Author Archives: Gianmario
I published on GitHub a tutorial on how to implement an algorithm for predictive maintenance using survival analysis theory and gated Recurrent Neural Networks in Keras. The tutorial is divided into: Fitting survival distributions and regression survival models using lifelines. … Continue reading
One of the determinants for a good anomaly detector is finding smart data representations that can easily evince deviations from the normal distribution. Traditional supervised approaches would require a strong assumption about what is normal and what not plus a … Continue reading
On June the 7th I had a quick introductory talk at AssoLombarda in Milan regarding the role of Data Scientist into the 4th industrial revolution. My presentation is an introduction to what Data Science in the industry is and what … Continue reading
In-Memory Logical Data Warehouse for accelerating Machine Learning Pipelines on top of Spark and Alluxio
Abstract: Legacy enterprise architectures still rely on relational data warehouse and require moving and syncing with the so-called “Data Lake” where raw data is stored and periodically ingested into a distributed file system such as HDFS. Moreover, there are a … Continue reading
The Barclays Data Science Hackathon: Building Retail Recommender Systems based on Customer Shopping Behaviour
In the depths of the last cold, wet British winter, the Advanced Data Analytics team from Barclays escaped to a villa on Lanzarote, Canary Islands, for a one week hackathon where they collaboratively developed a recommendation system on top of Apache Spark. The contest consisted on using Bristol customer shopping behaviour data to make personalised recommendations in a sort of Kaggle-like competition where each team’s goal was to build an MVP and then repeatedly iterate on it using common interfaces defined by a specifically built framework. Continue reading
This post has been published on the Cloudera blog and summurises the results and takeaways of a week-long hackathon happened in Lanzarote in December 2015. The goal was to prototype a recommender systems for retail customers of shops in Bristol in Bristol, UK. The article shows how the stack composed by Scala and Spark was great for quickly writing some prototyping code to run locally in a single laptop and at the same time scalable for larger dataset to process in the cluster. Continue reading
Proof of concept of how to use Scala, Spark and the recent library Sparkz for building production quality machine learning pipelines for predicting buyers of financial products.
The pipelines are implemented through custom declarative APIs that gives us greater control, transparency and testability of the whole process.
In the last years at Barclays we learnt and tried a lot of stuff that made the Advanced Analytics team very successful inside a large organization where, as such, being a productive data scientist is a tough challenge.
Our team works on a mix of descriptive, predictive and prescriptive projects that make use of machine learning and big data technologies, mainly on top of Apache Spark. Even though we deliver per-request insights coming from manual analysis, we primarily build automated and scalable systems to be periodically used either internally for a better decision-making or customer-facing in the form of analytics services (e.g. via the web portal).
In this post series I want to share some of the best practices, tools, methodologies and workflows that we experimented and the lessons learnt from them. Continue reading
Very hard to give guidelines here since that each project have its own deployment process that depends on many factors such as the business context and practical issues associated with it. Continue reading
Exploratory analysis should precede and follow any task from the modelling, design and development to the benchmarking. Major problem is how do you share, track and monitor your findings? How do you make your analysis repeatable and scrutinizable from the outside? This is still an open problem. Continue reading