Compare KNIME vs. Microsoft Azure Machine Learning Studio

KNIME is ranked 2nd in Data Science Platforms with 10 reviews while Microsoft Azure Machine Learning Studio is ranked 4th in Data Science Platforms with 5 reviews. KNIME is rated 8.4, while Microsoft Azure Machine Learning Studio is rated 7.4. The top reviewer of KNIME writes "Our average record size was around 10 million records. If we have bigger data, we can opt for a Big Data extension for Hadoop, Spark, etc". On the other hand, the top reviewer of Microsoft Azure Machine Learning Studio writes "Enables quick creation of models for PoC in predictive analysis, but needs better ensemble modeling". KNIME is most compared with Alteryx, RapidMiner and Weka, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Amazon SageMaker and Alteryx. See our KNIME vs. Microsoft Azure Machine Learning Studio report.
Cancel
You must select at least 2 products to compare!
Most Helpful Review
Find out what your peers are saying about KNIME vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: November 2019.
377,029 professionals have used our research since 2012.
Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros
This solution is easy to use and especially good at data preparation and wrapping.It provides very fast problem solving and I don't need to do much coding in it. I just drag and drop.Key features include: very easy-to-use visual interface; Help functions and clear explanations of the functionalities and the used algorithms; Data Wrangling and data manipulation functionalities are certainly sufficient, as well as the looping possibilities which help you to automate parts of the analysis.Clear view of the data at every step of ETL process enables changing the flow as needed.We leverage KNIME flexibility in order to query data from our database and manipulate them for any ad-hoc business case, before presenting results to stakeholders.The product is very easy to understand even for non-analytical stakeholders. Sometimes we provide them with KNIME workflows and teach them how to run it on their own machine.Easy to connect with every database: We use queries from SQL, Redshift, Oracle.We are able to automate several functions which were done manually. I can integrate several data sets quickly and easily, to support analytics.

Read more »

Visualisation, and the possibility of sharing functions are key features.It is very easy to test different kinds of machine-learning algorithms with different parameters. You choose the algorithm, drag and drop to the workspace, and plug the dataset into this component.When you import the dataset you can see the data distribution easily with graphics and statistical measures.Split dataset, variety of algorithms, visualizing the data, and drag and drop capability are the features I appreciate most.MLS allows me to set up data experiments by running through various regression and other machine learning algorithms, with different data cleaning and treatment tools. All of this can be achieved via drag and drop, and a few clicks of the mouse.The graphical nature of the output makes it very easy to create PowerPoint reports as well.Scalability, in terms of running experiments concurrently is good. At max, I was able to run three different experiments concurrently.Its ability to publish a predictive model as a web based solution and integrate R and python codes are amazing.

Read more »

Cons
It needs more examples, use cases, and MOOC to learn, especially with respect to the algorithms and how to practically create a flow from end-to-end.They could add more detailed examples of the functionality of every node, how it works and how we can use it, to make things easier at the beginning.The visualization functionalities are not good (cannot be compared to, for instance, the possibilities in R).The program is not fit for handling very large files or databases (greater than 1GB); it gets too slow and has a tendency to crash easily.​The data visualization part is the area most in need of improvement.The overall user experience feels unpolished. In particular: Data field type conversion is a real hassle, and date fields are a hassle; documentation is pretty poor; user community is average at best.Data visualization needs improvement.I'd like something that would make it easier to connect/parse websites, although I will fully admit that I'm not as proficient in KNIME as I would like to be, so it could be I'm just missing something.

Read more »

Operability with R could be improved.I would like to see modules to handle Deep Learning frameworks.I personally would prefer if data could be tunneled to my model through a SAP ERP system, and have features of Excel, such as Pivot Tables, integrated.Enable creating ensemble models easier, adding more machine learning algorithms.​It could use to add some more features in data transformation, time series and the text analytics section.Microsoft should also include more examples and tutorials for using this product.​

Read more »

Pricing and Cost Advice
KNIME desktop is free, which is great for analytics teams. Server is well priced, depending on how much support is required.

Read more »

To use MLS is fairly cheap. Even the paid account is something like $20/month, unless you are provisioning large numbers of VMs for a Hadoop cluster. The main MS makes money with this solution is forcing the user to deploy their model on REST API, and being charged each time the API is accessed. There are several pricing tiers for the API. If you do not use the API, then value of MLS is to create rapid experiments ($20/month). The resulting model is not exportable to use, thus you’ll have to recreate the algorithms in either R or Python, which is what I did. MLS results gave me a direction to work with, the actual work is mostly done in R and Python outside of MLS.

Read more »

report
Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.
377,029 professionals have used our research since 2012.
Ranking
2nd
Views
21,168
Comparisons
16,525
Reviews
10
Average Words per Review
333
Avg. Rating
8.4
4th
Views
9,085
Comparisons
7,606
Reviews
5
Average Words per Review
353
Avg. Rating
7.4
Top Comparisons
Compared 41% of the time.
Compared 14% of the time.
Compared 7% of the time.
Also Known As
KNIME Analytics PlatformAzure Machine Learning
Learn
Knime
Microsoft
Overview
KNIME is the leading open platform for data-driven innovation helping organizations to stay ahead of change. Use our open-source, enterprise-grade analytics platform to discover the potential hidden in your data, mine for fresh insights or predict new futures.

Azure Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions.

It has everything you need to create complete predictive analytics solutions in the cloud, from a large algorithm library, to a studio for building models, to an easy way to deploy your model as a web service. Quickly create, test, operationalize, and manage predictive models.

Offer
Learn more about KNIME
Learn more about Microsoft Azure Machine Learning Studio
Sample Customers
Infocom Corporation, Dymatrix Consulting Group, Soluzione Informatiche, MMI Agency, Estanislao Training and Solutions, Vialis AG
Information Not Available
Top Industries
VISITORS READING REVIEWS
Software R&D Company23%
Comms Service Provider16%
Manufacturing Company9%
Financial Services Firm9%
VISITORS READING REVIEWS
Software R&D Company28%
Comms Service Provider19%
Manufacturing Company6%
Media Company5%
Company Size
REVIEWERS
Small Business27%
Midsize Enterprise27%
Large Enterprise45%
VISITORS READING REVIEWS
Small Business17%
Midsize Enterprise2%
Large Enterprise82%
No Data Available
Find out what your peers are saying about KNIME vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: November 2019.
377,029 professionals have used our research since 2012.
We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
Sign Up with Email