Artificial Intelligence & Machine Learning

Implementing AI and ML in Your Projects

At Softarex, the development of any system with AI capabilities starts with thorough research into the domain in which it will be used. After we define the exact way in which AI technology will be implemented, what data to use for data sets training, what results we expect to get, and what algorithms for data analysis we are going to use. Then we move to the implementation of the system using technologies and algorithms.

Machine
Learning

Data science systems development (AI, ML, Data analysis, and Predictive modeling) is based on the CRISP-DM approach (Cross-industry standard process for data mining) and divided into 6 main stages:

1

Business understanding (analysis of customer business need);

2

Data understanding (data visualization, feature exploration, target definition);

3

Data Preparation (data parsing, data preprocessing);

4

Modeling/optimization (preparation of model or optimization task -> training the model or performing task optimization -> preliminarily evaluation);

5

Evaluation (model evaluation, metrics computation);

6

Deployment (model deployment with the best metrics results).

Additionally, we use ASUM-DM (Analytics Solutions Unified Method, extension of CRISP-DM).

Approaches and algorithms

Softarex engineers use existing algorithms and develop new ones from scratch when it comes to solving unique challenges. Our data scientists use Amazon Machine Learning — a service that simplifies machine learning techniques for developers at all levels.

 

 

Approaches we use:

  • Learning Decision Trees and Learning Association Rules;
  • Artificial Neural Networks and Deep Learning;
  • Support Vector Machine and Ensembles;
  • Clustering, Bayesian Networks, and more.

Algorithms we use:

  • Decision Tree, Naive Bayes Classifier, Linear Regression;
  • Logistic Regression, Support Vector Machine, Random Forests;
  • Gradient Boosted Trees, K-means, C-means;
  • DBSCAN, EM-algorithm, Kohonen Neural Network;
  • Value Decomposition, Independent Component Analysis, and others.

AI, Computer vision and Data science

OpenCV, DLib, Tensor flow, CUDA, DeepDetect, OpenNLP, Storm, Spark, Hadoop, SystemML, Stanford Classifier, SmileMiner, DirectX, OpenGL, QT, Cognitive services from Amazon, IBM Bluemix and Microsoft Azure

  • Extensive experience in AI-based solutions design and implementation
  • Capability to implement AI and ML in different domains
  • Ability to understand tasks from different domains
  • Capability to develop new algorithms for solving unique tasks
  • Profound knowledge of applied mathematics
  • Employing PhDs in applied mathematics

Our Expertise in Technology Domains

Case Study