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הצטרפו אלינו לחצי יום שמוקדש כולו לדאטה אינג'נירס - בואו ללמוד כיצד למנף כלי בינה מלאכותית לטובת הגברת הפרודוקטיביות ושיפור התהליכים היום יומיים.

Agenda:

9:00-9:30 - Gathering and refreshments

9:30-9:45 - Opening notes

9:45-10:30 - Utilizing GenAI with Databricks -Michael Krasnopolsky (Databricks)

10:30:11:15 - Boost productivity with Microsoft Fabric, Copilot, and AI - Michael Greenshtein (Microsoft)

11:15-11:25 - Recharge break

11:25-11:55 - Deploying LLM Projects from POC to Production - Amir Belgi (Varonis)

11:55-12:30 - Networking

12:30-13:30 - From Perceptron to GPT: Understanding the Evolution and Mechanisms of AI Models - Noam Brezis (Pecan)

Julia Copenhagen Meetup, Winter edition!

Hi everyone and welcome into 2024!

In the Julia Copenhagen Meetup admin group, we hope that everyone has enjoyed their Christmas and New Year holidays 🎄🎆 No matter what, we are ready for a new round of meetups!

The next meetup will be Monday the 5th of February at 18.00 at Desupervised, Strandboulevarden 122 3rd floor, 2100 København.

This time we will hear from Michael Green, CEO / CTO at Desupervised. Michael will give a talk with the title "Modern Molecular Property Prediction using Julia: Our Journey so Far".

In addition Claudia-Roxana Budimir from RD8 will give a talk with the title "Breaking Ground: Prototyping Adventures in Julia".

After the talks, we will find somewhere close by where we can hang out and get something to drink.

The program: 18.00 - 18.30 Doors open 18.30 - 18.35 Welcome 18.35 - 19.00 Michael Green (Desupervised) 19.00 - 19.05 Break 19.05 - 19:30 Claudia-Roxana Budimir (RD8)

In case anyone else also has something Julia-related, please feel free to get in touch with either Mikkel Paltorp or Christian Michelsen from the Julia Copenhagen admins!

Snowflake and Databricks both aim to provide data science toolkits for machine learning workflows, albeit with different approaches and resources. While developing ML models is technically possible using either platform, the Hitachi Solutions Empower team tested which solution will be easier, faster, and cheaper to work with in terms of both user experience and business outcomes for our customers. To do this, we designed and conducted a series of experiments with use cases from the TPCx-AI benchmark standard. We developed both single-node and multi-node versions of these experiments, which sometimes required us to set up separate compute infrastructure outside of the platform, in the case of Snowflake. We also built datasets of various sizes (1GB, 10GB, and 100GB), to assess how each platform/node setup handles scale.

Based on our findings, on the average, Databricks is faster, cheaper, and easier to use for developing machine learning models, and we use it exclusively for data science on the Empower platform. Snowflake’s reliance on third party resources for distributed training is a major drawback, and the need to use multiple compute environments to scale up training is complex and, in our view, an unnecessary complication to achieve best results.

Talk by: Michael Green and Don Scott

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc