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Machine Learning and Data Science

DataArt is a trusted technology partner that can help building efficient, automated, highly accurate systems using modern AI technology.

What We Do

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Predictive and Recommendation Systems

Automating decision-making routine, forecasting events, probabilistic analysis, user personalization

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Natural Language Processing

Advanced texts, speech and cognitive analytics. Structured and unstructured data. Chatbots

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Computer Vision

Visual classification of objects nature, image recognition, real-time video processing

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Data Mining and Analytics

Advanced data analytics, clustering, pattern detection, statistical analysis, 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.

Our Approach

Business Understanding

Data Acquisition & Understanding

  • Building data pipeline
  • Setting up environment
  • Data wrangling, exploration & cleansing


  • Feature engineering
  • Model training
  • Model evaluation


  • Scoring
  • Performance
  • Monitoring
  • Support

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.

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Phase 1.1

2–4 weeks

Feasibility study

  • Research applicable datasets in terms of data volume and set of fields; create ETL
  • Test different ML models, algorithms, libraries.
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Phase 1.2

1–3 months

Building PoC

  • Chose most appropriate dataset, model and model parameters
  • Prepare ML model for a simulation with production data
  • Elaborate on a suitable integration approach
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Phase 2

Depends on the project

Going live

  • Prepare and integrate a production ready ML model
  • Optimize and improve the model with new production data, weights, parameters
  • Improved model rollout
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Phase 3


  • Support and minor enchancements
  • Effectiveness monitoring


DataArt engineers work with the most popular modern technologies including world-leading cloud based MLaaS solutions and classic or deep learning open-source libraries.

MLaaS integration and training

  • aws
  • google-cloud-platform
  • ibm-watson
  • microsoft-azure

Bespoke solutions based on libraries and technologies available on the market

  • tensorflow
  • keras
  • pytorch
  • scikit
  • numpy
  • scipy
  • yolo
  • spacy
  • spark
  • python
  • r
  • docker

Articles and Case Studies

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