Dataiku

Dataiku by sennchi

Predictive Analytics And Machine Learning Solutions
https://www.dataiku.com

Dataiku gets code or click right. A haiku is a Japanese form of poetry of 17 syllables — concise and evocative if done well. that’s Dataiku’s guiding inspiration — to offer a data science platform that lets coders use a notebook when they must, but use visual tools to build workflows when productivity is at a premium. Dataiku is one of the new venture-funded startups that aim to be a well-rounded alternative to the long-time market competitors by offering a notebook experience embedded in a visual experience. With better model management capabilities, Dataiku is poised to challenge the leaders posthaste.

Dataiku is headquartered in New York City, U.S., and has a main office in Paris, France. It offers Data Science Studio (DSS) with a focus on cross-discipline collaboration and ease of use.

Dataiku remains a Visionary — and a popular choice for many data science needs — by enabling users to start machine-learning projects rapidly. Its position for Completeness of Vision is due to its collaboration and open-source support, which are also the focus of its product roadmap. Its overall Completeness of Vision score is lower than in the prior Magic Quadrant, due to comparatively poor breadth in terms of use cases and deficiencies in automation and data streaming. Dataiku's Ability to Execute has also decreased, due to some difficulties in operationalizing and scaling machine-learning models.

STRENGTHS

• **Collaboration across skill sets: **Dataiku DSS is differentiated by having multiple collaboration features across the machine-learning pipeline. It offers three profiles for users with different skill levels — from dashboard-type graphical UIs for less-skilled users to a visual pipelining tool for intermediate-level users. Data scientists can use coding features, including shells and notebooks that offer more flexibility but also require more in-depth knowledge. Many reference users observed that the collaborative nature of Dataiku DSS has democratized machine learning across their organization.

• **Flexibility and openness: **Dataiku DSS enables machine-learning algorithms to be "plugged in" from the open-source community. Users can choose to create models using native machine-learning code recipes, leading machine-learning engines (such as those of H2O.ai and Apache Spark MLlib), notebooks (such as Jupyter Notebook), and language wrappers for R, Python and Scala. There is good integration with Hadoop and Spark.

• **Ease of use and rapid prototyping: **An intuitive interface makes Dataiku DSS a popular tool for rapid prototyping. Reference customers reported significant time savings when using the tool for proofs of concept, rapid prototyping, and even a test-and-learn approach to get those who are not data scientists started with machine-learning projects. Although the platform lacks certain advanced functionalities, many reference customers use it for product development and research and development (R&D).

CAUTIONS

• **Operationalization of machine-learning algorithms: **Reference customers pointed to some difficulties deploying models in production environments and migrating to the latest version. Several mentioned instabilities and bugs, although they also reported that Dataiku was quick to fix them. DSS received low scores for delivery and performance and scalability.

• **Lack of model factory automation and advanced analytics functionality: **Dataiku's product scores for automation are low, with deficiencies in model factory capabilities, such as the use of machine learning to automatically propose or select features and for model optimization. DSS does not offer a full range of capabilities using advanced analytics techniques such as simulation and image analytics. However, the company's roadmap includes plans to offer native prebuilt deep-learning models for text and images, in addition to the existing integration with TensorFlow.

• **Pricing: **High prices are a concern, with a significant number of reference customers identifying them as inhibitors of wider adoption. Reference customers also gave Dataiku low scores for end-user evaluation and contract negotiation experience.