How Does Machine Learning Hackathon Differ From Other Hackathons?

What comes to mind when you think of a ‘hackathon?’

Maybe a room full of computer programmers and developers pounding on keyboards while the audience cheers — just like in the movie The Social Network, where the guys in the dorm drink shots while they compete to become Facebook’s first intern or like the hackathon scene from Korean drama Start-Up.

Though hackathons are not so dramatic in real life, they are revolutionising how companies build teams, innovate and validate ideas. Inarguably, hackathon challenges are cutting the learning curve of a lot of developers and programmers.

The pandemic has forced hackathons to take the virtual route. As per market research firm BlueWeave Consulting, the global hackathon management software market is expected to touch $292.2 million by 2026, growing at a CAGR of 8 percent during the forecast period (2021-2026).  

The advantages of an online hackathon platform/hackathon management software include:

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• Less overhead costs (venue, transportation, staff, etc.) 
• A larger and more diverse audience 
• Expect better quality deliverables when the challenges go on for a longer duration. 
• Improved chances of success using social media (sharing, reach and votes) 
• Managing and coordinating with teams and participants becomes much more accessible.
• Community and brand building

Rise of ML ‘hackathon’ platforms  

Machine learning hackathons are quite different from general coding or product-focused hackathons. Machine learning hackathons focus on building a tangible product with higher accuracy performance measurements, alongside training and comparing with existing models. 

Online hackathon platforms like MachineHack and Kaggle, have become the go-to platforms for machine learning developers, researchers and enthusiasts, essentially providing them with necessary assistance, support and tools to experiment with datasets and solve multiple problems.  

Chetan Ambi, a technology lead at Infosys Ltd and the winner of several hackathon challenges conducted by MachineHack, said the hackathon platform has become his favourite playground to showcase his machine learning skills. “I am looking forward to more challenging problems in the future,” he added. Ambi has won many hackathon challenges, including author identification problems, prediction of data scientist salaries, doctor consultation fees, and flight ticket prices etc. 

Stavya Bhatia, senior analyst at Antuit India, said the machine learning hackathon platforms [MachineHack] not only enhanced his data scientist skills but helped him build contacts. Further, he said several individuals reached out to him to learn from his approach and vice versa. 

The same is not the case with typical hackathons, where the focus is more towards winning the cash prize. Plus, there is very little room for beginners. 

Machine learning steps 

Before we dive deep, let’s look into the process involved in solving machine learning problems. 

Some of the basic steps include: 

• Identifying the target and independent features 
• Cleaning the dataset
• Feature Engineering 
• Feature encoding and scaling 
• Feature selection 
• Check distributions of the target variable 
• Get insights from graphs for fine-tuning features 
• Model application and hyper-parameter tuning for feature
• Combining different models

Running machine learning hackathons are easier said than done as it involves solving multiple steps, compared to traditional coding or a product-focused hackathon. 

Further, the coding or product-focused hackathons are pretty straightforward in their approach. They involve steps like identifying problems, developing solutions, selecting themes, rapid prototyping, product-market fit, product roadmap, coming up with a future roadmap list, presentation, etc. An expert panel or human intervention is required to evaluate product deployment, teams’ overall presentation, revenue model, scalability, among others. 

On the other hand, machine learning hackathons, which validate accuracy performance measurements and the system’s capability to predict outcomes, is purely result-driven.

In other words, if the machine learning models developed during the hackathon challenge have to show good output in terms of accuracy and interoperability, to figure in the leaderboard.

Wrapping up

Over 80% of Fortune 100 companies today use hackathons to foster innovation. More than 50% of the hackathons are recurring events. Meaning they are a reliable tool for sustained innovation and identifying top-notch talent. Also, the parameters to assess a machine learning hackathon are unique, both qualitatively and quantitatively, compared to other hackathons. 

P.S: MachineHack, a machine learning hackathon platform from Analytics India Magazine, has conducted multiple hackathons on numerous problem statements across industries.

Stay tuned for updates.