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DATAIKEN Review

3

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Very good

Revainrating 4.5 out of 5  
Rating 
4.7
Analytics, Analytics Platforms

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Description of DATAIKEN

DATAIKEN helps organizations run their data science, AI/ML implementations with three key features, A) by providing, an Integrated Platform for all AI/ML and BI workflows, B) pre-built components and API services and C) a low-code drag-and-drop environment integrated with built-in Data Governance and Process Audit features. These not only eliminate several issues of using traditional tools and processes, but also helps directs the user behaviour towards delivering quick outcomes and easily iterating to improve results. How does it benefit you? First: By providing an integrated platform for data scientists, developers and DevOps engineers. This eliminates the pains of integrating an AI/ML modeling software with a backend systems and scripting its deployment, CI/CD and monitoring functionalities. Second: By providing pre-built components and API services. This boosts the productivity of data scientists by enabling them to reuse the pre-built AI/ML models, and developers can use APIs to integrate external applications quickly. This speeds up the implementation process and can be used to show results to management, within a matter of hours and days, instead of weeks and months. Third: By providing a low-code environment with several commonly used components and workflows made available through a drag-and-drop interface called the Flow designer and runtime. This means that organizations can leverage their data analysts for data science AI/ML implementations,thereby avoiding the problem of skill-availability and retention. Flow Designer functionality encourages teams to think about the product being delivered, and allows data scientists and developers to easily collaborate to deliver the outcome.

Reviews

Global ratings 3
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Type of review

Revainrating 4 out of 5

Gartner Hype Cycle - Tool used across multiple departments

I love that it is simple to use in terms of managing projects and creating workflows for different teams within an organization. It's great to have all my tools together under one roof! There are not many out there like this yet! The UI could be better but we were able to find our way around quickly enough so no big deal. We're solving an issue by putting all these tools in a single platform. The ability to create different models and predict future using historical data set.The ease of use is…

Pros
  • Dataiku has helped us solve difficult business challenges faster than ever before..it enables analysts across multiple locations without any barriers such distance or time zone differences making remote work easy & efficient
Cons
  • When compared against Google Colab/Jupyter widgets (or Jaspersoft) they do need some customization

Revainrating 5 out of 5

Useful and powerful Machine Learning Toolkit

Dataiku provides all that you need to build machine learning pipelines in any language (Python + R). The library is well documented so it's fairly easy for new users who may be less experienced when first using these tools - or even more proficient but not familiar with ML tooling like sklearn / Python packages etc. I wish there was an easier way of connecting your models back into other apps such as Google Analytics etc rather than having to write custom integrations from scratch every time…

Pros
  • It works great wth both python2&3 plus rpy
Cons
  • Some cons

Revainrating 5 out of 5

Easy Machine Learning Tool with Scalable WorkFlow Management

The ability to use prebuilt libraries or build custom ones for complex machine learning problems is what I like best of this toolkit; it's easy but at same time powerful enough! Some python APIs are quite tricky in how they work especially when you try them from browser without knowing about all dependencies involved that sometimes make things less straight forward - need proper documentation here (the one provided by Datakit team so far isn't always complete). DataScience workflow management…

Pros
  • Easy integration with other data science frameworks
  • Good support/documentation available.
  • Scalable architecture helps us achieve high performance results while running large datasets through pipelines efficiently
Cons
  • I will keep silent