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Building and Deploying Models, Made Easy

The key to delivering business value from raw data lies not only in empowering every employee to leverage data-powered insights in his or her day-to-day work, but also in the company’s ability to go beyond the limits of small data and create machine learning models at scale.

The process of building models still — in most companies — follows the dreaded 80/20 rule. That is, 80 percent of the modeling process is connecting to data, cleaning, wrangling, and enriching it to get it into a state where a machine learning model can be applied. That leaves a lot of opportunity for automation in both the creation of models themselves (e.g., tuning hyperparameters) as well as throughout the model creation pipeline via AutoML and augmented analytics.

7 steps of the data lifecycle

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The real hurdle to building models may not even be all the steps leading up to it or the model building and tuning itself, but putting that model in production, or operationalizing it — a critical, yet challenging, piece of the puzzle.

How Dataiku Enables Modeling

Dataiku offers the latest machine learning technologies all in one place so that data scientists can focus on what they do best: building and optimizing the right model for the use case at hand:

  • Visual machine learning leverages state-of-the-art ML libraries, including Scikit-Learn, MLlib, XGboost, etc.
  • Create, train and deploy advanced, custom machine learning models using Python or R.
  • Integrate any external machine learning library accessible through code APIs.

Since the product’s inception, Dataiku has offered AutoML features as well as a visual AutoML suite that guides the user through all of the machine learning steps (train-test split, features handling, metrics to optimize, different templates of pre-set algorithms).

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Dataiku also has robust support for deployment of models into production (including one-click deployment on the cloud with Kubernetes), easing the operationalization of machine learning.

Operationalization: From 1 to 1000s of Models in Production

The ability to efficiently operationalize data projects is what separates the average company from the truly data-powered one.

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