Best Data Science Platforms & Tools

DakaIku is a great general purpose data science platform for both supervised and unsupervised learning. It handles Big Data very well.

Sparkcognition's Darwin product can handle very large data sets. 

If you want to handle computer vision data, I recommend the Superb AI Suite. 
https://www.superb-ai.com/

The question also needs to specify which domain, what kind of data and public or private platforms. 

For structured/tabular data driverless AI / H20.ai sparkling water is my preferred platform. 

My experience has not been on large scale systems. Not even  multi-terabytes. My mult-megabytes would not help. Sorry!

IBM SPSS Modeler

Need to address basic data issues, e.g., quality, standardization and security, and MDM first, to obtain meaningful data visualization and single entity views, e.g., customer, patient and product. Ideally, a visualization tool should be able to interact with a backend actionable data catalog driven by data virtualization/federation either directly or through data provisioning. Power BI, QlikView and Tableau are excellent standard data visualization tools. Cambridge Intelligence's KeyLines is an excellent interactive graph visualization tool.

In my case, I can recommend Power BI, that works very well with a lot of database. It shows very good visualization graphs that allows to create many dashboards easily and connect with many data sources that can work very good to present, share and compare data thought the company and with users.

We have been using Qlik Sense for the past 2 years and purchased but never really used Qlik View before that. We have used excel extensively and seen demos and tried Power BI and looked at demos for a couple of other BI tools.

We settled on using Qlik Sense as our Reporting, BI and Analytics tool due a very successful proof of concept delivered by our Qlik consultants.

Qlik Sense gives us the ability to visualize our data in various ways from simple bar and line charts or combined to scatter plots, mekko charts, funnel chart, pie charts, gauge charts and KPI items. Visualization options include table and pivot table that can be utilized to display detailed data. Visualizations also include a map chart that can be used to visualize various map layers with to display movement, density, are and points. 
This has been extremely valuable being from a logistics company.

I would therefore recommend Qlik Sense for the best visualization capabilities.

QlikSense. The associative analytics engine makes it kind of child's play to combine multi-source data and in combination with the augmented intelligence features QlikSense helps to create analytics and visualizations faster.

Now a days lot of visualization tools coming in the market, its difficult for anyone to choose from these variety of tools. However there can be various parameters which will help choose right set of Visualization tool for your requirements.

1. User Friendliness

2. Self Service Capability

3. Connectivity / compatibility with different systems that are available in the market

4. Compatibility with Cloud service providers

5. Relational, big-data systems and data lake connections, AI-ML and predictive analytics capabilities

6. License Cost 

I would recommend Power BI and Tableau as they provide lot of features and visualizations to choose from, with reasonable cost and connectivity with major systems.

For cost and capabilities, it' Looker.

I've used many softwares and this one seems have the best security, features and ease of use for the end user. 

There are powerful options. QlikView, Tableu and PowerBi offers agile implementation.

There is a lot of vendors that offers their data science platforms, but it depends on of what you call end-to-end vendors and if you write the Word really, well makes me think that you already test many of them. Data science platforms came from a variety of vendors like IBM, SAP, Microsoft, Domino Data labs, RapidMinder among others. First I suggest that you have a person or team ready to test these solutions, if not, remember to prepare some profiles with skills of programming and process design.

My recommendation is if you already work with IBM ask for their Data Science experience. In other case my suggestion is to try RapidMiner that seems to be very useful with a fluid interface for model deployment and could try Sas Enterprise Miner as the top of the model building and model deployment and appears as one of the leaders of these platforms.

I hope this was useful and regards.

KNIME or Alterxy is a good choice for a company to deploy AI applications.

It has:

1. light data processing like ETL,

2. AI modeling develop and deploy,

3. and output simple charts or output to databases for further use like API/BI/etc.

If you deploy in the cloud, you can also use the AWS Sagemaker or other cloud tools.

There are many vendors offering end to end deployment with pros and cons. You can evaluate based on :
- On-prem vs cloud requirement
- Data volume that you want to process
- Do you already have ETL processes in place to extract the relevant data from diff sources?
- How are you planning to consume your ML output (API/dashboard/reports, etc)?
- Lastly, your ML algorithms that you intend to use and whether analyzing structured or unstructured data or both.

If you need further details, I will ask my presales to get in touch with you. Please provide me your contact information
.

DataRobot for OnPrem
SageMaker for AWS

Another thing you need to be cognizant of is end-to-end platforms allow you to build and deploy models to production, that is ML 101, where the market is moving is building and scaling predictive applications for numerous business process and cases. Also many end-to-end platforms do not have the capabilities to deal with data drift, model retraining once it's in production and for more advanced use cases the capability for human-in-the-loop feedback to help retrain the model. A final thought I will put out there is explainability and interpretability are paramount today, you can build your models in open source, use these other tools to put them into production but you are going to have a gaping hole when someone comes to ask you, how did you build the model, what weights did you put on your features, how are you dealing with bias, etc. Majority of all platforms out there today, help you stitch together disparate open source solutions, but when you actually get into product-ionizing and scaling multiple business processes that are operationalized with machine learning they don't work.

The current issue today with the majority of DS platforms is they are based on disparate open-source libraries, or you need 5-6 different tools to build your end-to-end ML workflow, most have never seen production either.

At BigML we've been around for 10+ years were the first to market with an MLaaS platform and can help you and your team accomplish true end-to-end ML (source > dataset> model > predictions > production) all in a singular platform, we work with many clients in your space, and would be happy to talk with you. You can even sign up for our platform for free and take it for a spin.

One potential solution might be the SAS platform https://www.sas.com/en_us/software/platform.html

As others have said, many options but add Dataiku, H2Oi, Alteryx, and Databricks to your list.