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dida builds custom AI solutions with Google Cloud

dida builds custom AI solutions with Google Cloud


As enterprise demand for complex automation grows, so does the gap between what organizations want from ML solutions and what off-the-shelf black-box products can deliver. dida specializes in developing custom AI solutions for medium to large enterprises. With academic backgrounds in mathematics and physics, our highly specialized team excels at taking an abstract approach to complex problems and building elegant, realistic AI solutions for our clients. Our solutions are also modular by design, making them explainable and giving enterprises a clear picture of what is happening at each step of the process.

Energy of the future and bottlenecks of the past

Our unique combination of math and machine learning skills was put to great use when we used Google Cloud to build a custom AI solution to automate parts of Enpals' solar panel sales process. With growing demand for environmental sustainability solutions in Germany, Enpal was experiencing rapid growth and was on its way to becoming the country's first greentech unicorn. To keep up with this growth, Enpal needed a more efficient way to create quotes for potential customers. In this process, salespeople input satellite images of customers' rooftops into a desktop application, estimate the roof angle, and manually count the number of roof tiles to determine the roof size. Salespeople use this estimate to calculate how many solar panels the customer needs and create a mock-up of the solar panels on the roof.

The process took a total of 120 minutes for one sales rep to complete and was difficult to scale as the business grew. Manually counting roof tiles and predicting roof angles was inaccurate, prone to error and resulting in inaccurate cost and energy production forecasts. Enpal wanted to develop a custom AI solution to automate the process to reduce inefficiencies and increase accuracy. Recognizing the need to combine AI expertise with mathematical problem solving, Enpal turned to us.

Train robust ML models efficiently

At dida, we are platform agnostic to accommodate individual customer preferences, but whenever we have a choice, we build our solutions on Google Cloud. As a comprehensive platform with a wide range of tools for AI developers, Google Cloud is easy to use and cost-effective because you only use what you pay for. When developing the Enpals solution, we broke the process into a series of small steps that allowed us to build a modular and explainable solution. We used Google Cloud solutions for almost every step.

The first step was to collect enough data – rooftop images – to build a robust machine learning model. We used the Google Maps Platform API to collect images of rooftops of all shapes and sizes to train our model. All of these images were stored in Cloud Storage, where automatic storage class transitions allowed us to control costs.

We used these images to build an initial baseline model to prove that the concept worked. This included having the model distinguish between roofs and other features, and understand where obstacles like chimneys or skylights would prevent solar panels from being installed. As we developed the model, we used Cloud Build's CI/CD pipeline to run experiments and tweak parameters to build a functioning working model. Using Cloud Build allowed us to maintain a continuous development cycle, making the process more efficient, and we were able to build the baseline model in just four weeks.

Taking an abstract approach to a real-world problem

Next, we needed to spend time finding the right formula to accurately calculate the angle of a south-facing roof. For this, our team relied on our mathematical problem-solving skills and applied projective geometry to build a model that could calculate the correct angle based on a photo of the roof provided by a potential customer. This mathematical technique, combined with the ML model, resulted in an automated process for calculating the roof area and added two more steps: one to calculate the number of solar panels required and another to visualize the placement of the solar panels on the roof.

We used Compute Engine throughout the ML model training process, adding GPUs to our virtual machines to accelerate our workloads and provide high availability. Compute Engine gave us the flexibility to quickly scale our usage up and down as needed, and allowed us to pay only for the compute power we needed. As we fine-tuned our models, we used TensorBoard to monitor each individual training process to help us evaluate our model performance.

Automate your way to a faster, more accurate sales process

The entire development process took six months, but at the end of it, Enpal had a custom automated solution that allowed them to quickly and easily assess roof size and the number of panels needed. The solution was also modular, giving Enpal full visibility into how the solution was working and allowing them to manually adjust details like the exact dimensions of the roof along the way to ensure the results were as accurate as possible. The performance metric they used to measure the accuracy of rooftop detection was called Intersection over Union (IoU). After training, optimizing, and post-processing the model, the IoU reached 93%.




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