Azure Machine Learning vs. KNIME Analytics Platform

Azure can be a more unified product. It feels like 10 different tech teams were building it but we're not talking to each other. An example is when the user needs to know what is the next step. Automatically saving a previous state is very helpful as new users are usually not aware of the functionality.

Incentivized

KNIME Analytics Platform has vastly improved our effectiveness when working with large data sets. The self documenting GUI allows analysts to focus on what they are trying to accomplish, not complex code syntax. If we were to use traditional tools, like SQL, work would take much longer and it would be more difficult to collaborate both internally and with clients. Since KNIME Analytics Platform is database oriented, some spreadsheet functions are not supported, which is as it should be. For small data sets we often use Excel vlookup and pivot tables in place of KNIME Analytics Platform. If VBA code is requried, we go to KNIME Analytics Platform as we find VBA to be unstable in Excel.

• Easy to create the experiment.
• Easy to adopt the best algorithm.
• Efficient way to deploy the model as a web service.
• Centralized platform for the life cycle of machine learning goal.

• Visual programming as oppose to scripting encourages data analysts to reap deeper insights from their data
• Large community contribution in extending the KNIME Analytics Platform into other areas of analytics, e.g. Text Analytics, Predictive Analytics, ML, etc.
• Open source with periodic updates ensures it is equipped to deal with the most sophisticated data analytics use case

• Few models: Even though it has a lot of Machine Learning models, it is quite limited when compared to R. Most Data Scientists still use and prefer R, so the newest models tend to release as R libraries. With Azure ML, we need to wait for Microsoft to evaluate and decide if including a new model is a good idea or not
• Tableau interface: last time I checked there was no easy way to connect with Tableau.
• Cloud based: You always need a good internet connection to use it.

Incentivized

• Automation - e.g. RapidMiner Studio provides a Turbo Prep function, where one can get to working on models more quickly (RapidMiner is not open source though)
• KNIME does not replace a regular reporting tool - it is not meant to. However, if I have already spent some time developing a data acquisition and analytical model, it would be nice to be able to deploy, for example, a monitoring or reporting module that would process data autonomously and react accordingly.

Incentivized

We are happy with Knime product and their support. Knime AP is versatile product and even can execute Python scripts if needed. It also supports R execution as well; however, it is not being used at our end

Good UX/UI and overall good usability, but it takes a while to get used to the product & platform. The whole design seems fragmented with little in terms of integration with project management tools such as JIRA, or wireframing. Overall it feels like an unfinished product that's meant for teaching more than for production.

Incentivized

The training KNIME Analytics Platform provide helps you get to grips with a product that is already very intuitive. There is a KNIME Analytics Platform way of thinking about addressing problems, but once you understand a couple of patterns which you see again and again in your workflow it all makes sense.

Incentivized

I'm satisfied with the Azure Machine Learning Studio- it fulfilled my goal in a single channel. Even haven't worr[ied] about the maintenance or any fault tolerance. This provide[s] the user interactive UI to grab the features easily. [Their] support teams also very help[ful], they stand with us at any time.

KNIME's HQ is in Europe, which makes it hard for US companies to get customer service in time and on time. Their customer service also takes on average 1 to 2 weeks to follow up with your request. KNIME's documentation is also helpful but it does not provide you all the answers you need some of the time.

Incentivized

Not sure

KNIME Analytics Platform is easy to install on any Windows, Mac or Linux machine. The KNIME Server product that is currently being replaced by the KNIME Business Hub comes as multiple layers of software and it took us some time to set up the system right for stability. This was made harder by KNIME staff's deeper expertise in setting up the Server in Linux rather than Windows environment. The KNIME Business Hub promises to have a simpler architecture, although currently there is no visibility of a Windows version of the product.

Incentivized

The answer is quite simple: Microsoft Azure Machine Learning Workbench is the cheapest and most user friendly analytics tool I have ever seen! Unless you are running a team of data scientists, this is the tool to go. Most functions (marketing, sales, finance, supply chain, logistics, HR, R&D, etc.) could easily integrate Azure ML in its day to day activity.

Incentivized

There are two aspects which put KNIME Analytics Platform ahead of other products. Firstly the fact that KNIME Analytics Platform comes at no cost and no restrictions on its use is an instant winner for any organisation wanting to democratise their data. It means that a client is free to install it on as many machines as they wish without worrying about costs, the number of seats required or payment models or procurement negotiation. It also means that we are not building costs into our clients business. Secondly, KNIME Analytics Platform has a very comprehensive set of tools for importing/exporting data, data manipulation and data science. Some products offer analytics packages on top of their base offering at additional cost and they are still not as comprehensive as what you get with KNIME Analytics Platform for free. For some types of analysis you may require to download additional packages with KNIME Analytics Platform, but its invariably at no cost, those packages are kept out of the main download to keep the size down. Due to the easy integration with R and Python, I view KNIME Analytics Platform as also having the capabilities of those languages too. This has helped me in the past with seamlessly importing a rare filetype and using very specific models not directly available in KNIME Analytics Platform.

Incentivized

• It is easy to learn and construct, which impacts directly on productivity.
• Good for experimentation and validation for simple models.
• Has a use cost less than the best alternatives in the market.

Incentivized

• It is suited for data mining or machine learning work but If we're looking for advanced stat methods such as mixed effects linear/logistics models, that needs to be run through an R node.
• Thinking of our peers with an advanced visualization techniques requirement, it is a lagging product.

Incentivized