In this post are available a list of links where my work has been republished or acknowledged in different areas (e.g. different languages, publications, companies and universities).
Articles
Sample list of published articles which have then been republished in different languages:
- Tech4Future Profile (Italian)
- Changes Unipol Profile (Italian)
- La teoria dei giochi nell’intelligenza artificiale (Italian)
- Uczenie maszynowe online z użyciem Tensorflow.js (Polish)
- MLOps のための機械学習におけるデザインパターン (Japanese)
- 面向 MLOps 的机器学习设计模式 (Chinese)
Publications, Companies and Universities
Sample of mentions from publications, companies and universities:
- Google Scholar Profile
- Towards Data Science: Editorial Team Bio
- Towards Data Science Monthly Edition: Art, Creativity and Data Science
- Towards Data Science Daily Picks
- Towards Data Science The All-time Best Guides to Data Science Writing
- Towards Data Science Monthly Edition: Questions on Explainable AI
- Towards Data Science Monthly Edition: Writing Better as a Data Scientist
- DeployPlace: Top 3 DevOps trends of 2020 in the eyes of real engineers
- Cornell University Recommended Reading: Game Theory in Artificial Intelligence
- Experfy: Harvard Innovation Lab
- Datacast: Building Data Science Projects
- Global AI on Tour Conference Speaker
- Microsoft Learn Student Ambassador
- Microsoft Tech Community Blog
- Daniel Bourke Machine Learning Monthly August 2021: Video and Article
- ITWeeklyNewsletter - Paradoxes in Data Science
- Kaggle - R Learning Path Information For Beginners
- AWS AI/ML Community attendee guides to AWS re:Invent 2021
- SAS Institute - Virtual Event for the UK Government
- SAS Institute - Visual Model Development Webinar
- Codemotion 2022 - Paradoxes in Data Science Talk & follow up article.
- Open Data Science Articles: Causal Reasoning in ML, Azure for ML Engineers, Paradoxes in Data Science
- Anaconda Cloud - Ten Techniques for Machine Learning Visualization
- Google Cloud Credential Holder Directory Profile
- Kili Technology: Supercharging Your Machine Learning Models with Data Augmentation, Synthetic Data: The Ultimate Guide to Artificial Intelligence’s Best Kept Secret, Foundation Models and LLMs: a Complete Guide, How to Train Computer Vision Models on Satellite Imagery
- Institut Fourier - Poster Presentation
- Data Pizza - Data Engineer Resources
- Quix Ghostwriting - Apache Kafka vs Apache Flink: friends or rivals?
- Datacamp - Introduction to Autoencoders: From The Basics to Advanced Applications in PyTorch
- O’Reilly - Automating Data Quality Monitoring at Scale Book Review
- MSV Incognito Presentation
- Google Cloud Medium Blog - Human in the loop and Google Search with Langgraph
KDNuggets Silver and Gold posts:
- KDNuggets Profile
- How to Optimize Your Jupyter Notebook
- Probability Distributions in Data Science
- Understanding Cancer using Machine Learning
- Roadmap to Natural Language Processing (NLP)
FreeCodeCamp Top Contributor (2019/2020):
Open Source
Python Open Source Contributions and mentions:
- NVIDIA RAPIDS: GPU Accelerated Data Analytics & Machine Learning Tutorial
- Awesome Streamlit: Kaggle Mushrooms Dashboard
Tweets
SQL is one of the most requested skills in Data Science. @Pier_Paolo_28 walks us through how it can be used in Data processing and Machine Learning using BigQuery. READ 👉 https://t.co/QzH9eYzu8r via Towards Data Science
— Kaggle (@kaggle) September 3, 2019
Explore different approaches to #machinelearning projects in Python and learn how to benchmark their execution speeds in @Pier_Paolo_28's latest blog post, which features #TITANRTX and @rapidsai: https://t.co/gvdQI7jICq pic.twitter.com/C5aLtrdULa
— NVIDIA AI (@NVIDIAAI) November 25, 2019
So you've trained a model using simple Machine Learning algorithms, but what next?
— freeCodeCamp.org (@freeCodeCamp) September 15, 2019
Time to make that model useful in the real world.
In this tutorial by @Pier_Paolo_28, you'll learn how to deploy Machine Learning models on mobile & embedded devices. https://t.co/BATiDuLyq3
“RAPIDS lead to a consistent decrease in execution time. This can be greatly important when working with large amounts of #data...” says Pier Paolo Ippolito, AI Enthusiast, Data Scientist, and RPA Developer. https://t.co/zzYlUCPEK7 pic.twitter.com/rzmn50gQMH
— RAPIDS AI (@RAPIDSai) July 25, 2019
Check out this repository that uses Streamlit to analyze a Kaggle 🍄dataset. Thanks for building it @Pier_Paolo_28! 👇#MachineLearning #DataScience https://t.co/7K1m24nL2N
— streamlit (@streamlit) October 16, 2019
Contacts
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