Go beyond descriptive analytics. Get real competitive advantage.

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Advanced Analytics is the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations. This is the space commonly referred to as Machine Learning.

At Astrafy we focus on the following four Advanced Analytics use cases:


It refers to the task of assigning a label or category to an input data point based on its attributes. The goal is to learn a function that can accurately predict the class of unseen data points. Classification problems include image classification, spam email detection and medical diagnosis.


It refers to the task of predicting a continuous numerical value based on input data. It involves finding a mathematical function that can map the input variables to a continuous output variable, allowing the algorithm to make predictions on new, unseen data.


It focuses on teaching machines to understand and process human language. By applying computational and statistical techniques, NLP algorithms enable machines to analyze, interpret, and generate human language in various forms, such as text, speech, and image captions.


Recommendations refer to personalized suggestions provided by algorithms based on user behavior and preferences. It uses historical data and predictive modeling techniques to offer relevant and useful suggestions to users.



Our stack of predilection:

  • Vertex AI:
    One AI platform, every ML tool you need. Vertex AI brings together the Google Cloud services for building ML under one, unified UI and API. Vertex AI is natively integrated with BigQuery, Dataproc and Spark and with widely used open source frameworks such as TensorFlow, PyTorch, and scikit-learn.
  • BigQuery ML:
    BigQuery ML lets you create and execute machine learning models in BigQuery using standard SQL queries. BigQuery ML democratizes machine learning by letting SQL practitioners build models using existing SQL tools and skills.
  • Streamlit:
    Open-source app framework for Machine Learning and Data Science teams. We use it to create beautiful Machine Learning web apps in minutes; all in pure Python. No front‑end experience is required and it allows you to distribute your models via a nice UI to the different business stakeholders.

Our confort zone coding libraries:

  • Tensorflow:
    TensorFlow is a free and open-source software library for machine learning and artificial intelligence. Developed by Google, it is the most supported library on Vertex AI.
  • ScikitLearn:
    Is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

Why Astrafy ?

  • Our Advanced Analytics team is passionate about Machine learning and is always on the lookout for the newest tools.
  • The heavy workload has shifted towards MLOps with the focus of putting models into production in a fully automated way instead of developing models. We are early adopters of MLOps and master the art of automating the deployment of ML models.
  • Our Google Cloud expertise gives us an edge on using at best Vertex AI with other Google Cloud products.

Go beyond descriptive analytics