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Guillaume Autier
CEO at Groupe Meilleurtaux
Guillaume Autier is a highly experienced data science professional working as Pre Sales France - Southern Europe at Domino Data Lab. With his expertise in data science, he centralizes data science work and infrastructure across the enterprise using Domino Data Labs, making data scientists work collaboratively in building, training, deploying, and managing models more efficiently and at a faster pace.
Autier's educational background includes a degree in DUT, Services et Reseaux de communications from Université de Marne-la-Vallée. Before he joined Domino Data Lab, he worked as a Director Solutions Consultants and Senior Manager Analytics Solution Consultant at TIBCO Software Inc., where he specialized in EMEA TIBCO Jaspersoft and Asia Pacific Analytics Solution Consultants.
Autier has gained an extensive amount of experience throughout his career, having worked in numerous positions such as Director Technical Services EMEA and APAC and General Manager at Solutions Informatiques Libres. Furthermore, he also worked at Ville de Granville and Communaute Urbaine de Cherbourg with his expertise in IT.
With his rich background in data science and IT, coupled with his ability to understand complex systems, Autier is an accomplished professional who has made great strides in his field. He has a passion for technology and is driven by his desire to solve complex problems using the latest technological advancements.
Highlights
Where Is Data Science Innovation Going in Health and Life Sciences? Read insights from visionaries at Evidation, The Janssen Pharmaceutical Companies of Johnson & Johnson, Accenture, Snowflake and more in our new ebook, The Health and Life Sciences Data Science Innovator's Playbook. http://gag.gl/oS4F3u #MLOps
Will automated ML “democratize data science”? Not so fast.
While auto ML provides some advantages – and eliminates a lot of manual labor – it can't replace human data scientists entirely. In fact, according to Kjell Carlsson, Ph.D., it can make data scientists even more necessary.
Ellen Glover’s in-depth article for Built In reveals the pros, cons, and misconceptions about autoML. Read now to learn.
*Reasons autoML can't offer the “why” of its decision-making process *How autoML can be a "solution looking for a problem" *The necessary role of human data scientists in maximizing autoML's potential *The best use cases for investing in autoML:
