine Learning (AutoML) frameworks have revolutionized the field of machine learning by automating complex tasks such as model selection, hyperparameter tuning, and feature engineering. This study evaluates the performance of various AutoML frameworks for binary and multiclass classification tasks in predictive analytics. The importance of this research lies in enhancing the efficiency and accessibility of machine learning processes, significantly reducing the time and expertise required to build robust models, thereby democratizing advanced analytics. Understanding the strengths and limitations of different AutoML frameworks is crucial for their optimal application in real-world scenarios, providing valuable insights into their performance, robustness, and computational efficiency (He et al., 2021; Zöller & Huber, 2021). This study aims to contribute valuable knowledge to both the academic community and industry practitioners, guiding the development and adoption of more effective AutoML technologies.

🦾Who We Are

The Chair for Strategy and Organization is focused on research with impact. This means we do not want to repeat old ideas and base our research solely on the research people did 10 years ago. Instead, we currently research topics that will shape the future. Topics such as Agile Organisations and Digital Disruption, Artificial Intelligence, Blockchain Technology, Creativity and Innovation, Digital Transformation and Business Model Innovation, Diversity, Education: Education Technology and Performance Management, HRTech, Leadership, and Teams. We are always early in noticing trends, technologies, strategies, and organisations that shape the future, which has its ups and downs. More information on Nicolas Leyh can be found @Nicolas Leyh & under LinkedIn.

🎯 Goals

Objective: The objective of each master thesis paper within the research group is to evaluate the performance of three AutoML frameworks for binary and multiclass classification for predictive analytics tasks. The study specifically aims to identify the strengths and weaknesses of these frameworks across different types of (1) data segments and (2) runtimes, and to investigate the (3) reproducibility of their predictions (robustness). More detailed information and the exact approach will be shared during the project onboarding.

🎓 Profile

⚠️ Essential Requirements

1. Python Coding Skills
• Basic to intermediate level proficiency in Python is essential. You should be comfortable writing and understanding Python code.
2. Data Analysis & Data Science Background
• Basic knowledge of data analysis and data science is helpful. Intermediate skills in these areas will be preferred.
3. Understanding of Machine Learning
• A solid understanding of the fundamentals of machine learning is beneficial.
• Familiarity with the concept of a Machine Learning Pipeline is advantageous (Link).
4. Research Independence
• The ability to conduct independent research without collaboration with partner companies. This ensures the research remains unbiased and solely contributes to the overarching research project on benchmarking AutoML frameworks at the CSO.

🟢 Additional Skills (Nice to Have)

1. R Programming Skills
• Knowledge of R can be useful, especially for data analysis and result visualization.
2. Experience with AutoML Frameworks
• Previous experience with AutoML frameworks (like H2O.ai, or AutoKeras) will be considered a major plus.
3. Experience with Different Datasets
• Working with a variety of dataset sizes and formats, both structured and unstructured.

👩🏻‍🎓👨🏽‍🎓 Personal Attributes

1. Analytical Thinking
• Strong analytical skills to critically evaluate the performance of different AutoML frameworks and their limitations.
2. Attention to Detail
• Meticulous attention to detail to ensure accuracy in your experiments and reporting.
3. Proactive and Self-Motivated
• Proactivity and self-motivation are key, as independent research requires a high degree of self-discipline and initiative.

📚 Further Readings & Helpful Materials

AutoML Introduction

💡 Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time-consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality. Source: Microsoft

→ Please review both pages below to get a better understanding of AutoML

• What Is AutoML? | IBM
• What is automated ML? AutoML - Azure Machine Learning

→ Additional materials and readings will be shared during the Onboarding process.

📝 How to Apply / 📬 Contact

If you are interested, please contact Nicolas Leyh by submitting your CV, grade report and a short motivation letter (focus on fit for this topic, e.g. data science background).

Nicolas Leyh (Chair for Strategy and Organisation) 👉 nicolas.leyh@tum.de