Principal Component Analysis for Investment Management

Introducing new AI/ML tools for superior portfolio modeling

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

What We Do

Predictive and Recommendation Systems

Automate the decision-making routine and forecast events with probabilistic analysis, and user personalization

Natural Language Processing

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

Computer Vision

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

Data Mining and Analytics

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.

Our Approach

Business Understanding

Data Acquisition & Understanding

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

Modeling

  • Feature engineering
  • Model training
  • Model evaluation

Deployment

  • 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.

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
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
Phase 2: Duration 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
Phase 3

Support

  • Support and minor enchancements
  • Effectiveness monitoring

Technology

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

MLaaS integration and training

Bespoke solutions based on libraries and technologies available on the market

tensor
keras
IBM pyTorch
scikit
numPy
sciPy
YoLo
spaCy
spark
python
R
docker

The Power of Data

Our insights on modern data and analytics practices and on harnessing the power of AI, machine learning, and data science.

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Welcome
We are glad you found us
Please explore our services and find out how we can support your business goals.
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