- 4 tracks, 4 Fridays, 4 speakers each day
- It’s online and entirely free!
- Time is in CET (UTC +1)
- Can’t attend? Register anyways, we will send you the recordings.
- Machine Learning Use Cases (05 Feb 2021)
- Product and Process (12 Feb 2021)
- Career in Data (19 Feb 2021)
- Machine Learning in Production (26 Feb 2021)
Machine Learning Use Cases (05 Feb 2021)
14:00 CET – Phil Winder – Industrial Applications of Reinforcement Learning
Show abstractReinforcement learning (RL), a sub-discipline of machine learning, has been gaining academic and media notoriety after hyped marketing “reveals” of agents playing various games. But these hide the fact that RL is immensely useful in may practical, industrial situations where hand-coding strategies or policies would be impractical or sub-optimal. Following the theme of my new book (https://rl-book.com), I present a rebuttal to the hyperbole by analysing five different industrial case studies from a variety of sectors. You will learn where RL can be applied, how to spot challenges that fit inside the RL paradigm, and what pitfalls to watch out for. You will learn that RL is more than an bot in a game; it is the next frontier in applied artificial intelligence. I avoid using jargon to make this talk acceptable for a wider audience. I do expect that you have limited exposure to data science/machine learning in general.
15:00 CET – Himanshu Upreti – How to use AI in Consumer Food Product Innovation
Show abstractThe way to do market research and create product concepts hasn't changed in decades in the CPG Industry. 9 out of every 10 products launched by CPG Industry fail in the first year itself. With such a high failure rate, no wonder the Industry is looking out for latest tech solutions. In this talk, I will try to demystify the process of product concept generation and how the latest advancements in AI are being leveraged to achieve the same. If you are someone who is interested in understanding the links between consumer understanding and technology, then this talk will be a treat for you.
16:00 CET – Mahmoud AbdelAziz – Machine Learning in Manufacturing
Show abstractComing soon
17:00 CET – Eugene Yan – Building an ML System for Southeast Asia’s Largest Hospital Group
Show abstractComing soon
Product and Process (12 Feb 2021)
14:00 CET – Elias Nema – Building Data-Intense Teams
Show abstractNowadays, users expect your app to be not only fast and reliable but also smart. As a consequence, more and more teams are becoming data-intensive — relying on data to build their solutions. And it’s a common belief that putting models into production is one of the biggest bottlenecks in a journey of becoming more data-driven. While true, this step is only the beginning of the journey. I believe that a much broader transformation is required in how we think about product development lifecycle as well as communication flows between business, engineering and data. In this talk, I’ll show how we are building data-grounded solutions in the domain of search and recommendations and instilling an experimental culture in one of the biggest marketplaces.
15:00 CET – Susan Walsh – Dangers of Dirty Data
Show abstractWe all think we know what bad data looks like, but what is it and what are the consequences? Susan Walsh, The Classification Guru has spent nearly a decade classifying, normalising and cleansing spend data and will share real-life examples of dirty data, and the consequences it has on the output, such as decision making, reporting, analytics, AI and machine learning. She will share with you how to make quick, accurate checks and changes to your own data in excel, regardless of your level of experience, explain why data accuracy and maintenance is so important and implement best practices for this. Takeaways:
- The importance of data accuracy
- The consequences of dirty data
- How to ensure data accuracy
- Make sure your data has its COAT on
- How to spot check your data
16:00 CET – Elena Samuylova – How Your ML Project Will Fail
Show abstractCreating a machine learning model is not an easy task. Creating a useful machine learning model that gets into production and generates actual business value - is an even harder one. There are many ways for an ML project or product to fail even when the data is there and the model technically performs well. From the wrong problem statement to lack of trust from stakeholders, in this talk I will discuss what issues to look out for, and how to avoid them.
17:00 CET – Dan Becker – Translating ML Predictions Into Better Real-World Results with Decision Optimization
Show abstractMachine learning models make predictions, but predictions are only useful to the extent they help us make better real-world decisions. For example, a model may predict that a financial transaction is 5% likely to be fraud. Should the transaction be approved, denied or delayed for further investigation? This decision requires further information and analysis. A few years ago, data scientists could build models and hope someone else would figure out how to translate predictions into decisions. That division of responsibility has generally been a failure, and now most companies expect data scientists to ensure their models lead to better decisions and better business results. Learn decision optimization techniques so you can consistently translate your models into better decisions and optimized business results.
Career in Data (19 Feb 2021)
12:00 CET – Danny Ma – The ABC’s of Data Science
Show abstractComing soon
13:30 CET – Admond Lee Kin Lim – Personal Branding
Show abstractComing soon
15:00 CET – Parul Pandey – Career Transitioning into Data Science
Show abstractData Science is the art of making sense out of data. While we all agree that data should back all decisions, many of us still aren’t sure how to get the data to speak in the first place. Many people from varied backgrounds want to start on to their data science journey, but unfortunately face a lot of roadblocks. The sheer amount of materials and resources available online are more overwhelming than helping. There are thousands of books, papers, blogs, mentors and this doesn’t help. Beginners fail to devise a plan and hence cannot convert the theoretical knowledge into practical industry-based projects. If you are also sailing in the same boat, then this talk is for you. I’ll share my journey of how I switched to Data Science and share some industry experiences with you to create a balance between studying and execution.
16:00 CET – Vin Vashishta – New Roles and Key Skills to Monetize Machine Learning
Show abstractWe will discuss monetization roles and the capabilities people need to move into those roles. The key roles are ML Researcher, ML Architect, and ML Product Manager.
Machine Learning in Production (26 Feb 2021)
14:00 CET – Larysa Visengeriyeva – 10 Foundational Practices of Machine Learning Engineering
Show abstractThe hype around Machine Learning and Artificial Intelligence can give the illusion that software with integrated ML models is easy to develop. However, according to various studies, almost 80% of ML projects fail to get into production. In this talk, Larysa will present 10 fundamental practices for machine learning engineering to succeed in your own project.
15:00 CET – Mikio Braun – Putting Data Science in Production
Show abstractEvery year, the number of tools that promise to take care of everything that's needed to put ML into production is growing, but which tool is really the right tool for the job and what does what? In this talk I'll be taking a look from the inside out, starting with an ML algorithm and gradually adding layers to give a bit of structure to this area. At the end, you probably have a better understanding what kinds of tools exist and what you need when.
16:00 CET – Sara Robinson – Machine Learning Design Patterns
Show abstractDesign patterns capture best practices and solutions to recurring problems. Join us for a talk from one of the authors of the newly released O’Reilly book “Machine Learning Design Patterns”, covering solutions to common challenges in Data Preparation, Model Building, and MLOps. Sara will introduce three of these tried-and-proven methods to help engineers tackle problems that frequently crop up during the ML process. She'll cover the following patterns: Rebalancing, Workflow Pipeline, and Feature Store.
17:00 CET – Elle O'Brien – Continuous Integration for Machine Learning
Show abstractMachine learning is maturing as a discipline: now that it’s trivially easy to create and train models, it’s never been more challenging to manage the complexity of experiments, changing datasets, and the demands of a full-stack project. In this talk, we’ll examine why one of the staples of DevOps, continuous integration, has been so challenging to implement in ML projects so far and how it can be done using open-source tools like Git, GitHub Actions, and DVC (Data Version Control). We'll also discuss a new open source project (Continuous Machine Learning) created to adapt popular continuous integration systems like GitHub Actions and GitLab CI to data science projects.