Providing machine learning consulting services, we start with goal setting and analysis of business processes, then we help companies see machine learning capabilities matching their business goals.
By leveraging strong machine learning expertise, we handle all data-related processes, including data collection and preprocessing, to prepare datasets for effective modeling. We choose/create an optimal ML model, evaluating its accuracy to deliver production-ready solution tailored to specific business needs.

Our experts develop machine supervised, unsupervised and reinforcement learning solutions to deliver guaranteed value to clients.


Time Series Forecasting

With machine learning models trained on historical data, the tasks that demand forecasting, price predictions, seasonal fluctuations, and trends are solved much easier. Time series forecasting should become an integral part of the workflow for companies operating on markets with high volatility.

  • Time series classification
  • Confidence interval estimation
  • Stock market prediction
  • Time series clustering
NLP (Natural language processing)

CodeIT Machine Learning specialists create applications to recognize, process, and interpret unstructured written and oral natural language to extract meaningful information and value. We help companies from various industries improve their daily workflows using NLP outcomes.

  • Machine translation
  • Question answering
  • Sentiment analysis
  • Speech recognition
  • Semantic analysis
Computer vision

Based on machine learning algorithms, image analysis solutions can understand visual content and extract relevant information pertaining to object identification, recognition, and evaluation.

  • Face recognition and modeling
  • Biometric verification
  • Image reconstruction
  • Video analysis
  • Activity recognition
  • Gesture and emotion recognition
Recommender Systems

Leveraging machine learning capabilities, our data science team implements recommender systems critical in delivering personalized customer experience. Recommender systems are indispensable for e-commerce sites due to their ability of offering relevant suggestions to users.
Thus, we help companies win their customers’ loyalty, significantly increase conversions and boost revenue.

  • Collaborative filtering
  • Content-based filtering
  • Hybrid recommender systems
Predictive Analytics

Predict market trends, behavior patterns, product sales, and more by extracting and analyzing past and present data. Predictive analytics will help predict and prevent potential issues, assess risks by ensuring better decision making.

  • Predictive models
  • Descriptive models
  • Decision models
Anomaly Detection

The goal of anomaly detection is to identify unusual objects, events, or behavior in datasets that differ from the majority of data. The application of anomaly detection techniques is of great value in detecting intrusions, fraud, security issues, text data anomalies, health problems.

  • Unsupervised anomaly detection
  • Supervised anomaly detection
  • Semi-supervised anomaly detection

Technology stack

tensor flow Tensor flow
scikit learn Scikit Learn
keras keras
python python
matplotlib Matplotlib
Jupyter Notebook Jupyter Notebook
Pandas Pandas
Numpy Numpy

Machine Learning Workflow

We can work at some stages in parallel

1. Business task understanding

  • Define project requirements
  • Define task objective

2. Data Exploration

  • Discover data insights
  • Plot graphs, visualization
  • Domain knowledge reception

3. Data Preparation

  • Data cleaning
  • Feature extraction
  • Feature selection
  • Build pre-processing pipeline

4. Modeling

  • Choose model
  • Define loss function and metrics
  • Parameter tuning
Being an iterative process, ML model development implies taking a step back to model accuracy or changes in the solution approach when getting results/performance scores.

5. Evaluation / Validation

  • Performance scores estimation
  • Validation and test datasets evaluating
  • Cross-validation
We can work at some stages in parallel.

6. Deployment

  • Continuous model delivery
  • Prepare production-ready solution
  • Metrics logging
  • Performance monitoring
Being an iterative process, ML model development implies taking a step back to model accuracy or changes in the solution approach when getting results/performance scores

Industries we work with

real estate


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