Dataiku is a French startup and its product, DSS, is a challenger to market incumbents and features some visual tools to assist in building workflows.
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Writer
Score 8.6 out of 10
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Writer is a full-stack generative AI platform. It consists of Writer-built LLMs, a graph-based RAG, AI guardrails, and a flexible application layer. Writer boasts users at enterprises like L’Oreal, Vanguard, and Accenture.
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.
I think Writer is perfect for marketing professionals who need to draft copy about dense topics and subject matter, such as within tech and product companies. It bridges the gap of knowledge between marketer and engineer and saves the former countless minutes to bring what the latter develops to the marketplace
Regarding what Writer does, I can say as a first instance it makes my work easier when making my forms, sealing and inserting what I want to implement.
As a second place when it comes to directing me through a business document, you have the option of the synonym search engine and automatic correction.
As a third example, I would say that Writer makes my work easier, is easy to use[,] and saves versions of the same document.
i do find the organisation a bit confusing, like the functions available in 'ask Writer' being separate to the main window. there were lots of functions i wasn't able to find intuitively that we needed a support call for
asking for a summary of key points doesn't always pick up the main points
As I have described earlier, the intuitiveness of this tool makes it great as well as the variety of users that can use this tool. Also, the plugins available in their repository provide solutions to various data science problems.
The support team is very helpful, and even when we discover the missing features, after providing enough rational reasons and requirements, they put into it their development pipeline for the future release.
Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the flexibility to users who do not wish to alter the model hyperparameters in greater depths. Writing codes to extract meaningful data is time consuming compared to Dataiku's ability to perform feature engineering and data transformation through click of a button.
Writer's "recipes" and workflow approach allow me to think through the process of generation better than an open-chat format like ChatGPT or Gemini. It also is more flexible than Jasper (at least when I tested both).
