KNIME general.

Machine learning algorithms and the art of hyperparameter selection

Thu, 01/16/2020 - 10:00 admin

A review of four optimization strategies

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.

KNIME and AWS Machine Learning Service Integration

Thu, 01/09/2020 - 10:00 jfalgout

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.

Introducing More KNIME Hub Features

Mon, 12/16/2019 - 10:00 admin

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.

KNIME Server Profiles Simplify DB Driver Installation

Mon, 12/02/2019 - 10:00 Redfield

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.

The Best of Both Worlds: The Case for Visual Open Source Data Analytics

Thu, 11/28/2019 - 11:00 berthold

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.

Productionizing Data Science: E-Learning Course on KNIME Server

Mon, 11/25/2019 - 09:00 admin

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.

Build your CV based on LinkedIn profile with BIRT in KNIME

Mon, 11/11/2019 - 10:00 armingrudd

Author: Armin Ghassemi Rudd (Data Scientist & Consultant)

Are you trying to build an attractive CV? Maybe you’ve been searching the web for online CV builders? Using these online CV builders, you have to fill out a form and enter your information like name, contact information, skills, experiences, and so on. There are a few online CV builders that ease the job for you and ask for permission to access your LinkedIn profile and read your information. They are great tools for sure, but they have down points as well.

What Does It Take to be a Successful Data Scientist?

Wed, 11/06/2019 - 06:00 berthold

As first published in Harvard Data Science Review.

Abstract

Given recent claims that data science can be fully automated or made accessible to nondata scientists through easy-to-use tools, I describe different types of data science roles within an organization. I then provide a view on the required skill sets of successful data scientists and how they can be obtained, concluding that data science requires both a profound understanding of the underlying methods as well as exhaustive experience gained from real-world data science projects. Despite some easy wins in specific areas using automation or easy-to-use tools, successful data science projects still require education and training.

Deploying the Obscure Python Script: Neuro-Styling of Portrait Pictures

Mon, 11/04/2019 - 10:00 admin

Authors: Rosaria Silipo and Mykhailo Lisovyi

Today’s style: Caravaggio or Picasso?

While surfing on the internet a few months ago, we came across this study1, promising to train a neural network to alter any image according to your preferred painter’s style. These kinds of studies unleash your imagination (or at least ours).

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