1. Choose the appropriate session format for your content:
Machine Learning Week runs with three tracks: Business Case Studies, Industrie Case Studies and Tech/Deep Dives. Within the tracks, there are the following session formats:
A Keynote is a visionary lecture (~45 min.) on a major topic by well-known experts – there are very limited slots!
A Case Study (~30 min.) presents a concrete project and its business outcome – the company / client is presenting or has to be named! You can choose between a business- or an industry-related case study.
A Deep Dive (~60 min.) gives a deep understanding of a specific method, tool or topic – show not only slides, but also code, demo etc.!
A Table Discussion (~50 min.) provides the opportunity to discuss a controversial topic in a small group.
You can submit multiple applications. Co-Speakers are only accepted for case study sessions and only if it makes sense from a content perspective.
2. Always remember: You are talking to experienced professionals
Most participants are senior data scientists, head of data science, chief data officer or are working in similar roles for well-known companies (DAX, Fortune 500, Unicorns). Our attendees already have a deep knowledge of data science and want to apply data science to solve real business challenges.
Put yourself in the position of the audience: What would you like to hear? What would make a positive impression and make you feel your time well-spent?
3. The ideal session abstract:
- Short and concise session title and description
- Be open, honest and concrete: Name the problem, describe the solution, and quantify the benefit.
- Give a promise: tell the attendees what they can learn from your session and how they can use this knowledge for their work
- The topic should be new, interesting and relevant for the business
- Show a demo, code, figures, examples etc.
- One sentence – or a list of bullets – is not enough. More than 100 words is usually too much. Express your message fully but to the point.
Last but not least: Sales pitches are not allowed. We are vendor neutral. If you want to show results that have been achieved with a specific tool or service, you must always mention the alternative offerings that would let you achieve the same results.