Dataiku Reviews and Ratings

Use Cases and Deployment Scope

We use Dataiku to build automated flows for specific data science problems. Mostly ETL scripts to clean the data, and also to monitor for data drift. Because its visually easy to use, its also great for less technical users to follow along with the flows and what we are doing when we demo it

Likelihood to Recommend

Dataiku is an awesome tool for data scientists. It really makes our lives easier. It is also really good for non technical users to see and follow along with the process. I do think that people can fall into the trap of using it without any knowledge at all because so much is automated, but I dont think that is the fault of Dataiku.

Use Cases and Deployment Scope

Dataiku DSS is being used in my team to perform various tasks which ranges from data preprocessing to machine learning model creation. It provides a one-stop solution to fetch data from different sources such as Amazon S3, SQL Server databases, etc. and merge them onto a single platform. We use Dataiku DSS to perform data imputations, data cleaning and feature engineering to prepare datasets for creating machine learning models. We also extract business insights (data analytics) using various statistical methods and visual representations such as scatter plots, histograms, boxplots, etc. Furthermore, optimized ML models are created which are used to predict/forecast target variables and drive business decisions.

Likelihood to Recommend

Dataiku DSS is very well suited to handle large datasets and projects which requires a huge team to deliver results. This allows users to collaborate with each other while working on individual tasks. The workflow is easily streamlined and every action is backed up, allowing users to revert to specific tasks whenever required.

While Dataiku DSS works seamlessly with all types of projects dealing with structured datasets, I haven't come across projects using Dataiku dealing with images/audio signals. But a workaround would be to store the images as vectors and perform the necessary tasks.

Use Cases and Deployment Scope

I work for a client who's implementing all their data science solutions on Dataiku DSS. I have been working on implementing these solutions for my client for over a year now. Currently, it is channeled through the IT department and we have expanded across multiple departments ranging from finance to sales. We have implemented multiple time series forecasting projects, NLP, business optimization as well as customized analytics data flow for the finance department.

Likelihood to Recommend

I would recommend it because it's an amazing tool for different levels of users. From Business Analysts to Data Scientists to Managers, various employees can make use of this tool to make data-driven decisions. I'm not sure about where it would be less appropriate as I'm using it as Data Scientist and so far it pretty much caters to my need.

Use Cases and Deployment Scope

Platform is currently being used solely by our department due to licensing cost and budget reasons.

Platform offers a one stop shop for an (almost) end to end development of data analytics and machine learning products, including data import, manipulation, and visualization. It’s a low-code tool, and supports majority of workflow without the need for in-depth coding skills; this is a plus for exposing platform across a wider audience and use cases.

Likelihood to Recommend

For team(s) with varying levels of coding skills, the platform offers a one size fit all for most data analytics and machine learning projects that are of practical use in industrial settings (e.g. time series forecasting, predictive maintenance and production optimization). In research and development work, where projects are cutting edge and no out of the box solutions are available, then platform is of minimal use, since custom data ingestion and manipulation may be required.

Use Cases and Deployment Scope

Dataiku is being used as the integrated data analytic/AI/ML platform. It is a corporate-level standard solution, across multiple regions and business domains. The data scientists use this platform to develop various data pipelines, and/or train the AI/ML models, verify the model performances and eventually deploy the model as service to benefit business critical IT applications (majorly serve the predictive analysis/automation and integration with RPA).

Likelihood to Recommend

Dataiku is suitable for many steps of data processing pipeline development (from data collecting, filtering till cleaning, transformation and enhancement), and it is also good for the user who doesn't have too much in-depth AI/ML knowledge to quickly jump into it and give a try to solve some real-world problem.