Likelihood to Recommend Amazon SageMaker is a great tool for developing machine learning models that take more effort than just point-and-click type of analyses. The software works well with the other tools in the Amazon ecosystem, so if you use Amazon Web Services or are thinking about it, SageMaker would be a great addition. SageMaker is great for consumer insights, predictive analytics, and looking for gems of insight in the massive amounts of data we create. SageMaker is less suitable for analysts who do generally "small" data analyses, and "small" data analyses in today's world can be billions of records.
Read full review 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.
Read full review Pros Provides enough freedom for experienced data scientists and also for those who just need things done without going much deeper into building models. Customization and easy to alter and change. If you already are an Amazon user, you do not need to transition over to another software. Read full review The intuitiveness of this tool is very good. Click or Code - If you are a coder, you can code. If you are a manager, you can wrangle with data with visuals The way you can control things, the set of APIs gives a lot of flexibility to a developer. Read full review Cons The UI can be eased up a bit for use by business analysts and non technical users For huge amount of data pull from legacy solutions, the platform lags a bit Considering ML is an emerging topic and would be used by most of the organizations in future, the pipeline integrations can be optimized Read full review Read full review Usability 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.
Read full review Support Rating 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.
Read full review Alternatives Considered Amazon SageMaker comes with other supportive services like S3, SQS, and a vast variety of servers on EC2. It's very comfortable to manage the process and also support the end application by one click hosting option. Also, it charges on the base of what you use and how long you use it, so it becomes less costly compared to others.
Read full review 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.
Read full review Return on Investment We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers. We can prototype more rapidly because it is easy to configure notebooks to access AWS resources. For our use-cases, serving models is less expensive with SageMaker than bespoke servers. Read full review Given its open source status, only cost is the learning curve, which is minimal compared to time savings for data exploration. Platform also ease tracking of data processing workflow, unlike Excel. Build-in data visualizations covers many use cases with minimal customization; time saver. Read full review ScreenShots