Follow our Hackathon live on YouTube

https://youtu.be/ZMbUzb27amo

Agenda

Friday

12:00-12:45 - Welcome

12:45-13:40 - Task introduction & group finding

13:40 - Start Hackathon

14:45-15:15 - Open Question, Intro Bonus task

15:15-15:45 - Intro into Azure

19:00 - Virtual dinner

...hack … hack … hack

Saturday

09:00 - 10:00 - Fun activity: Yoga & Energizers

10:30-11:15 - In-depth intro Bonus task

16:00 - Deadline - Submission Task

16:00-17:00 - Preparation time pitch

17:00-19:00 - 5-minute pitch for jury

19:10-19:30 - Announcement winners

19:30 - Virtual Apero

Background: FDFA IT Support Mailbot

The Swiss Federal Department of Foreign Affairs (FDFA) (Eidgenössisches Departement für auswärtige Angelegenheiten, EDA) determines and coordinates Swiss foreign policy on behalf of the Federal Council. The FDFA consists of the organizational units at head office in Bern and the network of Swiss representations, which includes embassies, consulates, cooperation offices and missions.

The FDFA internal IT Helpdesk

FDFA has its own IT department, which provides IT services. The IT Helpdesk is in charge for supporting internal users. The Helpdesk collaborators treat around 95 phone calls and 80 incoming mails per day in various languages (DE, FR, IT, EN). About 40’000 incident tickets are opened every year.

Due to the high workload support mails are sometimes only treated after several hours, which can result in users calling in and creating even more workload.

Once the Helpdesk collaborator has analyzed a support request he creates an incident in the IT Service Management Tool. He assigns the incident to a service (e.g. Mobile Communication), to a support group (the group that will treat the incident) and evaluates the urgency und impact of the request (which determines the priority).

All interactions and activities are recorded in the ticketing system.

A “Mailbot” for the FDFA IT Helpdesk

To offer a better support service to the users, the FDFA IT Helpdesk plans to use a Mailbot (for German mails only). The Mailbot should analyze incoming support mails with a pretrained AI model, open an incident in the ticketing system with the correct service category and send a receipt mail to the user. This receipt mail should also contain links to manuals from a collection of how-to manuals, that the Helpdesk team maintains. Helpdesk employees will then take over and correct & finalize the created case.

«PoCathon» Challenge: Create the best ML Model for the Mailbot

A PoCathon is a proof-of-concept (PoC) in the form of a hackathon.

FDFA wants to discover what approach performs best to analyze and classify received Mails by the internal IT Helpdesk.

Every Team creates its own PoC and will compete against the other AI solutions.

Your Objective

Your objective today is:
Based on real IT helpdesk support mails, train one or multiple machine learning (ML) models that predict the following attributes as reliable as possible (measured by F1 Score):

[Task 1] Predict Labels IT-Service & How-To Manuals

1. Which service is the incident about? (e.g. “S_BA_Account” or “S_BA_Mailbox”). Update: Predict all Services 1:1 as in the training data and do NOT use an aggregated EDA_OTHERS category, as was wrongly stated in the Hackathon Intro.

2. Which manuals of the provided how-to manual list would fit? Try to find max. 4 matches per incoming E-Mail.

[Task 2] Service-Merging Strategy

Find a solution for the evaluation of the best possible services to merge with each other into a single service.

Try to come up with ready-to-use code that benchmarks different combinations of 2 or more services and show the resulting differences. Try to run automatic tests of one or more merges in parallel. 

You can use any programming language.

[Task 3] Bonus Challenge: Clustering to find new Manuals

If you completed the first parts of the PoC you may try to solve the following question:

Which manuals are missing on the list? Try to perform a clustering of the E-Mails, so that new manual categories can be identified and the Helpdesk team can create such how-to manuals.

Input Data: Support Mails (cf. 2.2 Data)

• [Task 1] F1 Score
  For each category "IT-Services" and "How-To Manuals" the team with the best F1 score (Micro F1-Score) gets 7.5 points. The other teams get points proportional to the best F1 score. A maximum of 15 points can be achieved.
• [Task 1] Tools & Methodology
  The jury evaluates the choice of tools and methodology for solving the challenge. This can yield an additional 5 points.
• [Task 2] Architecture, Methodology, Output
  The jury evaluates the choice of tools, methodology and output for solving the challenge. The originality of your strategy and the meaningfulness of your service-merging strategy will be rated. A maximum of 15 points can be achieved.
• [Task 3] Originality of Clustering
  The jury evaluates the originality of your strategy and the meaningfulness of your automatically generated categories. This part may yield up to an additional 10 points.
• Presentation
  The quality, timing and originality of your final presentation will be assessed by the jury. This can yield an additional 5 points.