Data Science and Machine Learning Platforms Reviews and Ratings

Dataiku's greatest strength lies in its comprehensive visual flow, seamlessly managing everything from data input to AI/ML application, visualization, and output. This accessibility allows diverse teams, including sales, data scientists, and service operators, to collaboratively participate throughout the MLOps/DevOps process. Furthermore, Dataiku impressively keeps pace with advancements in generative AI, rapidly integrating these technologies into its platform. This is particularly beneficial for manufacturing companies, often lagging in adopting cutting-edge technologies. For example, prompt engineering on raw data, which code using Dataiku's LLM mesh. The availability of public prompt examples further facilitates rapid implementation. Additionally, features like AutoML significantly streamline the workflow by automatically identifying and visualizing important features from vast datasets, leading to substantial time savings in our projects. As for customer service, I found the customer care to be exceptional. The in-app chat within Dataiku allowed me to quickly reach support staff and receive prompt, helpful responses, which greatly enhanced the overall usability.

Our experience with DataRobot has been exceptional, from the sales process all the way through to implementation and ongoing support. The team is very responsive, knowledgeable, and willing to go the extra mile to ensure we maximize the platform's potential. We evaluated the purchase based on an evaluation of the impact on business processes, broad usability, and improvements to model accuracy and ultimately ROI. DataRobot excelled across the board.

Alteryx is user friendly and it's very easy to access and use. It is also the best service in providing great customer service and it always works for finding best solution to overcome the problem.

Matlab is very easy to work with. The interactive environment provided by Matlab tool which consists of a command window, workspace and different tool set for debugging and visualizations helps the developer to build the algorithm easily. it has great visualization and plotting tool sets which is very helpful for the software developers.

I use RapidMiner to teach students machine learning. Most of the students are non-technical and they find it very easy to learn. But even my technical students really appreciate how rapidly they can create good models. In either case, RapidMiner lets me focus on teaching the intelligent business applications of machine learning without students getting lost in the technical details.

this is a wonderful product to begin the statistical or bio statistical data analysis process because of the point and click ease that it does provide. No matter the project that one is taking on this project makes data analysis so easy that sometimes it feels like anyone can take on a challenging data set just by using this product. It is especially helpful for newer team members who are not used to coding so this type of data analysis is very helpful. It helps to learn the process of data analysis and it also streamlines the process of, what data analysis test to run and being able to see everything that one is doing within one window making the process so much easier to follow and easier to understand.

I use AWS SageMaker on a daily basis for data science projects. I mainly use Studio and Notebook instances as the main development environment. The cloud makes it an ideal tool because you can work with large amounts of data with the ability to scale and obtain more resources as needed with a click.

Integrated our Sales Console to summarise our sales reports. We've been getting great insights into Revenue Ops' performance and cost gap analysis.

The platform SAS Enterprise Guide is easy to use. I have been using it for daily analysis for the past 14 years. No substitute software like SAS Enterprise Guide has even been found by me and it makes my work easier.

Overall experience with base SAS is very good and is widely known for its robust data management and analytics capabilities, here are some points based on overall experience it has powerful data handlining capability, reliability and stability. The SAS base has very strong community support & it gives users a good learning curve.

An overall all one in solution to languages like Python. Jupyter notebook and Spyder helped me greatly with my data science tasks and the package manager makes it incredibly easy to install, update, and maintain libraries and dependencies.

Databricks is a very customizable but powerful platform that helps us to process data in big scale and to organize our data.

For a long time, I didn't know KNIME Analytics Platform existed on the market, fortunately my organization decided to use KNIME. It was very good decision. The huge advantage of KNIME is that it is graphical. KNIME has correctly understood what graphical programming is and has enabled people who know next to nothing about programming to work with large data sets. KNIME has conceived of working with data as a data flow, where it is clear at a glance in what sequence the steps are performed. If you are writing a standard text based program, you can jump from different parts of the code to others, which makes it very difficult to read the logic of a text based program. KNIME logically guides the user to work in the data flow from the very beginning which is a huge advantage. The data workflow is broken down into small steps, which makes it easy to read the steps even months after the work has been completed, what was meant by that step (in contrast, complex SQL needs to be broken down and understood what was meant by that complex SQL even by the creator). In other words, the workflow is very easy to follow/auditing. At the same time, the KNIME makes it possible to aggregate small steps into packages so that even if there is complexity in the data flow , the attention is not overwhelmed by too much detail . This is very much appreciated.

As of now, all companies focused on AI automation and here at Microsoft Azure help a lot to select the best methodology and work with a reliable platform.

It is an application that procedures results for us in terms of business intelligence and data analytics, which we use to create the most suitable products for our customers by clearly seeing their usage behaviour and to keep up with the developing competitive environment. We can produce more specific results with the results produced by the application we developed using 3rd party C#. by the way, it is easily installed and configured in the Windows environment. Thanks to this application, our customer loss decreased and our efficiency increased.

Posit was an excellent vedor to work with: they provided no-nonsense quotes (no need to work with resellers or negotiate pricing - generally pricing is the same for everyone), they gave us a product trial of reasonable length (along with architectural support to get our infrastructure group on board), their product documentation is exceptional, and everyone I've talked to has been kind and helpful. Not once did I feel like I was trying to be upsold. And then add on the fact that the dollars you're spending are not only improving the paid product but also the open-source landscape for people across the world.

The Domino product is fantastic. It provides our users with all of the functionality that they require all packaged in a super user friendly interface that allows for maximum productivity and collaboration.

Overall, h2o.ai leads you towards insightful discoveries

supplier always avaiable to talk and the cms figure supports us in using the tool in the best way possible.

I have personally used vertex ai in developing Chatbot & I can say everyone should learn about vertertex ai it help you to create, deploy & manage all machine learning models.