Just saw this thread and wanted to jump in... To add to the point above about those visual workflow tools, I have been really happy with the performance lately. It is honestly surprising how well they handle massive datasets without eating up all your RAM like they used to. Basically, the way they manage temporary storage during long execution strings is pretty top notch now, no complaints on my end. It makes the whole data prep phase so much less of a headache when you arent constantly worrying about the engine crashing mid task. Performance is king for me so seeing that optimization is great.

Building on the earlier suggestion about those visual tools, I am honestly super happy with how they have stabilized lately. I tend to be a bit cautious with new stuff because I am always worried about compatibility breaking my flow, but these have been working well without any major hiccups. No complaints from my side tbh. If you are looking for the best way to actually set them up:

• honestly just google the specific tool + integration guide
• check out some of those data analyst subreddits for real feedback
• look at the manufacturer sites directly for their safety whitepapers I saw a really good video about this stuff a few days ago, forget the name but if you search for ai data workflows it is like one of the first results. Way more helpful than anything I could type out here lol... plus it shows the actual UI which helps.

No way, I literally just dealt with this yesterday. Small world.

Here are some for you:

• TensorFlow: An open-source library ideal for deep learning and numerical computation, offering flexibility and scalability.
• PyTorch: Known for its dynamic computational graph, it's excellent for deep learning projects and model prototyping.
• IBM Watson: Provides advanced natural language processing and data visualization tools, making it suitable for enterprise-level applications.
• Google Cloud AI: A comprehensive suite that supports multiple machine learning frameworks and offers automated model-building capabilities.
• Tableau: Renowned for its intuitive data visualization tools, it also incorporates AI-driven analytics to enhance insights.

Luzmo

I use Microsoft Power BI.

Alteryx.

Sisense enables users to embed conversational analytics directly into their BI tools, simplifying the process of data preparation and dashboard creation. It is designed to be accessible to users of all expertise levels.

Domo uses natural language processing to guide users through analytics deployment and insight extraction, making it user-friendly for those less familiar with data analysis.

I recently started exploring visual workflow tools like KNIME and RapidMiner. They are especially handy if you're aiming for a low-code or no-code environment. Both platforms integrate well with external machine learning models, offering a balance between ease of use and advanced functionality. For teams that might not have heavy coding expertise, these tools can be a game-changer.