Why Dataiku Is a Cloud Innovator

Dataiku Product, Tech Blog Timothy Law

Forbes recently named Dataiku to the list of Forbes Cloud 100, which recognizes “standouts in tech’s hottest category.” So, what is it that makes Dataiku a standout when it comes to cloud AI, machine learning (ML), and analytics? Here are just a few reasons. 

Instant Access to All of Your Cloud Data

According to the Gartner® Market Guide for Data Science & ML Engineering Platforms, one of the factors that will drive the adoption of data science and ML is “Data access across hybrid, and multi-cloud data sources with provisioning for on-demand scalable compute for data engineering and model training.” 

With pre-built integrations, Dataiku seamlessly connects to all types of storage at any scale on any of the major cloud platforms, including Amazon Web Services, Microsoft Azure, Google Cloud Platform, and Snowflake Data Cloud. And the platform pushes down processing to where the data resides, so your data never leaves your secure, governed, and compliant cloud storage.

Multi-Cloud and Cross-Cloud

Dataiku helps break down data silos and enables analysts and data scientists to work with all of their cloud data. Dataiku integrates with cloud data warehouses and data lakes, such as Snowflake and Databricks, so that enterprises can access data across clouds. 

Dataiku simplifies cloud data access so data scientists can spend more time building AI versus managing multiple cloud infrastructures. And they can leverage all of their data across clouds and on-premise to ensure the most performant and robust models. 

Cloud Computing for Scale

Dataiku features an innovative push-down architecture perfectly matched for cloud computing. This architecture lets users take advantage of their cloud provider's elastic and auto-scaling features. It eliminates data movement and pushes processing to the best cloud execution engine for the job, saving time and money and allowing customers to leverage their cloud infrastructure investments.

cloud infrastructure Dataiku

Elasticity for Faster Insights

Dataiku is committed to bringing cloud elasticity to every part of data science workflows to drastically reduce the time it takes from AI model inception to business value. For example, Dataiku has produced the most performant AutoML engine by coupling the distributed processing of Spark with Kubernetes in the cloud. These innovations are partly why Dataiku customers report upward of 70% faster time to insight. 

optimized data paths

Faster API Deployments at Scale With Kubernetes

Dataiku has made it easy to deploy API services in the cloud by simplifying the setup of Kubernetes clusters. Dataiku has pre-built integrations to cloud Kubernetes services, including Amazon Elastic Kubernetes Service (EKS), Azure Kubernetes Service (AKS), and Google Kubernetes Engine (GKE) for deploying API services. Users can easily attach to specific or dynamic clusters in just a few clicks in a visual interface rather than connecting to and configuring these services using code. 

SaaS and BYO Infrastructure

While most AI and analytics platforms are either fully managed or self-managed, Dataiku eliminated this false choice by delivering its platform both ways in the cloud. With its fully managed SaaS offering, Dataiku handles all administration, hosting, and infrastructure. Customers never have to worry about servers, administration, or upgrades. Dataiku handles everything, so customers can focus on analytics and AI.

Enterprises that want more control and customization can quickly deploy Dataiku in the cloud as a self-managed AI platform with cloud stack accelerators. This cloud deployment offers a no-code path to deploy Dataiku on your chosen cloud provider using best practices blueprints developed in partnership with AWS, Microsoft Azure, and Google Cloud Platform. The approach also removes much of the administrative overhead and technical burden of self-managed cloud deployment.

Innovating With Cloud Partners

Dataiku has created an extensive ecosystem of cloud technology alliances and invested broadly in strategic integrations to seamlessly connect data scientists to the storage and compute infrastructures that can help them deliver AI and ML at any scale, cost-effectively. 

For example, Dataiku built extensive integrations with Snowflake Data Cloud. This integration enables data scientists to leverage Snowflake auto-scaling and the massively parallel cloud compute clusters, as well as Snowpark and UDFs, to push down the execution of data preparation and transformations for fast, cost-effective processing. 

Dataiku can be accessed from any cloud marketplace, including AWS Marketplace, Google Cloud Marketplace, Azure Marketplace, and Snowflake Partner Connect. And with Snowflake Partner Connect, users can be up and running with a Dataiku instance in just minutes, leveraging the power of the cloud.


Deployable Microservices and Data Products

Dataiku enables self-service publication and consumption of data services. Dataiku can produce  “data products” as deployable microservices compliant with most microservices architectures and contributes to the realization of a data mesh. For ease of consumption, Dataiku can leverage microservices architecture to expose data services and inference engines. This approach provides data architects with a more agile, trusted, and governed cloud data architecture that enables a data mesh strategy

Deep Learning and Cloud AI Services

Increasingly, businesses are pursuing advanced AI use cases like computer vision and natural language processing, reliant on massive datasets and advanced algorithms. Deep learning models are notoriously compute-hungry and usually require GPUs, and enterprises have turned to the cloud to provide the necessary horsepower to train models.  

Dataiku works with cloud partners to push down deep learning pre-processing tasks, and Dataiku can train deep learning models on cloud GPUs or multiple GPUs from your preferred cloud provider. 

Data scientists can build and train deep learning models within Dataiku or leverage native integrations to leading cloud AI developer services, such as Google Vision AI, Amazon Rekognition, and Cognitive Services from Azure.

Simplifying Cloud Computing for Everyday AI 

Many cloud-native AI platforms serve only expert data scientists who work in code. Dataiku supports expert coders but has always focused on abstracting away the complexity of cloud and AI, enabling low- and no-code users who prefer a visual ML interface. 

With cloud-native technology, Dataiku enables expert data scientists/coders with their favorite IDEs within Dataiku. So, expert coders who prefer JupyterLab, VSCode, or RStudio can work directly in the Dataiku platform and leverage their preferred cloud infrastructure. This enables expert data scientists to work on projects alongside non-coders and citizen data scientists in a collaborative platform. 

The result of Dataiku’s cloud innovations is simplified cloud architecture that unifies access to data science tools and libraries, IDEs, and cloud data sources and services. It streamlines the entire analytics and AI lifecycle, which is why Dataiku customers save up to 75% of the time they spent on projects previously. 

Cloud-native architecture is critical to scaling AI, ML, and analytics, but cloud computing also offers opportunities beyond scaling to innovate and deliver more value across the AI lifecycle. Dataiku’s cloud innovations make analytics and AI at scale more productive and cost-effective.

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