Authors: Lionel Colliandre & Eric Le Roux, Discngine
The authors of this article, Lionel Colliandre and Eric Le Roux are both from Discngine, a company that operates in the life science field. The company serves the needs of chemists and biologists in research, helping organize their data, provide informatics services, screening, data acquisition, and enable better decision making.
By: Rosaria Silipo and Mykhailo Lisovyi. As first published in The Next Web.
Machine learning algorithms are used everywhere from smartphones to spacecraft. They tell you the weather forecast for tomorrow, translate from one language into another, and suggest what TV series you might like next on Netflix.
Author: Jim Falgout (KNIME)
Organizations are using cloud services more and more to attain top levels of scalability, security, and performance. In recent years, the Amazon Web Services (AWS) cloud platform has released several services that support machine learning (ML) and artificial intelligence (AI) capabilities to enable developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.
In our latest release of KNIME Analytics Platform, we added more functionality to our KNIME Amazon Machine Learning Integration. Think of KNIME as a quick and powerful start to AWS Services now enabling greater interaction between the various AWS services.
Authors: Kathrin Melcher, Tobias Schmidt, Christian Dietz (KNIME).
Do you remember the blog post “The KNIME Hub - Share and Collaborate”, where we introduced the new KNIME Hub and its first features? Since then our developers have been implementing a lot of additional functionality to make it even easier for you to find and share insights with the community. Learn more in this article.
Author: Rosaria Silipo (KNIME). As first published in DarkReading.
The challenge is to identify suspicious events in training sets where no anomalies are encountered. Part two of a two-part series.
The problem of anomaly detection is not new, and a number of solutions have already been proposed over the years. However, before starting with the list of techniques, let's agree on a necessary premise: All anomaly detection techniques must involve a training set where no anomaly examples are encountered. The challenge consists of identifying suspicious events, even in the absence of examples.
Author: Rosaria Silipo (KNIME). As first published in DarkReading.
Yes! You can predict the chance of a mechanical failure or security breach before it happens. Part one of a two-part series.
Authors: Jan Lindquist and Johan Tornborg (Redfield)
In the modern business environment, companies have to support a heterogeneous combination of operating systems and technologies. Defining customizations and profiles for these combinations simplifies deployment, speeds up rolling out changes, and generally makes it easier to onboard new users.
KNIME Server has a feature that greatly simplifies the installation of database drivers. Installing drivers in KNIME Analytics Platform involves a number of steps from finding the specific driver, accepting the conditions to use it, downloading the driver, and moving it to a folder within your KNIME installation, which then has to be referred to. That means quite a large number of potential failure points, especially when users have different levels of operating system experience. Any type of manual configuration can introduce errors; it's good to make this kind of process as automatic as possible; KNIME Server profiles achieve this kind of automation.
Author: Michael Berthold (KNIME). As first published in The New Stack.
There is a big push for automation in data science today. Given how complex programming data science applications can be, that is no surprise. It takes years to truly master scripting or programming languages for data analysis — and that’s ignoring that one needs to build actual data science expertise as well. However, code-free solutions can make the nuts and bolts of data science a lot more accessible. This means that the valuable time of data science teams can be spent on actually doing data science so that organizations don’t have to rely on an external, preconfigured, and intransparent data science automation mass product.
Authors: The Evangelism Team at KNIME
End to end data science is a phrase you might have read before, but what does it mean?
The terms “read - transform - analyze - deploy” describe the typical phases of a data science project. We often have our favorite phases of the model we enjoy focusing on, but data science is only end to end if the final deployment phase is also included. This is where data science brings value to a business, when the data science applications that have been created are subsequently productionized.
Authors: Rosaria Silipo and Paolo Tamagnini (KNIME)
Nobody is an island. Even less so a data scientist. Assembling predictive analytics workflows benefits from help and reviews: on processes and algorithms by data science colleagues; on IT infrastructure to deploy, manage, and monitor the AI-based solutions by IT professionals; on dashboards and reporting features to communicate the final results by data visualization experts; as well as on automatization features for workflow execution by system administrators. It really seems that a data scientist can benefit from a community of experts!
