Our trusted partner, GEMMACON, is hosting a Course for KNIME Analytics Platform in San Francisco, California on June 6-7, 2019.

This course teaches you everything you need to get going - from installation, to navigating around the various sections, through to fully utilizing KNIME Analytics Platform. Learn how to read and manipulate data, data visualization techniques, and how to export and deploy workflows. Explore advanced data mining techniques and generate reports based on KNIME workflows. Finally, dive into controlling workflows with Loops and Flow Variables. Test everything you learn in the hands-on session by going through a full churn prediction use case and taking a look into the future weather. This course is designed primarily for those who have little to no previous experience with KNIME, but is also useful for those who simply want to fine tune their knowledge.

Course Content

Thursday June 6, 2019

• Introduction to KNIME
• Reading Data
• Data Manipulation
• Data Vizualization
• Data Mining
• Exporting & Deployment
• Group Dinner

Friday June 7, 2019

• Date and Time
• Flow Variables
• Workflow Control
• External Tools
• Advanced Data Mining
• Model Selection
• Advanced Applications and/or BYOD (Bring Your Own Data)

Course Fees

• Course for KNIME Analytics Platform (2 days): 1'250 USD

Course Location

530 Hampshire St #306, San Francisco, CA 94110

Registration

Our trusted partner, HupData, is running this Data Coffee TECH on Data Science, an event to focus on predictive modeling.

Join HupData to explore a practical journey, involving development, automation, and the production of scaleable Data Science projects.

Topics:

• Getting and Preparing Big Data
• Machine Learning Algorithm Processing
• Boosting ML with Spark, PySpark, H2O Sparkling Water

Sign up for the Data Coffee TECH by April 23 at HupData's registration page here.

Note: No programming knowledge is required to attend; just bring your computer with KNIME Analytics Platform installed.

Our 3rd annual KNIME Fall Summit is taking place from November 6-9, 2018 in Austin, Texas! Join us, along with top data scientists and industry thought leaders, for four days of learning more about KNIME Analytics Platform and how it's being used to solve complex data problems in areas such as life sciences, manufacturing, marketing, retail sales, and more. There'll also be plenty of opportunities to meet the people behind KNIME and hear about new and upcoming features, as well as discuss all things data science and data analytics with other KNIME users and enthusiasts.

Overview – Courses– Agenda – Venue– Register

Since many of last year's attendees asked for more courses, we are now starting a day earlier and offering courses on both Tuesday and Wednesday before the Summit begins on Thursday.

Overview– Courses – Agenda – Venue– Register

On November 6 & 7 we are offering several one-day KNIME courses that cover a variety of topics. Your Summit registration allows you to select either one or two of these full day courses:

Tuesday, November 6, 2018:

• KNIME Analytics Platform for Beginners: From installation to utilization and everything in between. Room 203.
• Text Mining with KNIME Analytics Platform: Using the Textprocessing Extension. Room 101.
• KNIME Big Data Extensions: Data Mining with Apache Hive and Apache Spark. Room 202.
• Advanced Analytics Methods with KNIME Analytics Platform. Room 104.

Wednesday, November 7, 2018:

• Advanced Users in KNIME Analytics Platform: Beyond the basics. Room 203.
• Text Mining with KNIME Analytics Platform: Using the Textprocessing Extension. Room 101.
• KNIME Big Data Extensions: Data Mining with Apache Hive and Apache Spark. Room 202.
• IoT Analytics with KNIME Analytics Platform. Room 106.
• The Power of Random: Using Perturbation Experiments to Improve Model Accuracy and Interpretation. Presented by Dean Abbott, SmarterHQ. Room 104.

Overview– Courses – Agenda – Venue– Register

Here is the preliminary agenda for KNIME Fall Summit:

Thursday, November 8

9:00 AM - Registration & Coffee

10:00 AM - Welcome & Opening - Slides

• Michael Berthold (KNIME)

10:30 AM - What's New & Cooking - What's New Slides,What's Cooking Slides

• Bernd Wiswedel & Team (KNIME)

1:00 PM - Lunch

2:00 PM - Session 1

• Finding Themes in Text Data to Help Transform Member Experience
  Melvi M. Methippara (Kaiser Permanente)
• Using Analytics to Improve Consumer Choice in the US & the UK - Slides
  Michelle Leonard & Doris Sullivan (Consumer Reports)
• Advanced Job Analytics @ Daimler - Slides
  Julian Leweling (Daimler)

3:30 PM - Coffee Break

4:00 PM - Session 2

• Data Science at Palo Alto Networks: How Do We Innovate?
  Nandan Thor & Sirish Upadhyay (Palo Alto Networks)
• Data Science at Palo Alto Networks: How Do We Productionize?
  Juho Parviainen & Nilesh Dhomse (Palo Alto Networks)
• Keynote - Doing the Data Science Dance: The Interplay Between Algorithms, Data Preparation, Sampling, and Interpretation - Slides
  Dean Abbott (SmarterHQ)

6:00 PM - End of Summit Day One

6:40 PM - Shuttle Buses to Micheladas

7:00 PM - Dinner at Micheladas

Friday, November 9

9:00 AM - Registration & Coffee

9:30 AM - Session 3

• On Monsters and Tags... - Slides
  Jeany Prinz & Greg Landrum (KNIME)
• Deploying KNIME in an Amazon Cloud Environment for High-Throughput Image Analysis - Slides
  Andries Zijlstra (Vanderbilt/Nashville)
• A Data Pipeline Approach to Orphan Disease Insights - Slides
  Sebastian Lefebvre (Alexion Pharmaceuticals)

11:00 AM - Coffee Break

11:30 AM - Session 4

• Guided Analytics at Seagate - Slides
  Allan Luk & Eric Lin (Seagate)
• Data Analytics in Data Storage Device Development & Testing - Slides
  Debin Wang (Seagate)
• Using KNIME for Optimizing Die Utilization - Slides
  Zachary Eich (AMD)

1:00 PM - Lunch

2:00 PM - Session 5

• Guided Analytics for ML/AI Automation - Slides
  Christian Dietz & Simon Schmid (KNIME)
• Enterprise Scale Data Blending - Slides
  Shalini Subramanian (Juniper Networks)
• REST API: Workflow Integration with Python - Slides
  Owen Watson (Juniper Networks)

3:15 PM - Coffee Break

3:30 PM - Session 6

• Custom Language Translation using KNIME & Keras - Slides
  Mohammed Ayub & Joseph Gochal (NFPA)
• Creating an Equipment Anomaly Detection Framework - Slides
  Ziad Katrib (Calpine)
• Turning AI Hype into Something Practical: Demystifying Bots - Slides
  Phil Winters & Vincenzo Tursi (KNIME)

5:00 PM - Farewell Reception/Open Bar

7:00 PM - End of Fall Summit

Overview– Courses– Agenda – Venue – Register

The Fall Summit will once again be held at AT&T Executive Education and Conference Center.

Overview– Courses– Agenda – Venue– Register

We’ve just released the latest versions of KNIME Analytics Platform and KNIME Server. Here’s a quick summary of what’s new.

With KNIME Analytics Platform 4.0, there are many new features that allow easier sharing with the community, as well as a tighter integration with the new KNIME Hub.

Speaking of which... with this release, we’ve got a bunch of exciting new features. Share workflows and components publicly on the KNIME Hub, browse KNIME extensions, drag and drop nodes into your KNIME workbench, and search the KNIME Hub from within KNIME Analytics Platform.

Components are another change that make sharing and collaborating with the community easier as they are able to bundle up functionality, which can be shared and reused. They can be used for providing sophisticated methods for time series analysis, offering preconfigured visualizations for specific data types, or they can focus on specific application areas. For more information, check out this blog post.

This release features significant performance improvements, meaning you should notice considerable speedups of factors two to ten in your day-to-day workflow execution when working with native KNIME nodes. Under the hood, these improvements are due to, among others, use of columnar storage, and better in-memory processing.

We’ve got a new database extension, new machine learning functionality, and an integration with Plotly, which brings all kinds of interactive visualizations. For big data, there’s simplified support for Kerberos, a Spark Repartition node, and revised Spark model learner nodes. And, remember the community survey we did recently? Something many of you requested has resulted in one of our newest nodes - the Duplicate Row Filter!

With KNIME Server 4.9, you can edit workflows directly on KNIME Server with the Remote Workflow Editor. We’ve also made improvements to the scheduling feature, and you can now choose which executor type a specific workflow should be executed on. On top of our existing cloud offerings, KNIME Server Large is now available on AWS, and KNIME Server Small on Azure.

These features and more are summarized here, as well as in the detailed changelog. We’re also hosting a webinar on July 25 highlighting all the new stuff! The best way to check it out, though, is by trying it for yourself. Open KNIME and go to File -> Update KNIME to get the latest version, or download and install it here.

Remember to mark your calendars for KNIME Fall Summit in Austin, Texas from November 5-8, 2019, where you can see these new features in action! Register now for a $100 early bird discount.

Our trusted partner, AdapticAI, is hosting a Course for KNIME Analytics Platform in New York, NY on September 17 - 18, 2019.

This course teaches you everything you need to get going - from installation, to navigating around the various sections, through to fully utilizing KNIME Analytics Platform. Learn how to read and manipulate data, data visualization techniques, and how to export and deploy workflows. Explore advanced data mining techniques and generate reports based on KNIME workflows. Finally, dive into controlling workflows with Loops and Flow Variables. Test everything you learn in the hands-on session by going through a full churn prediction use case and taking a look into the future weather. This course is designed primarily for those who have little to no previous experience with KNIME, but is also useful for those who simply want to fine tune their knowledge.

Course Content

Tuesday September 17, 2019

• Introduction to KNIME
• Reading Data
• Data Manipulation
• Data Vizualization
• Data Mining
• Exporting & Deployment
• Group Dinner

Wednesday September 18, 2019

• Date and Time
• Flow Variables
• Workflow Control
• External Tools
• Advanced Data Mining
• Model Selection
• Advanced Applications and/or BYOD (Bring Your Own Data)

Course Fees

• Course for KNIME Analytics Platform (2 days): 1'250 USD

Course Location

WeWork, 205 Hudson St, New York, NY 10013, USA

Registration

Our 4th annual KNIME Fall Summit is taking place from November 5-8, 2019 in Austin, Texas. Join us, along with top data scientists and industry thought leaders, for four days of learning more about KNIME Software and how it's being used to solve complex data problems in areas such as life sciences, manufacturing, marketing, retail sales, and more. Meet the people behind KNIME and hear about new and upcoming features, as well as discuss all things data science and data analytics with other KNIME users and enthusiasts.

Confirmed Speakers

• Dean Abbott (SmarterHQ)
• Laura McElhinney (Horizon Media)
• SJ Porter (HireRight)
• Sebastien Lefebvre (Alexion)
• Erik Bower, Jere Helenius, Deepthi Katta (Palo Alto Networks)
• Kenneth Longo (Wave Life Sciences)

Courses

On November 5 & 6 we are offering several one-day KNIME courses that cover a variety of topics. Your summit registration allows you to select either one or two of these full day courses. More courses will be added over time.

Tuesday, November 5, 2019:

• KNIME Course for Data Wranglers: Access, merge, transform, and inspect your data
• KNIME Analytics Platform for Beginners: From installation to utilization and everything in between
• Text Mining with KNIME Analytics Platform: Using the Textprocessing Extension
• KNIME Big Data Extensions: Data Mining with Apache Hive and Apache Spark
• KNIME Server Course
• KNIME Refresher Course: Master the Latest KNIME Features
• Machine Learning in KNIME: More than an Introduction

Wednesday, November 6, 2019:

• Advanced Users in KNIME Analytics Platform: Beyond the basics
• Text Mining with KNIME Analytics Platform: Using the Textprocessing Extension
• KNIME Big Data Extensions: Data Mining with Apache Hive and Apache Spark
• KNIME Server Course
• KNIME Refresher Course: Master the Latest KNIME Features
• The Power of Random: Using Perturbation Experiments to Improve Model Accuracy and Interpretation. Presented by Dean Abbott, SmarterHQ.
• KNIME Time Series Analysis Course: Looking at the Internet of Things

Venue

The summit will once again be held at AT&T Executive Education and Conference Center. We have booked a contingency of hotel rooms for summit attendees. Please reserve your room using this link.

Register

Together with Dr. Andries Zijlstra, Associate Professor at Vanderbilt University, we submitted a project for the Strata Data Awards.

The Strata Data Awards recognize the most innovative startups, leaders, and data science projects from Strata sponsors and exhibitors around the world. This project was submitted under the “Data Impact” category, which recognizes projects or initiatives that use data science and analytics to have a widespread, positive impact on society. The project was aimed at improving identification of, and medical intervention for men with aggressive prostate cancer. Below is more information and an overview of the project.

KNIME Image Processing Extension for Biomedical Image Analysis/Analysis of Human Tissue Cells to Aid Diagnosis of Aggressive Cancer. 

In spite of many advances in diagnosing cancer patients, few methods can distinguish between aggressive and benign disease. Machine Learning (ML) on cellular features captured through digital pathology holds enormous promise for providing a means to identify cancers with lethal potential before curative intervention is no longer possible. To achieve such transformative implementation of AI in medicine we pursued true integration of single-cell biology, imaging, statistics, computer sciences and informatics using the open computational environment KNIME.

This project’s goal is to achieve improved outcome for men with prostate cancer with personalized/precision medicine; reduce resource allocation through improved diagnosis, prognosis & treatment. To achieve this, a pipeline encompassing all expertise achieved accurate mapping of subcellular alterations associated with aggressive prostate cancer which was subsequently used to assess the risk of future disease progression.

The first milestone of the project was a proof-of-principle that demonstrated the success of integrating multiple non-computational expertise into an open & advanced computational platform without requiring re-training of clinical personnel or translation of mission objectives in computational terms. In excess of 5 million cells from 3000 images taken from 500 patients were used for the discovery and subsequent quantitative analysis of a novel subcellular alteration present in the nucleus of aggressive prostate cancer. These quantiations are subsequently used to predict which patients are at risk of future cancer progression and/or recurrence.

This initiative demonstrates the critical need for open platforms that enable the integration of multiple fields. In particular, the project has demonstrated that KNIME Analytics Platform lowers the threshold for participants across fields to participate in the building of complex AI environments that can be deployed to ask clinical questions. The specific product of this strategy is a computer-guided prediction of patient outcome. It takes into consideration the complexity of the disease at a single-cell level. Beyond the immediate technological gains and academic understanding, this work matches an ongoing evolution in digital pathology that will undoubtedly reshape how patients are diagnosed and how treatment decisions will be made in the clinic.

The Strata Data Conference is taking place in New York City from September 23 - 26. If you’re going, make sure you visit us at booth #1254. Andries will also be there to explain his project in more detail and, if you think it's a deserving candidate, you can vote for it during the conference. Winners will be announced on September 26.

Come to a meetup on October 1, 2019 hosted by our trusted partner GEMMACON at the ActionSpot co-working space in the City of San Jose.

The evening starts with a meet & greet and welcome drink, as well as a brief introduction to GEMMACON - who the company is and what they do! The focus of this meetup is hands-on examples. We would like to demonstrate a use case - an example of how GEMMACON is using KNIME Analytics Platform.

If you’re new to KNIME, don’t worry! There will be an introduction to KNIME Software and we’ll also be explaining what you can learn at the upcoming 2-day KNIME User Training in San Francisco, later in October.

Agenda

• 4:30-5:00 PM Meet & Greet over Welcome Drink
• 5:00-5:15 PM Opening Speech by GEMMACON: Who we are - What we do - What we want to achieve at this meetup
• 5:15-6:00 PM Introduction to KNIME Software
• 6:00-7:00 PM Use Case by GEMMACON using KNIME
• 7:00-7:15 PM Introduction to KNIME User Training
• 7:15-8:00 PM Networking with drinks & snacks
• 8:00-8:30 PM Meetup comes to an end

Location

• ActionSpot, 453 W San Carlos St. San Jose, CA

Register

Sign up to come to the meetup on the Bay Area KNIME Users meetup.com page here!

Join us, together with our trusted partner LIS-Solutions, on October 10, 2019 for a meetup in Madrid. 

Manuel Coterillo, LIS-Solutions co-founder, will kick-off the event with his talk "Data Consulting", which will focus on digital transformation and how companies are dealing with it. He will present the ecosystem of data and how data-driven services can support a successful digital transformation. The second talk “Create and Productionize Data Science” by Vincenzo Tursi from KNIME will introduce the CRISP-DM cycle and explain how KNIME Software supports data scientists and business users through all phases of the process.

Data science applications can help optimize business operations and offer great potential for companies. The third talk “Bring your Company to a New Level: Data Science and KNIME” by Adolfo Garandal, will highlight how KNIME can be applied within companies to create value and promote the development of a data-driven company culture. After a short break, we’ll have a presentation by a LIS-Solutions and KNIME customer, where they’ll highlight the implementation of a use case - using both KNIME Analytics Platform and KNIME Server. The talks will be rounded off with a churn prediction demo by Juan Kempe from LIS-Solutions. He’ll demonstrate how to build a KNIME workflow that predicts customer churn. After the talks there will be time for networking over snacks and drinks.

Please note: Some talks will be held in English and some will be done in Spanish

Agenda

• 10:00 – Event Opening 
• 10:05 – Data Consulting – LIS Solutions (Manuel Coterillo)
• 10:20 – Create and Productionize Data Science – KNIME (Vincenzo Tursi)
• 10:40 – Bring your Company to a New Level: Data Science and KNIME – LIS-Solutions (Adolfo Garandal)
• 11:00 – Coffee Break
• 11:10 – Client Presentation 
• 11:25 – Churn Prediction with KNIME (Demo) – LIS-Solutions (Juan Kempe)
• 11:45 – Networking 

Location

Torre Europa, Paseo de la Castellana 95, Planta 28, 28046 Madrid, Spain

Registration

Please register for this event via the LIS-Solutions registration page.

About the speakers

Manuel Coterillo co-founder of LIS-Solutions

Vincenzo Tursi is Partner Manager at KNIME

Adolfo Garandal is Senior BI Consultant at LIS-Solutions

Recently, KNIME was in Munich - pre-Octoberfest - for two meetups: one hosted together with our trusted partner GEMMACON, and another - an internal Siemens-wide meetup. Both events had a great response and lots of participants asked us when the next meetups in Munich would be. Here is a quick summary of these events:

KNIME Meetup with GEMMACON: September 11, 2019

This event started with a pre-meetup workshop “Getting Started with KNIME Analytics Platform”. After a short introduction, people started building their first workflow, which was a great opportunity for all the newbie KNIMErs to really try things out and ask specific questions. The workshop was followed by three presentations - the first being “Automated Retrieval of Market & Competitive Information using KNIME” by Matthias Stephan from Siemens. After an introduction to how KNIME Analytics Platform is used at Siemens, he presented two use cases showing how KNIME can be used to replace repetitive tasks using web crawling and text mining. GEMMACON was up next with an ETL use case, and Kathrin Melcher from KNIME wrapped up the meetup with a presentation on anomaly detection with machine learning. Over thirty people attended and, whilst most attendees knew KNIME already, they were happy learning about new tips & tricks such as the node monitor. The lively networking session at the end was a nice way to round off the evening.

Siemens Meetup: September 12, 2019

On Thursday we joined a full-day, internal meetup, which was organized by the Siemens DataVisions team, together with Robotic Land. Around 70 people from various organizations at Siemens attended the meetup. In the morning, we had four parallel tracks, where those who already knew KNIME could either:

• Attend the introductory session for getting started with KNIME Analytics Platform
• Take the KNIME certification
• Learn more about KNIME Server and take part in the open floor discussion
• Join the ‘Bring Your Own Data’ session to get help on specific projects

The afternoon sessions were all about demonstrating how KNIME is being used at Siemens. Two use cases were shared - one on calculating bonuses and the other on developing business plans. The internal digitalization platform was also presented, which hosts tutorials and use cases and provides a platform for connecting and collaborating. Read the article all about this particular event in the post “Five Takeaways from the First KNIME Meetup@Siemens” by Matthias Stephan, Team Lead, Data Visions, Digital Industries, at Siemens.

We are already looking forward to future KNIME events in Munich. Look out for these at www.knime.com/events.

KNIME will be at the GCC Conference in Mainz, from November 3-5, 2019. The GCC Conference aims to reflect and highlight the new role of cheminformatics in the modern information world.

We will hold a hands-on workshop: “Exploring and learning from chemical data with KNIME Analytics Platform”. At this workshop we’ll introduce various cheminformatics functionality of KNIME Analytics Platform and you’ll learn to exploit all of them. In the workshop we’d like to convince you that with KNIME you have the power and simplicity to rapidly prototype ideas, share complex analyses with colleagues, and load and integrate data from diverse data sources.

We made the workflows available and they can be accessed via the link below

From November 5-8 we held our 4th annual KNIME Fall Summit in Austin, TX. We welcomed over 250 people through the doors this year - a mix of KNIME users and enthusiasts, as well as data experts from a variety of industries.

Here are all the highlights:

Plus a few nice impressions:

This was our biggest summit in the US so far and, as always, was packed with data science in action. No worries if you missed it: you can look up some of the talks, come to Berlin in spring next year, or join us next year in Austin (date to be confirmed very soon!)

Michael Berthold, KNIME CEO, opened the Summit by showing how to automate and guide selected parts of the data science life cycle. Dean Abott, SmarterHQ Founder and Chief Data Scientist, closed with his keynote presentation on how to enable your models to speak the language of business. We were overwhelmed by the impressive presentations from our users, which covered a diverse range of use cases across different industries. Here are a select few:

• Using KNIME to Build a Data-Driven Culture (and Workflows!) in a Biopharma Setting
• Visualizing with Guided Analytics: For End Users AND Experts
• Virtual Anticipation Network: Applying Tensor Completion to the Study of Inter-State Competition and Irregular Conflict

Check out all slides and recordings here (we’re adding to this page daily).

On Tuesday and Wednesday we ran our KNIME courses - including the new Time Series Analysis course, which was a big hit (we even had to pull in some extra seats!) We ran the KNIME Refresher Course for the first time in Austin, and our Partner, Elder Research, ran a course on machine learning with KNIME. At the summit, attendees were also able to sign up and do the KNIME certification.

It was great hosting everyone in Austin and we are looking forward to our upcoming KNIME Spring Summit from March 30 to April 3 2020 in Berlin. Secure your spot now with an early bird ticket.

KNIME just got stronger in guided analytics, sharing knowledge, and enterprise usage. The 2019 Santa Claus release strengthens the way to share, search, and reuse knowledge on the KNIME Hub with components. It adds guided labeling, and includes many additional nodes, integrations like Google Cloud Services, Databricks, Power BI, and OAuth identification.

Leverage your data with Guided Labeling

We believe that data science shouldn’t be a black box. The data scientist should be able to build powerful analytical applications that allow interaction with the domain expert whenever their expertise and feedback is required. We call this Guided Analytics. Now, in this release we introduce Guided Labeling. Using machine learning (ML) techniques (active learning, weak supervision), the domain expert can efficiently label unlabeled data in a guided setting, saving hugely on resources and getting the right data fast. This solves one of the big problems in ML: you need a lot of labeled data to get meaningful models. Find the details on our what’s new page.

What about new nodes?

There’s a new addition to the ML Interpretability Extension - the Binary Classification Inspector - that makes comparison of binary classifiers easier, applying the best thresholds for each different machine learning algorithm. The WebRetriever node is new too. Use it to issue HTTP GET requests and parse the requested HTML web pages. Tucked into the KNIME Labs Extensions, you’ll now find a Row Filter (Labs) node for filtering data based on multiple conditions. Find the complete list of new nodes in the changelog.

Share, find, and reuse knowledge on the Hub

Are you already using the KNIME Hub - our central resource for data science expertise? Since the summer we’ve increased its functionality. Sharing components, for example: Shared in your public space, you provide your colleagues and the KNIME community with ready-made building blocks to perform repetitive or difficult data science tasks easily. Shared in your private space, your components are stored in a central easy to access location. 

Now, in addition to new input filtering and validation, this release enables component editing outside of a workflow, adding component descriptions, plus an image and category (or color). Learn more here.

Even more Cloud Connectivity and Databricks

Organizations are using cloud services more and more to attain top levels of scalability, security, and performance. You can now interact with the popular Google Cloud services such as Google BigQuery, Google Cloud Storage, and Google Cloud Dataproc. Another new integration to give more flexibility is support to connect to your Databricks cluster on Microsoft Azure or Amazon AWS. And… in addition to our AWS Comprehend nodes, this release provides AWS Personalization nodes to increase support for AWS machine learning services. Power BI is a further integration: datasets created through KNIME can be uploaded to Power BI dashboards. See the specs here. 

SSO and more flexibility for the enterprise

KNIME Server now supports the open standard for authorization: OAuth identification. When set up for KNIME Servers, Single Sign-On is also supported for connections from KNIME Analytics Platform clients. It can also be used together with the KNIME WebPortal.

By popular request, we’ve tweaked our Server Managed Customization: You can host one or more update sites in your own network, giving easy access to all users who need to install additional extensions.

It’s also now easier to configure workflows running on KNIME Server. You can directly access configuration node dialogs (introduced in KNIME Analytics Platform 4.0) to adjust settings ad hoc: change credentials, for example, or file selections. Learn more about this KNIME Server release here.

More? Yes more!

Here, we’ve highlighted just some of the new functionality provided in the 2019 Santa Claus release. Read more on our what’s new page and the complete list of new nodes, enhancements, and bug fixes in our changelog. 

ZURICH and AUSTIN, Texas — January 10, 2020 —KNIME, a unified software platform for creating and productionizing data science, today announced the availability of KNIME on AWS, its commercial offering for productionizing artificial intelligence (AI)/machine learning (ML) solutions on Amazon Web Services (AWS). KNIME on AWS is designed to allow customers to assemble and deploy ML solutions across the enterprise at scale and securely on AWS and to gain tangible value quickly. The offering is now featured in AWS Marketplace, including free trials.

Many enterprises seek to create value by deploying ML and AI solutions but can lack the data scientists, data platform engineers, experience, money and time necessary to make a meaningful impact quickly. The result is that teams and individuals lacking this set of highly technical skills are left out of the innovation loop and are unable to realize the potential that their data offers. Further, there are many steps in the process of bringing an AI/ML solution into production that require a transfer of context and knowledge from data preparation to analysis and modeling to deployment.

KNIME on AWS is a visual data workflow editor that allows customers of all skill levels to extract and prepare their data from Amazon Simple Storage Service (Amazon S3), Amazon Redshift, or other sources; utilize AWS AI/ML services along with custom data science to build an impactful model; and deploy this solution “as a service” or to an analytics application. In each step, the solution is underpinned by the storage, compute, security and scale of AWS. This end-to-end solution from data to deployment can be realized with no coding required, and scheduling/automation can be employed in order to create a continuous stream of insights or decisions with minimal manual effort required.

Eli Feldman, CTO of Advanced Technology at EPAM, explained, “As an Advanced Consulting Partner in the AWS Partner Network (APN) that has developed KNIME connectors for seamless data integration and visualization, EPAM is uniquely positioned to help enterprise customers realize the full benefits of KNIME on AWS. As more companies leverage next-gen technologies like AI and ML for better decision-making, we’re proud to work with AWS and KNIME to help our customers gain data-driven insights to achieve greater agility in driving innovation.”

Added Paul Treichler, vice president of global partnerships at KNIME, “Enterprises across all sectors are collecting and storing vast amounts of data in order to make better decisions, but few are realizing its full potential. Our relationship with AWS fills the gap for customers looking for a no-code data analytics solution that can make it easier for the community to productionize data science on enterprise-scale using AWS services. Top-tier APN Partners like EPAM bring their domain expertise to drive impact with these solutions for the largest companies in the world.”

KNIME on AWS covers a broad array of industries and use cases and is being utilized today by customers in such industries as manufacturing, high tech, media, education and life sciences as well as use cases from predictive analytics to image and text analysis to time series analysis. The transparency, flexibility and speed of the platform support functions from rapid prototyping to governance, audit and scaling, including compute, execution or across a big data environment. KNIME on AWS is utilized by enterprises to encapsulate, automate and scale the delivery of data-driven insights to the business to allow teams to focus on execution and optimization.

KNIME on AWS is designed to represent a meaningful improvement in speed to value for customers with technical capability constraints who wish to utilize AWS AI/ML services. As of the KNIME Analytics Platform 4.1 release, Amazon Translate, Amazon Comprehend, and Amazon Personalize are available as native nodes. Other AWS services are immediately accessible using the Boto3 Python library integration with KNIME. More details are available at www.knime.com/blog/knime-on-aws.

KNIME on AWS is available through AWS Marketplace or directly through KNIME and consists of the open source KNIME Analytics Platform and the commercial KNIME Server, which can be purchased on an hourly or annual basis.

About EPAM Systems

Since 1993, EPAM Systems, Inc. (NYSE: EPAM) has leveraged its software engineering expertise to become a leading global product development, digital platform engineering, and top digital and product design agency. Through its ‘Engineering DNA’ and innovative strategy, consulting, and design capabilities, EPAM works in collaboration with its customers to deliver next-gen solutions that turn complex business challenges into real business outcomes. EPAM’s global teams serve customers in more than 30 countries across North America, Europe, Asia and Australia. As a recognized market leader in multiple categories among top global independent research agencies, EPAM was one of only four technology companies to appear on Forbes 25 Fastest Growing Public Tech Companies list every year of publication since 2013 and was the only IT services company featured on Fortune’s 100 Fastest-Growing Companies list of 2019. Learn more at www.epam.com and follow us on Twitter @EPAMSYSTEMS and LinkedIn.

About KNIME

KNIME is the software platform for creating and productionizing data science, helping organizations drive innovation through its fast, easy and intuitive visual data workflow editor. For over a decade, a thriving community of data scientists in over 60 countries has been working with the KNIME Analytics Platform on every kind of data: from numbers to images, molecules to humans, signals to complex networks, and simple statistics to big data analytics, enabling data scientists, data engineers, IT, data analysts and business analysts. Enterprise collaboration is enabled through open source and commercial software and global community forums. Headquartered in Zurich, KNIME has offices in Austin TX, Konstanz and Berlin. Learn more at www.knime.com.

KNIME, KNIME Analytics Platform, KNIME Server, KNIME Forum, and KNIME Hub are trademarks of KNIME. All other brand names and product names are trademarks or registered trademarks of their respective companies.

Tags: KNIME, AWS, AWS re-invent, AWS Marketplace, data science, data scientists, data analytics, data wranglers, machine learning, deep learning, data prep/ETL, artificial intelligence, AI, ML, EPAM

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Contacts:

Paul Treichler
Vice President, Partnerships
KNIME Inc.

paul.treichler@knime.com

Dottie O’Rourke
TECHMarket Communications
(650) 344-1260

KNIME@techmarket.com

Groundbreaking approach removes the gap between creating data science and using it in production

Virtual KNIME Spring Summit 2020 dives into the future of data science

ZURICH and BERLIN — April 1, 2020 —KNIME today unveiled a groundbreaking approach — Integrated Deployment — to eliminate the gap between the creation of data science models and their use in production.

Integrated Deployment allows not just a model but all of its associated preparation and post- process steps to be identified and automatically reused in production with no changes or manual work required. From within the KNIME platform, organizations can replicate the process repeatedly with ease to maintain model performance.

• This not only saves massive amounts of time and frees data science and model operations resources, it also dramatically reduces the risk of errors that can occur when moving from creating a model to deploying a complete production process based on that model.

• Another benefit is that good governance and compliance reporting for such topics as GDPR and CCPR are fully supported since the entire creation and production processes are captured and stored in self-documenting workflows.

“Our open approach and close collaboration with the community means that KNIME is always at the forefront of what is possible in data science. Integrated Deployment represents another big step forward,” said Michael Berthold, CEO and co-founder of KNIME. “This solves perhaps one of the biggest problems in data science today by completely eliminating the gap between the art of data science creation and moving the results into production.”

Integrated Deployment is being unveiled today by Berthold in his livestreamed keynote presentation during the virtual KNIME Spring Summit 2020: www.knime.com/integrated-deployment.

Closing the gap: why integrated deployment matters

Integrated Deployment is significant because virtually all business topics that use decision science are affected by this gap. For example, a mobile provider might develop a model to predict whether customers will renew their contracts. This model relies on call transaction data, payment data, and information about support provided. The iterative model creation process discovers that the best model is made by combining 15 pieces of data. Nine of these pieces do not exist in the raw data but were created using both traditional mathematics as well as advanced techniques. The model method itself has had settings tuned for best performance.

Until now, the process of moving that model into production and applying it to new customers has required manual replication of the exact data creation and model settings to ensure that the model could be usable in production. With KNIME Integrated Deployment, however, the created model as well as all required steps and settings are automatically captured and packaged so that the entire production process is, for the first time, instantly available for production use.

Back to basics: KNIME refines end-to-end data science

KNIME’s Integrated Deployment approach represents the next step in the evolution of data science. Traditionally, the end-to-end data science process starts with raw data and ends with the creation of a model, but the model cannot be moved into daily production use without a lot of additional work. This is because every machine learning model uses data that have been specially optimized for it. When that model is made available in production, it requires the data in exactly the correct form.

Data science offerings to date have allowed data scientists to save the model and provide access to their library for production use, but the process of recreating the exact data required by the model is manual and involves investigating the optimized creation process to identify just those final steps required. This is then followed by manually recoding or moving portions of that create process to generate a production process. In some cases, data scientists even need to leave an environment and rebuild something different to be able to put the model in production. No matter which approach is used, it takes time and introduces a risk of errors creeping into the productionizing process.

How it works in KNIME

KNIME’s Integrated Deployment is the first approach to address these challenges effectively. Using open-source KNIME Analytics Platform, a workflow is created to generate an optimal model. Integrated Deployment allows a data scientist to mark the portions of the workflow that would be necessary for running in a production environment, including data creation and preparation as well as the model itself, and save them automatically as workflows with all appropriate settings and transformations saved. There is no limitation in this identification process — it can be simple or as advanced (and complex) as required.

KNIME Integrated Deployment automatically creates production data science

With KNIME Server in production, these captured workflows are then referenced and reused. There is no need to rewrite or recode any of the process. Moving an optimized process from creation to production can be totally automated or done manually with a simple drag-and-drop from the KNIME Analytics Platform creation environment to the KNIME Server production environment. As all production workflows are also KNIME workflows, users gain all the advantages of documentation, version control, security and collaboration.

For organizations with many production models, this setup gives the additional benefits of being able to take the optimized creation workflows and use them in a scheduled or triggered environment. In doing so, when new models are required in production, the same KNIME Server setup can rerun the creation and optimization workflow automatically, delivering the newly updated and automated production workflows to the business.

To find out more about Integrated Deployment, please visit www.knime.com/integrated-deployment.

About KNIME

KNIME provides open-source software for fast and intuitive access to advanced data science. At the core is the open-source KNIME Analytics Platform, a visual workbench providing a wide range of state-of-the-art analytics tools and techniques to handle any use case — from basics to highly advanced. It is complemented by the commercial KNIME Server which makes data science productive in the enterprise, while staying in the same software environment for deployment, collaboration, management and optimization. Headquartered in Zurich, KNIME has offices in Austin TX, Konstanz and Berlin. Learn more at www.knime.com.

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KNIME, KNIME Analytics Platform, and KNIME Server are trademarks of KNIME. All other brand names and product names are trademarks or registered trademarks of their respective companies.

Tags: KNIME, data science, Integrated Deployment, data scientists, data analytics, machine learning, deep learning, artificial intelligence, AI, ML, open source, big data, KNIME Analytics Platform, KNIME Server

The KNIME Summits, in spring and fall, have been taking place since 2008 in Europe and the US. When the decision came to move as much of this year’s agenda online, we felt sad that we wouldn’t be able to see familiar and new faces in Berlin this year. At the same time we were inspired to do our best to make a success out of the online summit and start connecting as many home offices as possible - virtually everywhere.

March 30 to April 7 was the first week of the online summit program, with a mix of courses, the Virtual Summit session, and the“Talk to the KNIMErs” sessions. Resonance around the full-day online courses on machine learning, data wrangling, time series analysis, and KNIME Server was impressive with between 150-200 participants in each course.

Michael Berthold unveiled the new approach of Integrated Deployment. This approach removes the gap between creating data science and putting data science into production. You can see a video of his talk, The Future of Data Science: Integrated Deployment on the summit webpage.

Further talks from the KNIME Team at the Virtual Summit session, of which you can see recordings in the links, included:

• Create and Productionize Data Science Solutions
• Community, Partners, and Teaching: the KNIME Family
• What’s New and Cooking in KNIME Analytics Platform and KNIME Server

At the keynote session, Dean Abbott (Smarter HQ) looked at how to “Focus on the Task - Enabling your Models to Speak the Language of Business”. Based on several use cases, he examines how predictive models can be used to provide benefit to businesses and why a machine learning approach is so effective and accurate. Watch the video of his talk here.

In total over 2000 people dialed in to the courses and summit sessions. Thank you to everyone for bearing with us while we experimented with this new online format.

KNIME Spring Summit Extended Program - Continues Online

More talks by invited speakers and a wide range of workshops are being streamed live as free webinars in the coming weeks as part of our extended online summit program. To date, the presentations of KNIME in Action are brought to you by speakers from Discengine, Chiesi Farmaceutici, Redfield, and Queen’s University, Canada. The program of workshops being broadcast in the coming weeks covers the topics of time series analysis, guided labeling, machine learning, RDKit in KNIME, GDPR compliance & anonymization techniques, and sharing & deploying data science with KNIME Server. 

We have devised a series of compact online courses for April & May, ranging from basic data wrangling, text processing, machine learning algorithms, and big data to name just a few. There are also different schedule options here: e.g. four 90-minute sessions/day spread over a week, or one 90-minute sessions hour/week spread over 4 weeks. We hope to cater to your preferred schedule.

• Visit the complete extended summit program and register on our website. 

KNIMErs Connecting around the World

We hope that everyone joining us for the extended summit online program will benefit from the new format, despite not being able to meet in person. We are thrilled to see people signing up from all over the world. One attendee even wrote she would be setting her alarm for 2 AM to tune in from Australia. KNIMErs in Bad Oldesloe to Bilbao, Sugar Land to Bowling Green (yes they really do exist), Mexico City, Al Ain City, Dublin….we are all connecting.

This free online learnathon is a mix between a hackathon and a workshop. It's like a workshop because we'll learn more about the data science cycle: data access, data blending, data preparation, model training, optimization, testing, and deployment. It's like a hackathon because we'll work in groups to hack a workflow-based solution to guided exercises.

The tool of choice is the open-source, GUI-driven KNIME Analytics Platform. Because KNIME is open, it offers great integrations with an IDE environment for R, Python; SQL, and Spark.

Agenda & groups/breakout rooms

We'll start with an introduction to KNIME Analytics Platform, followed by a short presentation about the data science cycle.

After this presentation we split into three groups. Each group focuses on one of the three aspects of the data science cycle.

Three zoom breakout rooms will be activated for this purpose. You go into the room for the group you sign up for (below) to attend the specific tutorial and exercises.

There will be a KNIME data scientist in each breakout room to help you while you work on the exercises.

Choose which group (Group 1, 2, or 3) you want to join on the zoom registration page.

• Group 1 - Working on the raw data. Data access and data preparation.
• Group 2 - Machine Learning. Which model shall I use? Which parameters?
• Group 3 - I have a great model. Now what? The model deployment phase.

Resources:

Download the learnathon materials here.

Recap & Office Hours - Get your Workflow Questions Answered

If you’ve recently done a KNIME online course or have just started using KNIME Analytics Platform, chances are you’ve got questions about the workflows you’ve started building. At this webinar Satoru and Ana will do a recap of topics from our courses and answer your questions. In parallel to the Recap is the Office Hours session, where you can bring a specific topic to discuss with a KNIME data scientist.

Recap Session Agenda:

• Data access
• Data wrangling and machine learning
• Collaboration and knowledge sharing
• Deployment options

Make sure to bring any questions you might have so they can be answered, live! And tune in to hear the discussions on other people’s questions.

Office Hours Session

While the Recap and Q&A Session is taking place, the Office Hours Session will be going on in parallel. You can reserve a 15-minute meeting with a KNIME data scientist to discuss a specific issue face to face, also sharing your screen if necessary, in a dedicated breakout room. Note that the 15-minute meeting slots are limited. Specify on the zoom registration page if you would like to book one.

The release of KNIME Analytics Platform 4.2 and KNIME Server 4.11.0 brings new functionality for enterprises to solve major data science challenges, as well as features for the individual KNIME user to collaborate, access more data sources, and blend more tools.

New Features for the Enterprise

KNIME launches Integrated Deployment, Flexible Execution Deployments, Metadata Mapping, and Guided Analytics - solving four major enterprise challenges in a simple and unique way.

Integrated Deployment

KNIME Integrated Deployment moves not only the selected model, but the entire data model preparation process into production simply and automatically. This allows continuous optimization in production and, for the deployment process, saves a lot of time and eliminates risk of errors. The capture and write workflow nodes are now available in KNIME Analytics Platform and the production workflow can be deployed on KNIME Server. New blueprint workflows for ML and Continuous Deployment are available on the KNIME Hub.

Elastic and Hybrid Execution

Elastic and Hybrid Execution leverage the enterprise infrastructure choices while covering periods of high demand, dynamically. This reduces costs by starting up special-purpose, pay-as-you-go (PAYG) executors without needing to maintain specialized hardware year round. KNIME Executor Groups and Reservation are new features in KNIME Server. KNIME Server is now available on the AWS marketplace as bring-your-own-license (BYOL), while AWS Auto Scaling can also be used on a PAYG basis with KNIME.

Metadata Mapping

The new Metadata Mapping with workflow summary uses the KNIME’s self-documenting nature and enables complete metadata mapping of all aspects of the workflow. Blueprint workflows are available on the KNIME Hub for documenting the nodes, data sources, and libraries used, as well as runtime information. These workflows can be extended to cover other aspects of good governance and lineage tracking. For example, the collection of metadata information across workflows via KNIME Server.

Guided Analytics

KNIME’s Guided Analytics applications can be customized based on reusable components and are available on the KNIME Hub. The completely reworked KNIME WebPortal, which is part of the new version of KNIME Server, is the application for the collaboration with end users.

All four of these enterprise enablers are controlled and executed using one single platform, which KNIME is able to offer thanks to its open, self documenting, and transparent architecture.

New Features for KNIME Users

The release highlights for KNIME users are: multiple spaces for sharing and a new profile on KNIME Hub, some great connector nodes, the TensorFlow2 integration and several performance improvements.

• With multiple public and private spaces on KNIME Hub, you can now organize and share your work in a more flexible way. Spaces are now accessible in the new Hub profile, which enables interacting with other users by giving kudos and seeing their work and user statistics.
• The new connector nodes enable reading and writing files in Microsoft SharePoint, accessing and manipulating data and tables in Amazon DynamoDB, as well as reading data from Salesforce and SAP (the latter requires a commercial Theobald license).
• The new TensorFlow 2 Integration enables the use of the TensorFlow 2 Python package with a few dedicated nodes.
• The performance improvements in this release include the Simple File Reader, the Joiner node, and executing Python nodes.
• In this release, we also worked on improving the user experience for integration nodes with Tableau, PowerBI, and R, and more.

More details available

Read more on our what’s new page and the complete list of new nodes, enhancements, and bug fixes in our changelog. 

Together with our partner IQuartil and the KNIME Community fan Gabriel Cornejo (Senior Data Scientist at Instituto Profesional AIEP) we are hosting a virtual data science learnathon in Spanish.

This free online learnathon is a mix between a hackathon and a workshop. It's like a workshop because we'll learn more about the data science cycle: data access, data blending, data preparation, model training, optimization, testing, and deployment. It's like a hackathon because we'll work in groups to hack a workflow-based solution to guided exercises.

The tool of choice is the open-source, GUI-driven KNIME Analytics Platform. Because KNIME is open, it offers great integrations with an IDE environment for R, Python; SQL, and Spark.

Agenda & groups/breakout rooms

We'll start with an introduction to KNIME Analytics Platform, followed by a short presentation about the data science cycle.

After this presentation we split into three groups. Each group focuses on one of the three aspects of the data science cycle.

Three zoom breakout rooms will be activated for this purpose. You go into the room for the group you sign up for (below) to attend the specific tutorial and exercises.

There will be a KNIME data scientist in each breakout room to help you while you work on the exercises.

Choose which group (Group 1, 2, or 3) you want to join on the zoom registration page.

• Group 1 - Working on the raw data. Data access and data preparation.
• Group 2 - Machine Learning. Which model shall I use? Which parameters?
• Group 3 - I have a great model. Now what? The model deployment phase.

Resources:

Download the learnathon materials here.