DataArt is a trusted technology partner that can help build efficient, automated, and highly accurate systems using modern AI technology.
What We Do
Automate the decision-making routine and forecast events with probabilistic analysis, and user personalization
Advanced texts, speech, and cognitive analytics. Structured and unstructured data. Chatbots
Visual classification of object nature, image recognition, and real-time video processing
Advanced data analytics, clustering, pattern detection, statistical analysis, and data visualization
Why Work with Us
Artificial Intelligence and Data Science project methodology is significantly different from traditional research for software delivery projects.
It requires companies to:
- Develop new data science and AI skills (such as NLP, computer vision, machine learning, deep learning, etc.)
- Build new infrastructure for big data and model deployment (often cloud based)
- Adopt new culture of collaboration between the business and data scientists
DataArt can help to bootstrap AI capabilities, or fill data and analytics gaps for companies that do not have the expertise internally or do not want to hire new talent until the benefits of AI are proven.
DataArt focuses not only on research, but also on delivering end-to-end solutions starting with solution design and ending with deployment of ML-model and integration into the existing or newly developed client environment.
Data Acquisition & Understanding
- Building data pipeline
- Setting up environment
- Data wrangling, exploration & cleansing
- Feature engineering
- Model training
- Model evaluation
How We Work
Our main value is to deliver valuable and cost-effective solutions to our clients. That’s why we developed an approach to R&D projects that allows us to see the progress at every stage and deliver solutions incrementally, allowing clients to decide if additional efforts are worth investment or a change of direction is required.
- Research applicable datasets in terms of data volume and set of fields; create ETL
- Test different ML models, algorithms, libraries
- Chose most appropriate dataset, model and model parameters
- Prepare ML model for a simulation with production data
- Elaborate on a suitable integration approach
- Prepare and integrate a production ready ML model
- Optimize and improve the model with new production data, weights, parameters
- Improved model rollout
- Support and minor enchancements
- Effectiveness monitoring