Putting Data Science in Production: 9 Steps to Finding the Common Ground

Technology|Data Science| March 24, 2016| Pauline

To succeed in today's rapidly evolving data ecosystem, companies must continuously re-invent & deliver innovative data products.

Unfortunately, in most organizations, there is a disconnect between development and production environments causing projects to either fail or to drag on for months beyond promised deadlines. But with the overwhelming plethora of new technologies and blooming skill sets, it doesn't have to be that way.

So why is it that companies are having so much trouble successfully building data products and then deploying them into production? Is it because teams are often isolated and playing by their own rules? Is it because development and production environments are perceived as two separate worlds from the moment a project is conceived? Moreover, what can organizations do to bring development and production together.

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By implementing the following 9 steps, we believe that organizations can find the common ground needed to empower the Data Science and IT Teams to work together for the benefit of the data projects as a whole.

1) CONSISTENT PACKAGING & RELEASE

How to support the reliable transport of code & data from one environment to the next.

2) CONTINUOUS RETRAINING OF MODELS

What strategy for efficient re-training, validation, and deployment of models?

3) MULTIVARIATE OPTIMIZATION

A/B testing? Multi-armed bandit testing? Or optimized multi-armed bandit testing?

4) FUNCTIONAL MONITORING

How to ensure that your business sponsors have the capability to detect early signs of drift.

5) ROLL-BACK STRATEGY

How to make sure rolling back to a previous model version is just a few clicks away.

6) IT ENVIRONMENT CONSISTENCY

Making sure Python, R, Spark, Scala... H2o, scikit-learn, MLlib... SQL, JAVA, .NET can work together.

7) FAILOVER STRATEGY & ROBUST SCRIPTS

How to prepare for the worst with failover and validation procedures to maintain stability.

8) AUDITABILITY & VERSION CONTROL

How to easily know what version of each output corresponds to the code used to create it.

9) PERFORMANCE & SCALABILITY

How to create an elastic architecture that can handle significant transitions.

All in all, the ultimate success of a data science project comes down to contributions from individual team members working together towards a common goal. As can be seen from the topics discussed, “effective contribution” goes beyond specialisation in an individual skill-set. Team members must be aware of the bigger picture and embrace project level requirements, from diligently packaging both code and data to creating Web-based dashboards for their project’s business owners. When all team members adopt a “big picture” approach, they are able to help each other complete tasks outside of their comfort zone.

Data science projects can be intimidating; after all, there are a lot of factors to consider. In today’s competitive environment, individual silos of knowledge will hinder your team’s effectiveness. Best practices, model management, communications, and risk management are all areas that need to be mastered when bringing a project to life. In order to do this, team members need to bring adaptability, a collaborative spirit, and flexibility to the table. With these ingredients, data science projects can successfully make the transition from the planning room to actual implementation in a business environment

To find out more about strategies and procedures that have enabled dozens of companies to efficiently build, deploy, and run their own data-driven advantages, please download our guide.

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