Before starting a machine learning project, we need a clear and well-defined goal. It is necessary to define the problem we want to solve as well as its business value. In addition, we check whether we have all the data needed to solve the specified problem, how the data is delivered and how it should be governed. In the rapidly evolving world of AI, it is also important to research whether we can build upon previous academic research.
Data exploration and analysis
Data is the fuel of artificial intelligence, which is why we need to know your data, inside and out. We search for the best machine-readable representation for our machine learning model, after which we use known statistic methods to normalize data, detect outliers and fill in missing values. Furthermore, we document our findings in a report to improve the quality of your data. If necessary, we also annotate the data for the model building step.
Our AI engineers constantly stay up to date with the latest developments in machine learning. Each project we carefully select the most suitable algorithms and architectures to create an efficient and performant solution. These solutions range from using existing AI services to building a fully custom model tailored to meet your needs. This model is trained with your data and objectives in mind, and will be evaluated on relevant, real world data.
experience & interface
Because of the ever-evolving nature of data, any worthwhile AI model needs human validation for periodic training. And of course, you want to know exactly how well your new top employee is performing. Therefore, we use the latest technologies to create interfaces that bridge the gap between human and machine. Based on the feedback of your domain experts, our interfaces evolve to make sure that the end result is efficient to use.
Integration and deployment
Once development is complete, it's time to put our brains in a jar. In this final phase, we ensure that our application is completely ready to run independently. We provide the integration of the application with the customer's system and ensure its portability and scalability using technologies like Docker and Kubernetes. Finally, we deploy your application either on-premise or on any of the major cloud platforms.
Both AI's and HI's (Human Intelligences) make mistakes, which is why we believe AI systems should team up with humans.
While AI should take care of annoying and repetitive work, an employee can check and adjust the AI work where needed. This allows our applications to work reliably in any environment. The employee's feedback is used to continuously improve the models, guaranteeing performance in the long run.
It is certainly not our intention to replace people but to shift their focus to value-creating activities and hand over unpleasant activities to AI.
AI is more than just AI
A good AI application is more than just a model. With our end-to-end approach, we take care of the entire process: starting from an initial concept, we start exploring the data and the possibilities. More than just building a machine learning model we focus on the data pipeline, front-end and back-end. And of course, we deploy the final product in the cloud or on-premise.
Even without any available data we can still build an AI model, this is commonly known as a "Cold Start" scenario. Initially human feedback is quite common, however over time the AI model will require less correction until it can function almost completely independently.
The right tools for the job
We believe that each project requires a unique approach. Based on the needs of the customer, we choose the best possible solutions for each project and problem.
The philosophy of "use when possible, build when necessary" is central to this approach. We use pre-built services or products where possible to reduce costs and development time.
When the use case requires a customized solution, we use our extensive domain knowledge and experience with frameworks such as Tensorflow and PyTorch to build powerful custom AI models.