You could pay a company good money for advice on what architectures you should choose to serve your machine learning models.
And the answer would be “It depends”!
Or you could read Roy’s article for free.
Just saying.
Xebia ♥️ #data, #machinelearning & #knowledgesharing
Distributed #machinelearning has mainly two flavors: model and data parallelism.
Each has its own approach and output.
My colleague Nitin Bhushan will tackle the first approach, model parallelism, leveraging Apache Spark, in his upcoming workshop during the Data Learning week.
The session is tuned for anyone starting out with distributed ML for the first time and for the folks looking to enhance their understanding of scaling learning models with Apache Spark.
Register 👇🏼 to be electrified by our electrical engineer in residence, Nitin!
https://xebia.ai/learning-week-nitin
Happy to announce Data Learning Week, our latest Data initiative at the Xebia Academy!
The week is packed with workshops where you can dive into our courses—for free!
What’s on the menu? It ranges from scalable and production machine learning to convincing your stakeholders through data to deep learning and AI demystification!
✨ April 8th: Scaling ML workflows with Apache Spark with Nitin Bhushan 🧠 April 9th: Demystifying AI with Enrico Erler 📈 April 10th: Enhancing Data Visualization Techniques with Lysanne van Beek 🖥️ April 11th: Developing Production-Ready ML Applications with David Coba
Secure your spot 👉🏼 https://xebia.ai/learning-week
#MachineLearning #AI #DataViz #xebiaacademy #deeplearning #ML
Xebia truly is a great place to work.
Besides supporting our team with time and resources so they could concentrate on writing a best seller, I also had the opportunity and great fun of editing a chapter.
If you are, aspire to be, or lead a team of analytics engineer(s), this is the book to get.
Among others, we cover #dbt (dbt Labs), Airbyte, #duckdb (MotherDuck), and Tableau.
Amazon link below 👇🏼
#data #analyticsengineering
Great article on the nuances of Gemini and why many have labeled it (pun intended) as woke
“Today, the bots are different: they appear to know right from wrong and truth from lies. Simplifying a bit, this is a product of a mechanism known as reinforcement learning from human feedback (RLHF). In RLHF, a largely-formed LLM is fine-tuned by presenting it with a variety of prompts, and then browbeating the model to give the answers that get the highest marks from human reviewers.”
https://lcamtuf.substack.com/p/gemini-how-did-we-end-up-here
Looking forward to participating in a panel to discuss how AI is shaping our society at the Tech Summit Europe.
It’s on the 18th of April in Amsterdam.
More info and tickets 👇🏼
https://xebia.ai/europe-tech-summit-gl
The biggest barriers to productivity (e.g., vague goals and processes) don’t magically disappear the moment an employee walks into an office."
One of the most important take-home messages from Atlassian’s lessons learned from 1,000 days of distributed work.
Do you recognize vague goals and processes as the biggest barrier to productivity?
https://www.atlassian.com/blog/distributed-work/distributed-work-report
What’s happening in China is really interesting. If we trust their official figures and estimates, the economy was driven by 3 things:
As 1. is crashing, consumers—who put most of their savings in, guess what, real estate—are more conscious about spending, hurting another good 30% of their economy.
So, 2/3 of their economy is hurting and will hurt more badly in 2024.
China’s and Hong Kong’s markets are also spooked, having shed USD 1.5 TRN in January alone.
It’s going to be an interesting 2024.
Analyzing data of protons traveling just 11 km/h less than the speed of light (299.781 km/s!), was Rogier van der Geer’s favorite occupation while working at the Large Hadron Collider at CERN.
He then switched to Xebia, starting to apply his travel domain knowledge to recommenders, forecasting, and fraud detection.
That’s why I’m excited that he’s participating in a panel, hosted by Mark Jacks, with Ronel Schoeman from TUI and Michael Schrage from the Massachusetts Institute of Technology to discuss the Future of Travel Recommendations.
If you want to know more about customers’ behavior, how recommendations alter it, the magic behind recommendation engines, and the ethical responsibilities associated in building them, hit the link below and reserve your spot!
https://xebia.ai/future-of-travel
Your company is probably not successful in getting value out of AI.
But don’t worry, only 15% of companies can pull it off.
Why? What are the bottlenecks, and how can you address them?
We have a white paper to guide you through and—spoiler alert—it’s not boring!
Get it here 👇🏼
