The first Apple Developer Academy opened in Brazil in 2013, with the goal of providing the tools and training for aspiring entrepreneurs, developers, and designers to find and create jobs in the thriving iOS app economy. Since 2013 Apple has opened more than a dozen academies across the world with two more on the way: one in Korea, and one in Detroit. The programme has empowered students around the world with app development and entrepreneurial training, many of whom have gone on to start their own businesses, create and sell apps on the App Store, and give back to their communities.

Across the world, whether students are in Naples, Italy, or Jakarta, Indonesia, academy participants learn the fundamentals of coding as well as core professional competencies, design, and marketing, ensuring graduates have the full stack of skills needed. The curriculum also incorporates Apple’s values throughout the coursework, encouraging students to design inclusively and make a positive impact in the world. Apple offers two distinct training programmes as part of its academy: 30-day foundations courses that cover specific topic areas.

Applications for the first academy cohort in Detroit — Apple’s first Developer Academy in the US — open this week. The academy, part of Apple’s Racial Equity and Justice Initiative, is scheduled to open this October in a newly redesigned space in downtown Detroit. All Michigan residents 18 and over are welcomed to apply, regardless of prior coding experience, at developeracademy.msu.edu/students.

Antonio Chiappetta; Photo Credits: Apple newsroom

This is Antonio Chiappetta, 25, who was part of the very first Apple Developer Academy cohort in Naples, Italy, in 2016. A computer engineer by training, he was inspired by the hands-on learning opportunities and close collaboration with mentors he met at the academy. Antonio credits his time at the academy with helping him gain the skills and confidence that power his career today:

“The Apple Developer Academy is not just about learning to code. It’s about breaking barriers and empowering students, and has infused me with the enthusiasm and curiosity that drive me today.”

Arthur Motelevicz, Photo Credits: Apple newsroom

For many academy graduates, the community aspect is as important as the hard skills they learn through their coursework. Students have the opportunity to learn from new colleagues from different personal and professional backgrounds, each of whom brings new ideas and life experiences to their app designs and business plans.

Arthur Motelevicz, 36, met his future business partner at the Apple Developer Academy in Curitiba, Brazil, in 2013 — and now counts 11 academy graduates among his 25 employees. After graduating from the academy, Motelevicz decided to dedicate himself to entrepreneurship in pursuit of a new way to mix coding with music via  Super Pads, a DJ app.

“I love being able to work with amazing people, learn every day, and experiment with combining coding and music. The Apple Developer Academy gave me the strength to pursue and implement my ideas into something that I will spend my life working on.”

Aisyah Widya Nur Shadrina; Photo Credits: Apple newsroom

Aisyah Widya Nur Shadrina, 23, and her all-women team used the front-facing camera and machine learning on iPhone to create Hearo, an app that can translate sign language into voice and text, providing a more seamless way to communicate with deaf people. This year, Shadrina and her team will attend Apple’s Worldwide Developers Conference virtually. She says she looks forward to learning more about new Apple technologies, frameworks, and tools that can help developers build more innovative and inclusive apps.

Apple’s annual Worldwide Developers Conference (WWDC) will take place June 7 through 11, in an all-online format. WWDC will feature hundreds of sessions for developers of all ages and backgrounds — including hundreds of current and former Apple Developer Academy students — to learn about the new technologies, tools, and frameworks they rely on to build innovative and platform-differentiating apps and games.

Can you tell us more about Parkinc.?

Christian: When we were brainstorming ideas and how to improve the city life, because the topic of the hackathon was Smart Cities, we came up with this idea, because it’s really close to us. As we know, in Sofia there is a big problem with parking spaces, especially in the downtown area, because they are reserved for the ones who live or work there. So our idea was to make the process of finding a parking space much easier and faster, so people don’t lose their time, energy and money and pretty much – make lives easier.

How does it work from a more technological point of view?

Stefan: Basically me and David focused on the ML model, using PyTorch. We feed an image, taken from a stationary angle from a CCTV camera, then we would pick out spots from legal parking spaces and feed this to a binary classification model, which would evaluate whether or not the space is occupied or vacant. We designed this to connect and update the counters for vacancies through a backend SQL server, which we had connected to a web socket to our website that Stoyan had made.

How do you develop such a thing in just two days? Was it hard?

Stefan: Basically David and I did a huge Crash Course on the PyTorch library and it was a very rudimentary model since this was my first time making a successful convolutional neural network. But once you have the basics done, you just have to play around with the parameters and get your model to work at an accuracy that you desire. For our case it was around 85%.

David: Also, because it’s a binary classification model it’s really easy to make it very close to a 100% accuracy, because it has only two outcomes and if we had more data, pictures of cars and vacant parking spaces, we could have done this model to work at 99% accuracy.

And how do you test the whole thing?

Stefan: In ML you have the three splits – the test set, the validation set, and the training set. The validation and training set work with each other while the testing set validates the results, using loss functions for training and validation. It’s all in the code and for us it is kind of intuitive.

Were there any challenges during the hackathon?

Stefan: Finding the data set and normalizing it for our standards. Originally, we found a data set, but it was not in the proper resolution to feed into our model and I had to resize all the images, because build-in library functions were not working for resizing. Those were the issues with the model.

David: There was also an inconvenience that some of the libraries that we used for the model like the Python libraries and everything else that was required for the environment, worked together with certain versions, so if there was a newer version of a particular library that is used in the software, it did not want to work with the other parts of the project, because it was not implemented for that yet. So we had to find the correct versions, the data set and normalize it. For us they had to be 150×150 and labeled too. So that was the challenge. Our model is 85% accurate, because we could only find 12 000 images of free and busy parking spots. There was actually a way way bigger data set – about 150 000 images, but the images weren’t labeled and we couldn’t really use it for a ML model. And now here are the challenges from the web developers.

Stoyan: The only challenge that we had was how to send the user data from our web app to PyTorch and get back the output to the people so they know how many spaces are there. Initially we did a web app, because in two days it’s very hard to do a cross-platform, so to do an iOS and Android in that time is very hard. But we managed to do it all, and everything worked, the socket worked, so basically it’s a done project. We just need the cameras.

Eva, can you tell us about the design part of the project?

Eva: We worked on it together with Stoyan and I also worked on designing a logo. I wanted to implement a new logo for our app and for that I used Illustrator.

Stoyan: For the design I used mostly CSS and JavaScript to make it look really nice.

Eva: We also used jQuery for some parts.

Stoyan: If you’ve seen our design you know that it’s simplistic, but it brings a nice aesthetic feel.

What benefits do you see in your product?

Christian: Our whole business and economic model was built on the fact that our service would be free for the mass of customers, which would be citizens of the big european cities. We know that american cities were built for cars and car usage, so they don’t have the infrastructure problem with the parking spaces. For example european cities like Sofia, really struggle with this which is a real problem for normal commuters, who probably go an hour early to their workplaces, just to make sure they have a parking space free to them. Our whole idea would be to add value for the commuters by saving their precious time. It also saves stress, because they don’t have to worry if there’s a vacant spot for them and also as an added benefit, we believe that our app can drastically shorten the commuter time spent in cars around the city center areas, that will also improve the air quality at least to a certain degree.

Will you continue to work on Parkinc. as a group? And what future do you see in the product?

Christian: Our product for now is very unique, because I think there is only one project like this, which is completed, but only in the Netherlands. So there is really a big gap for such products to start and actually provide value to the community. To continue our project we need funding, because the software is there and working – front-end, back-end and etc. Everything is nicely set, so maybe we only need bigger data sets and the infrastructure of CCTV cameras. When we were considering our business model, we did research on CCTV cameras and how expensive they are, and we concluded that with good funding we can make our project work in the foreseeable future. So the only thing for now that is stopping us from continuing improving and working is basically funding.

Were there other projects on the hackathon that you liked?

Christian: Personally, it was the project GeoShare that won the hackathon. The other one that I liked was Udoma – they won the second prize. I think that both of them can in a way communicate with our project and make a city really smart and efficient.

What do you think about the startup ecosystem in Bulgaria?

Christian: I think that here we have the environment to actually do startups. There are a lot of unexploited markets and fields of improvement. We definitely have the human capital of smart IT guys like my colleagues, who are able to build sophisticated ML Algorithms and code in a short period of time. We have the technical skills, we have the space to actually try something new. But the only problem in general would be the funding, because as far as i know there is no state funding for startups and any big commercial fund that would actually invest in startups in Bulgaria. But – yeah. Everything is here, except maybe money and publicity.

Final words from you guys? A conclusion?

Stefan: Basically I gathered these people for their talents. Two business model developers, two web developers and two backends, including myself. We all contributed and we ended up developing our idea to the specifications and to the scope of what we intended. If we had developed too niche of a problem, it wouldn’t have made sense economically.

Ain’t that the most important thing, that you’re not just developers? You all have a set of different talents.

Stefan: Indeed. We have to take into account all the aspects of the project and not just the development. Because David and I were part of the same team who got third place last semester. But we only did the development part and bombed the entire economic and design portion, because it was our first time attending.