Technology Stack

The tools we use to deliver our projects.

Main knowledge areas

Machine Learning

Classification, Regression, Ranking, Survival Analysis, Anomaly Detection, Clustering, Semi-Supervised Learning, Active Learning

Deep Learning

Convolutional Neural Networks, Recurrent Neural Networks, Transformers, Transfer Learning, Multitask Learning

Computer Vision

Image classification, Object Detection, Image Segmentation

Natural Language Processing

Text Classification, Named Entity Recognition, Text Summarization, Text Generation

Recommender Systems

Collaborative Filtering, Content-based models, Basket Analysis

Optimization

Linear & Integer Programming, Metaheuristics

Forecasting

Uni & Multivariate time series forecasting, anomaly detection

Technical tools

We cover a comprehensive range of tools. Below, we showcase some of the tools we use on a daily basis. In case you are using a different stack, contact us to validate if we can cover it.

Programming languages

The languages we use to speak with our machines on a daily basis.

Python
R
Data Analytics

Core analytics tools we use to bring intelligence to our projects.

Tensorflow
Keras
Pytorch
Scikit-learn
XGBoost
PySpark
Spacy
NLTK
OpenCV
HuggingFace
GPT3
RapidCanvas
Data Visualization

Numbers are hard to understand, they need to shine with a story.

Matplotlib
Seaborn
Plotly
Streamlit
Databases

Information needs to be preserved. We handle a wide variety of databases to transform raw data into actionable knowledge.

PostgreSQL
MongoDB
BigQuery
Hive
SQL Server
Snowflake
Operations

From conceptual consulting to production code, we handle a wide set of tools to bring your project to live.

Git
Docker
DVC
Jenkins
Apache Airflow
Prefect
FastAPI
Flask
Neptune.ai
MLFlow
Cloud & Edge

On the cloud, premises, or on the edge. You name it. Some environments we have used to deploy our projects.

AWS
GCP
Azure
Google Coral
Nvidia Jetson
Raspberry Pi
Intel Neural Compute Stick

Want to know how we are using these technologies in our projects?

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