Artificial Intelligence & Machine Learning

Computers can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation, and sometimes better than us.

Machine learning is a subset of Artificial intelligence that involves teaching computers to learn from data and improve their performance on specific tasks. In other words, instead of being explicitly programmed to perform a task, a machine learning algorithm is trained on a large dataset and can use that knowledge to make predictions or decisions about new data.

Like most companies, you're probably on a gold mine of large amounts of data collected over years that is ready to be put to work.



Is one of the most popular programming languages for AI and ML due to its simplicity, readability, and flexibility. It has several libraries and frameworks like NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch that are widely used.



Is an open-source machine learning library developed by Google that is widely used for building neural networks. It supports both CPU and GPU acceleration and has a large community of users and contributors.



Is an open-source machine learning library developed by Facebook that is known for its ease of use and flexibility. It has a dynamic computational graph that allows for more flexible model building, and it supports both CPU and GPU acceleration.



Is a high-level neural network library that is built on top of TensorFlow and designed to simplify the process of building neural networks. It has a simple and intuitive API and can be used for a wide range of applications.

Professional Services

Planning and Preparation

  • Identify goals and objectives
  • Discovery and analysis
  • Scope definition
  • Project plan


  • Data collection
  • Data processing
  • Model selection
  • Model training
  • Model evaluation

Optimization and Upgrade

  • Model deployment
  • Integration in the overall product
  • Monitoring and Maintenance