Hi, I’m Gabriele.

I’m the founder and lead A.I. and M.L. engineer of G.M.S.C. Consulting, an A.I consulting firm whose mission is to enable small and medium software companies to ride the unavoidable A.I. revolution, rather than drown in it.

This page will tell you more about who I am, what I did, and what I can offer you and your company.

Here’s a brief portfolio of Machine Learning projects I worked on

Here at G.M.S.C. Consulting, we will help you ride with the wave, in this case, the unavoidable AI revolution, rather than drown in it.

My Recipe to Delivering Extraordinary Value to Our Customers

After 3 STEM degrees and having completed many successful projects as lead machine learning and A.I. engineer, I found my 6 points recipe that delivers extraordinary value to my customers.

Want to work with us?

Did you say languages?

Here’s a list of programming and natural languages that I use

Programming languages

  • Python: Master

  • C++: Expert

  • Matlab: Expert

  • .Net: good

Operative Systems

  • Debian (Ubuntu): Proficiency with both GUI and Terminal

  • Windows: Good

Natural Languages

  • Italian: Native Speaker

  • English: Proficient Speaker

  • Chinese Mandarin: Traveling and basic work interactions

  • Spanish: Traveling and basic work interactions

Let’s talk about Tech Stack

Python is my language of choice for all AI related projects.

Python provides a vast amount of ML libraries that make any ML project much easier to build.

A few major examples are Spacy (a library for Natural Language Processing), H2O (Auto Ml), Tensorflow (Low level Neural Networks library), Scikit-Learn (a collection of traditional and novel machine learning models), SciPy (Signal processing), Nltk (Natural language Processing), Pandas (Data Science)

Deployment & DevOps

The deployment of ours ML solutions mostly fall into 3 categories:

  1. Cloud computing.  The ML solution is packaged in a docker container and exposed to the other cloud service by RestApi or Message Processing. DevOps are managed by Kubernetes + CICD (ex: Jenkins or code pipeline) + git

  1. Local computing - separate program.   The ML solution is once again packaged in a docker container which interacts with other running applications by Socket and IPC. DevOps are managed by CICD (ex: jenkins) + git

  2. Local computing - same program.   The ML solution is wrapped by a C or C++ layer thus becoming a C library that can be embedded in almost any application. DevOps are managed by CICD (ex: jenkins) + git

Book a call with me.

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