Deploy machine learning models with GKE and Dataiku



In a previous post I described how easy it is to create and deploy machine learning models (exposing them as REST APIs) with Dataiku. In particular, it was an XGboost model predicting home values. Now, suppose my model for predicting home values becomes so successful that I need to serve millions of request per hour, then it would be very handy if my back end scales easily.

In this brief post I outline the few steps you need to take to deploy machine learning models created with Dataiku on a scalable kubernetes cluster on Google Kubernetes Engine (GKE).

Create a Kubernetes cluster

There is a nice GKE quickstart that demonstrate the creation of a kubernetes cluster on Google Cloud Platform (GCP). The cluster can be created by using the GUI on the Google cloud console. Alternatively, if you are making use of the Google cloud SDK, it basically boils down to creating and getting credentials with two commands:

gcloud container clusters create myfirst-cluster
gcloud container clusters get-credentials myfirst-cluster

When creating a cluster, there are many options that you can set. I left all options at their default value. It means that only a small cluster of 3 nodes of machine type n1-standard-1 will be created. We can now see the cluster in the Google cloud console.


Setup the Dataiku API Deployer

Now that you have a kubernetes cluster we can easily deploy predictive models with Dataiku. First, you need to create a predictive model. As described in my previous blog, you can do this with the Dataiku software. Then the Dataiku API Deployer, is the component that will take care of the management and actual deployment of your models onto the kubernetes cluster.

The machine where the Dataiku API Deployer is installed must be able to push docker images to your Google cloud environment and must be able to interact with the kubernetes cluster (through the kubectl command).


Deploy your stuff……

My XGboost model created in Dataiku is now pushed to the Dataiku API Deployer. From the GUI of the API Deployer you are now able to select the XGboost model to deploy it on your kubernetes cluster.

The API Deployer is a management environment to see what models (and model versions) are already deployed, it checks if the models are up and running, it manages your infrastructure (kubernetes clusters or normal machines).


When you select a model that you wish to deploy, you can click deploy and select a cluster. It will take a minute or so to package that model into a Docker image and push it to GKE. You will see a progress window.


When the process is finished you will see the new service on your Kubernetes Engine on GCP.


The model is up and running, waiting to be called. You could call it via curl for example:

curl -X POST \ \
  --data '{ "features" : {
    "HouseType": "Tussenwoning",
      "kamers": 6,
      "Oppervlakte": 134,
      "VON": 0,
      "PC": "16"


That’s all it was! You now have a scalable model serving engine. Ready to be easily resized when the millions of requests start to come in….. Besides predictive models you can also deploy/expose any R or Python function via the Dataiku API Deployer. Don’t forget to shut down the cluster to avoid incurring charges to your Google Cloud Platform account.

gcloud container clusters delete myfirst-cluster

Cheers, Longhow.


Dataiku 4.1.0: More support for R users!



Recently, Dataiku 4.1.0 was released, it now offers much more support for R users. But wait a minute, Data-what? I guess some of you do not know Dataiku, so what is Dataiku in the first place? It is a collaborative data science platform created to design and run data products at scale. The main themes of the product are:

Collaboration & Orchestration: A data science project often involves a team of people with different skills and interests. To name a few, we have data engineers, data scientists, business analysts, business stakeholders, hardcore coders, R users and Python users. Dataiku provides a platform to accommodate the needs of these different roles to work together on data science projects.

Productivity: Whether you like hard core coding or are more GUI oriented, the platform offers an environment for both. A flow interface can handle most of the steps needed in a data science project, and this can be enriched by Python or R recipes. Moreover, a managed notebook environment is integrated in Dataiku to do whatever you want with code.

Deployment of data science products: As a data scientist you can produce many interesting stuff, i.e. graphs, data transformations, analysis, predictive models. The Dataiku platform facilitates the deployment of these deliverables, so that others (in your organization) can consume them. There are dashboards, web-apps, model API’s, productionized model API’s and data pipelines.


There is a free version which contains already a lot of features to be very useful, and there is an paid version, with “enterprise features“. See for the Dataiku website for more info.

Improved R Support in 4.1.0

Among many new features, and the one that interests me the most as an R user, is the improved support for R. In previous versions of Dataiku there was already some support for R, this version has the following improvements. There is now support for:

R Code environments

In Dataiku you can now create so-called code environments for R (and Python). A code environment is a standalone and self-contained environment to run R code. Each environment can have its own set of packages (and specific versions of packages). Dataiku provides a handy GUI to manage different code environments. The figure below shows an example code environment with specific packages.


In Dataiku whenever you make use of R –> in R recipes, Shiny, R Markdown or creating R API’s you can select a specific R code environment to use.

R Markdown reports & Shiny applications

If you are working in RStudio you most likely already know R Markdown documents and Shiny applications. In this version, you can also create them in Dataiku. Now, why would you do that and not just create them in RStudio? Well, the reports and shiny apps become part of the Dataiku environment and so:

  • They are managed in the environment. You will have a good overview of all reports and apps and see who has created/edited them.
  • You can make use of all data that is already available in the Dataiku environment.
  • Moreover, the resulting reports and Shiny apps can be embedded inside Dataiku dashboards.

The figure above shows a R markdown report in Dataiku, the interface provides a nice way to edit the report, alter settings and publish the report. Below is an example dashboard in Dataiku with a markdown and Shiny report.


Creating R API’s

Once you created an analytical model in R, you want to deploy it and make use of its predictions. With Dataiku you can now easily expose R prediction models as an API. In fact, you can expose any R function as an API. The Dataiku GUI provides an environment where you can easily set up and test an R API’s. Moreover, The Dataiku API Node, which can be installed on a (separate) production server imports the R models that you have created in the GUI and can take care of load balancing, high availability and scaling of real-time scoring.

The following three figures give you an overview of how easy it is to work with the R API functionality.

First, define an API endpoint and R (prediction) function.


Then, define the R function, it can make use of data in Dataiku, R objects created earlier or any R library you need.


Then, test and deploy the R function. Dataiku provides a handy interface to test your function/API.


Finally, once you are satisfied with the R API you can make a package of the API, that package can then be imported on a production server with Dataiku API node installed. From which you can then serve API requests.


The new Dataiku 4.1.0 version has a lot to offer for anyone involved in a data science project. The system already has a wide range support for Python, but now with the improved support for R, the system is even more useful to a very large group of data scientists.

Cheers, Longhow.