Serverless means literally “without server” and it refers to a way to deploy and execute applications where the owner of the application doesn’t have to worry about the server and infrastructure used to serve it.
MLflow, the open-source platform released by Databricks in June 2018, has found a quick and broad acceptance in companies around the world.
These past months have been filled with uncertainty, we have been living a pandemic caused by Covid-19 that has changed our lives and our daily habits; working every day from home, juggling and balancing our work and personal life.
Talent is that word we say several times a day and what companies fight for, particularly those in industries where – unfortunately – it is scarce.
Over the last few years and even now, we usually hear that in order to build a microservices architecture or any distributed system, the services must be stateless. This way if we need, we can scale out adding more instances to face all the traffic that arrives. However, these systems also need to store the state in some way.
Welcome back to our series on Swarm Intelligence Metaheuristics for Optimization. On this post, we will focus on Particle Swarm Optimization. Recall we define Metaheuristics as a class of optimization algorithms which turn out to be very useful when the function being optimized is non-differentiable or does not have an analytical expression at all.
There is one striking element that does not seem to have been addressed as a common purpose between business teams and IT teams when confronting Innovation or Digital Transformation roadmaps… A Data Management Strategy.
Apache Ignite is a distributed in-memory cache, query and processing platform. Discover how to build your own Apache Ignite persistence with Scala.
Transfer learning consists in training a base network and reusing some or all of this knowledge in a related but different task.
Transfer Style allows to use the inner understanding of an already trained Convolutional Neural Network to transfer style from one picture to another.