Xebia Articles

Giovanni Lanzani

Giovanni holds a PhD in Theoretical Physics, with research focusing on models describing DNA mechanics. Prior to joining GoDataDriven, Giovanni worked at the Software Quality department at KPMG As Data Whisperer Giovanni advices clients on how to extract valuable insights from (big) data analysis. Data driven products are developed using Giovanni's excellent understanding of python (especially Numpy and pandas), C++, C, Haskell, Javascript, Erlang, Hive, Pig, Impala, Hadoop, PostgreSQL, MySQL, Spark. On top of that, Giovanni is a certified Cloudera trainer for the Data Analyst, Developer, Spark and Admin Cloudera courses. Giovanni regularly shares his knowledge during conferences and other events, like meetups, including PyData Paris and NoSQL Barcelona.
Find me on:

Recent Posts

Applied Data Science - The new standard in data-driven business

Posted by Giovanni Lanzani on May 9, 2017 9:00:00 AM

Putting predictive models into production demands a streamlined workflow, and highly skilled data scientists and engineers. For the past few years, organizations have focused on developing strong proof of concepts and initial use cases. Now they must successfully introduce data-driven concepts into their daily operations. This year, data-driven companies will make productionizing data projects the priority and focus. Productionizing will increase exposure in other parts of the company, so data science teams will turn to proven methods, like microservices and APIs, to streamline their process. Data governance will become a standard part of any data project. Big Data technology has matured and is mostly enterprise-ready, increasing the pressure on and demand for data scientists. Major tech players like Google, LinkedIn, and Facebook will continue to open-source their product innovations in an early stage. In 2017, the gap between textbook data scientists and seasoned practitioners will widen further. Companies that retain, foster, and attract people who can apply data science in production will gain a significant competitive advantage.

Read More

Topics: Big Data & Data Science

Applied Data Science: Bringing models into production

Posted by Giovanni Lanzani on Dec 13, 2016 12:08:17 PM

In Data Science, software quality often is an issue that prevents models to hit production. Issues like no automated data pipelines (including how to make the results available to the outside world), bad quality of code, or not enough attention to non functional requirements (like performance) are showstoppers for applied data science.

How can you successfully bring data science models into production?

Read More

Topics: Big Data & Data Science