Google Cloud AI vs. KNIME Analytics Platform

Google Images analysis model is a good one and I think is very useful in our case of detections. Speech AI is also a good one. I can only recommend Google Cloud AI API and the model for that second will be SpeechKit by Yandex both these tools have exceptional values one can utilise to enhance their projects.

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.

• Smart reply and its AI suggestions make the organization think more carefully about their e-mail responses in Gmail. We were skeptical at first but it really works well for many instances.
• We do a lot of business and contracts in Western Europe and South America, so the translate solutions make this much easier for our banking paperwork.
• When we go to meetings or during a meeting, we often use the Google voice search to save time on research and filtering ideas or analysis.

Incentivized

• 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

• Hard to find what to use - To find the right products, you need look closely at the details of each API, and find which suits your purposes. This can be easily fixed by creating a main page that details all of the products simply.
• Expensive - The API costs can quickly add up, especially during the setup process and as engineers figure out the usage of the API.
• No playground or training - There is a lack of an "API playground" or training sessions that could make onboarding engineers to this API much easier.

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 extremely satisfied with the impact that this tool has made on our organization since we have practically moved from crawling to walking in the process of generating information for our main task to investigate in the field through interviews. With the audio to text translation tool there is a difference from heaven to earth in the time of feeding our internal data.

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

I give 8 because although it´s a tool I really enjoy working with, I think Google Cloud AI's impact is just starting, therefore I can visualize a lot/space of improvements in this tool. As an example the application of AI in international environments with different languages is a good example of that space/room to improve.

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

Every rep has been nice and helpful whenever I call for help. One of the systems froze and wouldn't start back up and with the help of our assigned rep we got everything back up in a timely manner. This helped us not lose customers and money.

Incentivized

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

In fact, you only need the basic tech knowledge to do a Google search. You need to know if your organization requires it or not,. our organization required it. And that is why we acquired it and solved a need that we had been suffering from. This is part of the modernization of an organization and part of its growth as a company.

Incentivized

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

Google's documentation for their AI and Machine Learning products is a bit more straightforward and still much easier to onboard into compared to the Azure Machine Learning and other AI products. Additionally, Google's Cloud AI products provide more comprehensive specific use-cases that are API-optimized, and easier to integrate into existing scripts and backends.

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

• Positive impact on ROI due to reduction in staff needed to build, deploy and manage a AI workload pipeline.
• Positive impact on the business by moving to OpEx without need for upfront CapEx investment.
• Improvement in time to analyze the data (structured and unstructured), increasing the business's ability to act based on AI results.

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