Labs

SHARE TO:

Gradient Boosting to the Xtreme – Part I

A key element of Rubikloud’s philosophy around software is that machine learning should be embedded into business software, not necessarily to replace human intuition, but rather, to augment and enhance it. The reasons for why we believe that to be true, you can refer to Kerry’s posts here: HOW TO USE MACHINE LEARNING TO FURTHER RETAIL ANALYTIC CAPACITY  AN INSIDER'S VIEW OF...

Read More

Definition of Done

At Rubikloud, we focus heavily on shipping well-engineered products that are driven by data science and machine learning. That means we spend a lot of time prototyping and working through very large datasets and iterating over performance and feature considerations. This process requires many teams to work together to achieve a complete and shippable product. However, as any Product Manager knows,...

Read More

Announcement: Data Science Seminars

At Rubikloud we're big fans of learning (and not just the machine kind)! Since our company revolves around all things data, we've organized a seminar series about just that: data science. Its purpose is to encourage anyone and everyone to learn more about data and how to use it. These seminars are open to the whole company and cover topics...

Read More

Beyond Collaborative Filtering (Part 2)

Note: This is Part 2 of our series on recommendation systems and collaborative filtering. Please check out Part 1 of our series for the challenges of building a retail specific product recommendation system and an overview of collaborative filtering. Retail Product Recommendations Once a good collaborative filtering model has been built using matrix factorization, the individual dense latent customer and product vectors can...

Read More

Beyond Collaborative Filtering (Part 1)

Here at Rubikloud, a big focus of our data science team is empowering retailers in delivering personalized one-to-one communications with their customers. A big aspect of personalization is recommending products and services that are tailored to a customer's wants and needs. Naturally, recommendation systems are an active research area in machine learning with practical large scale deployments from companies such...

Read More

MongoDB vs Cassandra

On the rare occasion that I walk into the engineer pit I am usually hard-pressed to be acknowledged with more than a grunt, smile and a quick glance away from the computer screens. I get it though, "plugged-in" engineers are focused creatures with little to no tolerance for outside distraction. Recently however, I was surprised by the heated discussion I...

Read More

Data Science: The Endless Possibilities

With continuing advancements in computational power and the collection and processing of data, the world is seeing new and exciting uses for data analytics. From charitable ventures to "smart" homes, the FIFA World Cup and climate change, the examples are endless. Here are a few of my favorites: Help for distressed teens: Started by Nancy Lublin, the Crisis Text Line (CTL)...

Read More
SHARE TO: