Project Partner Case Study: Entomics
We’ve been working with project partner, Entomics, for the past six months. In this time they’ve worked with Cambridge Spark alumnus, Gergely Hantos, on an industry-specific capstone project provided by the organisation.
The capstone project explained
At the end of the Cambridge Spark Applied Data Science Bootcamp, students have the choice of working with one of our partner organisations on a real-life industry problem, or applying their skills to their own projects. This allows students to evidence the skills employers look for by building their portfolio up.
Entomics Biosystems and the capstone project
Entomics develops targeted value-added engineering and biological solutions for optimising the insect-based bioconversion process of organic waste into three sustainable ‘fuels’ for plants, animals and vehicles. This is done by using an insect called the Black Soldier Fly (BSF) as a conversion engine.
Entomics is developing a fully automated engineering solution that will conduct waste transformation without the need for human interaction. To achieve this technological optimisation, the most important variable to determine and predict is the daily waste input. This target variable is a function of several other input variables such as:
- Temperature (continuous value)
- Humidity (continuous value)
- Insect activity / movement (category)
- Insect maturity (category)
- Insect health (category)
To help drive development of this automated solution, students are given two tasks:
- Develop a predictive model able to beat the rules-based system by using historical data instead of human intuition.
- Could the categorical variables be inferred via image recognition instead of manual human input?
This project was driven by the fact that the current process depended on humans performing this classification daily using the inputs and intuition to decide the appropriate amount of waste to be fed, as opposed to taking a data-driven automated approach.
Hear more about the capstone project available to Applied Data Science bootcamp students from the Co-Founder and CTO of Entomics in the video below.
Hello my name is Fotis Fotiadis, I’m the CTO of Entomics, and here at Entomics, we transform organic waste into high-value animal feed, using insects as a bio-conversion engine. We’ve been a project partner with Cambridge Spark for about 6 months now and we’re very excited to be working with Cambridge Spark because the students delivered really good quality work.
“…the student had just learnt how to apply machine learning and how to work with certain frameworks and methodologies and they immediately applied that to a real-life scenario, and it worked…”
Working with Cambridge Spark has been a very enjoyable experience, to date. I think it’s very useful for the students because they get to apply what they’ve learned on the course into a real-life scenario. In our case, the student had just learnt how to apply machine learning and how to work with certain frameworks and methodologies, and they immediately applied that to a real-life scenario, and it worked, so it seemed at least to be very beneficial for the student, and that’s the feedback we got from them as well.
At entomics we use datasets from feeding records, to frequency of feeding per day, to lifecycle and life growth of the insects, and various other biological data that we need to monitor, track and elaborate and work with to draw results.
“…we decided to work with Cambridge Spark because we’re looking to hire Software Engineers at this stage that are specialising in machine learning and Cambridge Spark, through the course and the project, seemed the ideal partner…”
So we decided to work with Cambridge Spark because we’re looking to hire Software Engineers at this stage that are specialising in machine learning and Cambridge Spark, through the course and the project, seemed the ideal partner to give the students an exercise, see how they do, and if they do well, give an opportunity to them to join the company.
Our previous student worked on Machine Learning and Database Analytics where they used data points and data amassed from entomics around feeding records and around inspection data to create a predictive algorithm to see how much we would feed every day. We’re very satisfied with the work they did and, even though we just communicated with them in the beginning, they delivered a really good outcome, meaning they were well supported by Cambridge Spark and that’s why the outcome was so good. So it was real added value for us because we used the output immediately to our current proceedings, and the algorithm is working great, so i’m very happy overall.
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