This is the second part in a series of articles demonstrating best practices for engineering ML pipelines and deploying them to production. In the first part we focused on project setup - everything from codebase structure to configuring a CI/CD pipeline and making an initial deployment of a skeleton pipeline …

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The is the first in a series of articles demonstrating how to engineer a machine learning pipeline and deploy it to a production environment. We’re going to assume that a solution to a ML problem already exists within a Jupyter notebook, and that our task is to engineer this …

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Tags: python, machine-learning, mlops, kubernetes, bodywork

Solutions to ML problems are usually first developed in Jupyter notebooks. We are then faced with an altogether different problem - how to engineer these notebook solutions into your products and systems and continue to maintain their performance through time, after new data is generated …

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I have often lent heavily on Apache Spark and the SparkSQL APIs for operationalising any type of batch data-processing ‘job’, within a production environment where handling fluctuating volumes of data reliably and consistently are on-going business concerns. These batch data-processing jobs may involve nothing more than joining data sources and …

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Stochastic processes are used extensively throughout quantitative finance - for example, to simulate asset prices in risk models that aim to estimate key risk metrics such as Value-at-Risk (VaR), Expected Shortfall (ES) and Potential Future Exposure (PFE). Estimating the parameters of a stochastic processes - referred to as ‘calibration’ in the parlance …

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• 17th August 2019 - updated to reflect changes in the Kubernetes API and Seldon Core.
• 14th December 2020 - the work in this post forms the basis of the Bodywork MLOps tool - read about it here.

A common pattern for deploying Machine Learning (ML) models into production environments - e.g. ML models …

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Conducting a Bayesian data analysis - e.g. estimating a Bayesian linear regression model - will usually require some form of Probabilistic Programming Language (PPL), unless analytical approaches (e.g. based on conjugate prior models), are appropriate for the task at hand. More often than not, PPLs implement Markov Chain Monte Carlo …

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Building machine learning and statistical models often requires pre- and post-transformation of the input and/or response variables, prior to training (or fitting) the models. For example, a model may require training on the logarithm of the response and input variables. As a consequence, fitting and then generating predictions from …

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Elasticsearch is a distributed NoSQL document store search-engine and column-oriented database, whose fast (near real-time) reads and powerful aggregation engine make it an excellent choice as an ‘analytics database’ for R&D, production-use or both. Installation is simple, it ships with default settings that allow it to work effectively out-of-the-box …

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Every now and again someone comes along and writes an R package that I consider to be a ‘game changer’ for the language and it’s application to Data Science. For example, I consider dplyr one such package as it has made data munging/manipulation that more intuitive and more …

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Quite often I want to share simple (static) web pages with other colleagues or clients. For example, I may have written a report using R Markdown and rendered it to HTML. AWS S3 can easily host such a simple web page (e.g. see here), but it cannot, however, offer …

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Parts one, two and three of this series of posts have taken us from creating an account on AWS to loading and interacting with data in Spark via R and R Studio. My vision of a Data Science platform for R&D is nearly complete - the only outstanding component is …

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Part 1 and Part 2 of this series dealt with setting up AWS, loading data into S3, deploying a Spark cluster and using it to access our data. In this part we will deploy R and R Studio Server to our Spark cluster’s master node and use it to …

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Part 1 in this series of blog posts describes how to setup AWS with some basic security and then load data into S3. This post walks-through the process of setting up a Spark cluster on AWS and accessing our S3 data from within Spark.

A key part of my vision …

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Here’s my vision: I get into the office and switch-on my laptop; then I start-up my Spark cluster; I interact with it via RStudio to exploring a new dataset a client uploaded overnight; after getting a handle on what I want to do with it, I prototype an ETL …

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