How Companies Get Value from Data Science Production

There’s a part of data science that you never hear about: the production. Everybody talks about how to build models, but not many people worry about how to actually use those models. Yet production issues are the reason many companies fail to see value come from their data science efforts.

The data science process is extensively covered by resources all over the web and known by everyone. A data scientist extracts some data, splits it, cleans it, builds features, trains a model, deploys it to assess performance, and iterates until he’s happy with it. That’s not the end of the story though. Next, you need to try the model on real data and enter the production environment.

These two environments are inherently different because the production environment is continuously running. Data is constantly coming in, being processed and computed into KPIs, and going through models that are retrained frequently. These systems, more often than not, are written in different languages than the data science environment.

To better understand the challenges companies face when taking data science ‘into production,’ Dataiku, the maker of the all-in-one collaborative data science platform Dataiku DSS, recently asked thousands of companies around the world how they do it. The results show that companies using data science have unique challenges that fall into four different profiles: Small Data Teams, Packagers, Industrialisation Maniacs, and The Big Data Lab.

Small Data Teams (23%)

Small Data Teams Focus on building small projects fast: Standard machine learning packages with a unique server and technical environment for all analytics projects.

> 3/4 Do either Marketing or reporting.

> 61% Report having custom machine learning as part of their business model.

> 83% Use either SQL or Enterprise Analytics databases.

These teams, as their name indicate, use mostly small data and have a unique design /production environment. They deploy small continuous iterations and have little to no rollback strategy. They often don’t retrain models and use simple batch production deployment, with few packages. Business teams are fairly involved throughout the data project design and deployment.

Average level of difficulty of deployment: 6.4

Packagers (27%)

Packagers Focus on Building a Framework (the software development approach): Independent teams that build their own framework for a comprehensive understanding of the project.

> 48% have set-up Advanced Reporting.

> 52% of respondents mix storage technologies.

> 63% use SQL and open source.

These teams have a software development approach to data science and have often built their framework from scratch. They develop ad-hoc packaging and practice informal A/B testing. They use Git intensely to understand the globality of their projects and their dependencies, and they are particularly interested in IT environment consistency. They tend to have a multilanguage environment and are often disconnected from business teams.

Average level of difficulty in deployment: 6.4

Industrialisation Maniacs (18%)

Industrialisation Maniacs Focus on Versioning and Auditing: IT-driven teams that think in terms of frequent deployment and constant logging to track all changes and dependencies.

> 61% have Logistics, Security, or Industry Specific use cases

> 30% have deployed Advanced Reporting (vs 50% of all respondents)

> 72 % use NoSQL and Cloud.

These data teams are mostly IT-led and don’t have a distinct production environment. They have complex automated processes in place for deployment and maintenance. They log all data access and modification and have a philosophy of keeping track of everything. In these setups, business teams are notably not involved in the data science process and monitoring.

Average level of difficulty in deployment: 6.9

The Big Data Lab (30%)

The Big Data Lab Focus’ on Governance and Project Management: Mature teams with a global deployment strategy, rollback processes, and preoccupation with governance principles and integration within the company.

> 66% of companies have multiple use cases in place.

> 50% do advanced Social Media Analytics (vs 22% of global respondents).

> 53% use Hadoop and two thirds of them only use Hadoop.

These teams are very mature with more complex use cases and technologies. They used advanced techniques such as PMML, multivariate testing (or at least formal A/B testing), have automated procedures to backtest, and robust strategies to audit IT environment consistency. In these larger, more organized teams, business users are extremely involved before and after the deployment of the data product.

Average level of difficulty in deployment: 5.6.

Overall, the main reported barrier to production for all groups (50% of respondents) is data quality and pipeline development issues. In terms of the overall difficulty of data science production, the average reported difficulty of deploying a data project into production is 6.18 out of ten, and 50% of respondents’ state that on a scale of 1 to 10, the level of difficulty involved in getting a data product in production is between six and 10.

Considering the results, these are a few principles that companies should keep in mind on how to build production-ready data science products:

  1. Getting started is tough. Working with small data on SQL databases does not mean it’s going to be easier to deploy into production.
  2. Multi language environments are not harder to maintain in production, as long as you have an IT environment consistency process. So mix’n’match!
  3. Real-time scoring and online machine learning are likely to make your production pie more complex. Think about whether the improvement to your project is worth the hassle.
  4. Working with business users, both while designing your machine learning project and after when monitoring it day to day, will increase your efficiency. Collaborate!

To download the complete survey and results visit:


About Dataiku

Dataiku develops Dataiku Data Science Studio, the enterprise-grade platform for data teams that enables companies to build and deliver their own data products more efficiently. Thanks to a collaborative and team-based user interface for data scientists and beginner analysts, to a unified framework for both development and deployment of data projects, and to immediate access to all the features and tools required to design data products from scratch, users can easily apply machine learning and data science techniques to all types, sizes, and formats of raw data to build and deploy predictive data flows.

More than 100 customers in industries ranging from e-commerce, to industrial factories, to finance, to insurance, to healthcare, and pharmaceuticals use DSS on a daily basis to collaboratively build predictive dataflows to detect fraud, reduce churn, optimize internal logistics, predict future maintenance issues, and more. Dataiku has offices in Paris and New York.

Dataiku raised a $14M Series A round led by FirstMark Capital in October, 2016.

This article was originally published on Read IT Quik

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