Revolutionizing the Legal Industry with Spark, NLP and Azure Databricks at Clifford Chance - Databricks

Revolutionizing the Legal Industry with Spark, NLP and Azure Databricks at Clifford Chance

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From zero to data science in a legal firm: how one of the world’s largest law firms is reshaping operations with advanced analytics. Clifford Chance LLP is one of the ten largest law firms in the world. With thousands of global clients their teams handle millions of legal documents every year.

The data science team will share their approach to building an agile data science lab from zero on top of Apache Spark, Azure Databricks and MLflow. They will deep dive into how they used deep learning for natural language processing in the classification of large documents using MLflow and Hyperopt for model comparison and hyperparameter optimization.

 

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About Mirko Bernardoni

Clifford Chance

As head of data science and domain architect for Legal Technology Solutions, Mirko’s role is to build and lead the data science lab. Before joining Clifford Chance in January 2018, he gained 18 years of experience, working in a wide range of industries including financial services, manufacturing, international security and pharmaceuticals. Mirko always worked in technical roles close to research and delivered advanced applications and systems. He has always been interested in cluster computing, parallel programming, optimisation and all the mathematical aspects involved. Mirko strongly believes that the work that we do in Clifford Chance research department offers a unique opportunity to shape and change the legal sector which is why he also work closely with universities for research.

About Michael Seddon

Clifford Chance

Michael has been working as part of Clifford’s Chance Data Science Lab as a Senior Machine Learning Engineer helping to solve the significant data engineering challenges working in such an environment presents. As a core member of the team, he has been involved in the design and development of the data pipelines crucial to the lab’s success, as well as delivering numerous machine learning projects helping to reshape Clifford Chance’s operational approach.