We build robust and scalable Machine Learning solutions. We automate data processing and develop models and algorithms that become our clients' core IP. Knowing that the ML project can be complex and unpredictable, we lead you through it with a fully-managed process.
We work with a client to achieve a high-level overview of the ML application and classify possible technical areas considering approaches to building the infrastructure and developing the ML model.
We don't reinvent the wheel: we identify state-of-the-art research papers for related problems and analyze the strengths and limitations of the described approaches.
We identify the most efficient approach based on the problem, the research, and constraints (such as a limited data set). We don't strive for the fanciest solution: sometimes, a heuristic may be a better choice than sophisticated deep learning.
The importance of a good data set is quite often underestimated. It is often expensive and time-consuming to build. It is also difficult to manage it and ensure the data is consistent. We bring creativity in designing data augmentations and experience in data governance and management.
It's common to validate dozens of model variations and run hundreds of training experiments. We combine ML skills with engineering excellence and passion for automation to keep this dynamic and highly experimental work under control.
ML research results are only useful if they deliver business value to users. We deploy the models and expose APIs that other engineers can use without any knowledge of ML.
Machine learning projects are all about experimentation, rapidly testing various ideas, some of which are successful and others leading nowhere. We run hundreds of experiments with different hyperparameters, model variants, and training sets. After several months we need to identify which experiment performed best and which model variant was used there.
Usually, things get even more complex, as systems often use not one but a chain of linked models. We can control it only when following a well-defined, strict process supported by proper tooling - our ML Ops framework.
All the data is divided into datasets and stored in a central repository. Data management and maintenance are straightforward, and different ML models can be trained and validated on identical data samples for easier comparison.
ML Model Engineering
We put ML models under version control. All the pipelines are automated, and test environments are customized and isolated. It makes experiment results reproducible, so we can focus on running more experiments and developing the most promising approaches.
Comparing Experiments Results
Performance metrics defining the ML model's business usability are automatically collected and uploaded to the visual analytic dashboard. All the stakeholders have access to it, which speeds up choosing the approach we want to pursue.
Model deployment is automated and supports various integration and testing techniques.