A couple of days ago, we hosted a Logistics Optimisation webinar. We covered some challenges of Logistics Chains and talked about how graphs, graph technology, and Hume can help you tackle them.
So far, the Graph-Powered Machine Learning book has introduced us to graphs and machine learning. The second part of the book talks about recommendations. Recommender systems (RS) gather information about users and items and provide item suggestions, bringing great value to online stores - clothing stores, bookstores, you name it. Companies like Netflix base their entire businesses on high performing recommender systems.
Everyone has a favourite grocery shop they usually go to, maybe the shop close to home, the one with the most competitive prices, the freshest fruit, or simply the best cake. Similarly, everyone may be inclined to buy from one particular e-commerce platform rather than another.
What have we learned from Graph-Powered Machine Learning so far?
Knowledge sharing has always been extremely important for Engineering at GraphAware.Whether it is techniques, tools or technology, lessons learned from our consulting engagements, or experience in general,sharing sparks conversation, creativity and discovery of different or better ways to do things.
Last week we at GraphAware hosted yet another webinar. This time we talked about Money Laundering and how Hume can help you detect money laundering activities. Don’t worry if you missed it; here is your summary of what we covered.
Welcome back to the Graph-Powered Machine Learning book club. As you know by now, Graph-Powered Machine Learning is a book written by our very own Dr. Alessandro Negro. The book is a must-read for all data scientists, but it’s also a great read for everyone interested in graphs. In this blog series, I summarise the key points of each chapter and provide some more explanations useful for us less technically savvy. We learned the basics about machine learning, graphs, and why the two are a great fit in the first chapter.
There are a multitude of databases to choose from when deciding how to store your data. They differ in complexity, scalability, data modelling possibilities they offer, and application. Let’s walk through some of the most popular databases and their differences.
Only a few things are more satisfying for a graph data scientist than playing with Neo4j Graph Data Science library algorithms, most probably running them in production and at scale. Possibly also using them to fight against scammers and fraudsters that every day threatens your business.
It is always a valuable opportunity to understand our product better and recognize user needs. At GraphAware, building Hume, a graph-powered insight engine, we are proud of making an impact on our customers’ success. However, we use Hume also to support our processes and help our own needs. In the case of the event that took place throughout December, we were also able to have great fun and integrate the team.