Webinar: Mastering Flow Variables in KNIME Learn how flow variables enable dynamic, parametric workflows beyond simple automation. #KNIME #DataScience #Webinar #Automation #Analytics #NoCode

RE: https://bsky.app/profile/did:plc:tj7ezlihla4k6jbegjthsssi/post/3lom2xzgozk2y

Hoje em dia talvez seja papo meio fora de moda, mas quando eu discutia muito sobre migrações do #Windows para #Linux, o fantasma do #Excel com macros nojentas em VBA sempre espreitava. Depois de alguns anos trabalhando com #KNIME, eu sugiro romper com o Excel de uma vez. Se não for possível abandonar certos fluxos legados, nem vale a pena migrar. Migração para Linux não pode ser encarada como "Windows de graça" ou "Windows sem os problemas do Windows". Isso é auto-enganação e dá prejuízo depois.

Estou processando alguns dados extraídos do #Notion via #KNIME (usando snippet de código #Python que consulta "bancos de dados do Notion" via API) e não consigo ignorar o consumo de recursos. O processador Xeon de >10 anos frita para processar cerca de mil linhas. São vários minutos até conseguir chegar numa tabela utilizável. E a coisa só não fica pior porque eu rodo um ambiente leve (baseado no #FVWM) e posso dedicar 8 GB de RAM apenas para o KNIME, já que o computador tem 40 GB.

Hey @KNIME, is the #knime server open source licenced as well?

https://docs.knime.com/latest/server_installation_guide/index.html#_introduction

Read this blog to learn about #financialanalytics, what a financial analyst does, how to become one, and why financial analytics matters. Discover use cases, challenges, and how #lowcode #KNIME helps automate workflows in FP&A, audit, tax, and more. https://brnw.ch/21wN0Ae

More exciting #OpenMS news:
We are proud to announce the release of OpenMS 3.2, now with support for #KNIME 5.3 better export integration with #SIRIUS and improvements to our spectra viewer TOPPView. Read about all the improvements and find a link to download the installers at https://openms.de/news/release3.2/
#OpenScience #TeamMassSpec #opensource

What if we told you that in roughly 10 mins from now, you could have a completely free #LLM running locally on your computer — with zero lines of code? Read how in this article by KNIME CloudOps Engineer SJ Porter, using #GPT4All and #KNIME Analytics Platform: https://brnw.ch/21wMPYv

In our latest blog, we're happy to share a comprehensive overview of all free KNIME cheat sheets! Whether you're just starting your data science journey or a seasoned pro, we've got something for everyone, organized by learning level, data profession, and area of expertise. Check it out and boost your skills with us! https://bit.ly/4e3TiTd

Ready to beef up your data science knowledge? Dive into this complete glossary to learn all the most common data science terminology from the experts at KNIME. Don't forget to bookmark it! https://brnw.ch/21wLNS6

Take a peek at our most recent blog for a #lowcode way of connecting with #opensource #LLMs locally via #Ollama using #KNIME's AI extension! We've got a tutorial to guide you in connecting to 80+ LLMs using KNIME: https://brnw.ch/21wLvt2

#DBSCAN is effective for detecting fraudulent transactions, because it can automatically detect clusters of normal behavior and highlight anomalies without needing detailed prior knowledge about the fraud patterns. Read this #KNIMEforFinance article: https://brnw.ch/21wLmmC

With advanced tech, soccer clubs can track passes, angles, and ball speed, during matches. ️ Can #ml algorithms distinguish a men's game from a women's game, and, if yes, what are the differences? Read on to find out more. https://brnw.ch/21wL0Qz

#KNIME helped #Audi automate one of its most complex and important processes – supply chain management. Automating this single supply chain management workflow has resulted in a yearly saving of €30,000, eliminating manual work in Excel and Python, while also enabling immediate access to forecasts. Want to know more? Read this success story: https://brnw.ch/21wKY7t

Navigating the complexities of evolving fraud patterns can be daunting, given the enormous volume of transactions. Read our blog post that examines #quantiles as a simple and effective method for identifying potential fraudulent activities. https://brnw.ch/21wKY38

Overfitting, data leakage, and incorrect use of normalization are the 3 common #ml pitfalls when splitting a dataset. Read this blog by our guest writer, Dr. Carsten Lange, a professor of Economics at Cal Poly Pomona, to learn how to avoid these and more. https://brnw.ch/21wKVfP

Did you know that you can build GenAI functionalities into your KNIME workflows to speed up basic tasks, build basic workflows, and facilitate quicker insights? Let's look at 7 common GenAI-related use cases using KNIME to enhance efficiency and cost-effectiveness. https://brnw.ch/21wKPEt

Calling all high school students! Don't miss out on a chance to upskill in the world of data with KNIME's free virtual summer school this July. Join us from your own timezone in Europe or the US. Learn more: https://brnw.ch/21wKHS2

88% of organizations agree that open source is critical to innovation in data science and machine learning. Data science tools, like KNIME, that embrace openness as their core principle will always be among the first to integrate any innovation. From big data to autoML and now GenAI! Read this blog post for a dive deep into how open-source tools can sharpen your competitive edge. https://brnw.ch/21wKGbo

In this #KNIMEforFinance credit card #frauddetection series, learn how to implement a fraud detection algorithm based on outlier detection techniques. Start with this Random Forest supervised learning algorithm to help identify fraudulent transactions: https://brnw.ch/21wKBH1

Na linha do que comentei no meu último toot, sempre fico pensando se deveria explorar produzir conteúdo sobre as ferramentas que uso no dia a dia. Eu sinceramente acho que a combinação entre #KNIME e Google #Sheets é fantástica, além disso, a possibilidade de jogar fragmentos de código em #R ou #Python (com ambientes devidamente configurados com o #Anaconda) facilita demais extração de dados.

Por exemplo, profissionalmente, eu mantenho múltiplos bancos de dados no #Notion. Consigo extrair informações deles usando código em Python no KNIME, fazer uma série de tratamentos e concluir carregando tudo no Google Sheets. Dali, o consumo é feito em planilhas com alguma dose de abstração (usando fórmulas) e, até mesmo, em um dashboard no Looker Studio.

Adicionalmente, o paradigma visual do KNIME ainda facilita documentar os processos, facilita a utilização por não-programadores e permite introduzir abstrações nas caixas de diálogo dos componentes, eliminando a necessidade de modificação dos fluxos de trabalho construídos.

Enfim, tudo muito sofisticado e a custo baixo (a maioria dos mortais pagaria apenas pelo Notion, o que significaria desembolsar cerca de 50 reais por mês).