Knative, serverless without vendor lock-in

 

How serverless works:

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. The only responsibility of developers is to write the code needed for the application to work properly and the cloud provider is the one who has to dynamically provision  the server, deploy the application in it, and make it work.

Because the code is totally independent of the platform and server on which it will be deployed, the developer doesn’t need to define endpoints or listeners to communicate with the outside world, instead of this, the cloud provider is the one responsible for providing the means to communicate with other services using events that trigger the execution of the application logic.

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Implementing the statsmodels flavor in MLflow

MLflow, the open-source platform released by Databricks in June 2018, has found a quick and broad acceptance in companies around the world. As stated by Databrick’s co-founder and CTO Matei Zaharia in their presentation post, there were a lot of different tools prior to this, created to track and/or take ML models into production, but they were either proprietary, language-dependent, or able to address only one aspect of the complete Machine Learning lifecycle. MLflow aims to be open in all senses – open source software but also language-agnostic (open interface).

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Stratio enters a new era

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. Difficult times often bring out the best in people, and this has been no different in Stratio, Stratians has been giving the best version of themselves.

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Smart Teams, a new way to build high-performance agile teams

Talent is that word we say several times a day and what companies fight for, particularly those in industries where – unfortunately – it is scarce.

Talent is something one is born with – but also something that one nurtures and grows, especially in the particular case of work environments. Individual talent in a collaborative culture – something which should be the norm in the technology industry – is however in short supply; in fact, it can even be counterproductive.

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Stateful Serverless Services, the new way to do microservices

Limitations of stateless services

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. And how do they do it? Well, because they can not store it in the service itself, it has to send it far away from the logic that must have to process it. Normally this datastore can be either a relational or document database.

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Discover the 3 most-read posts on our blog

Since Stratio’s creation in 2014, we have posted a total of 86 posts on our blog. We would like to congratulate and thank all those Stratians who have written their posts and taught us about their specialities and discoveries in relation to Spark, Machine Learning, Deep Learning, Scala, business, Kafka… We know that is hard to find time to read all of the blog posts, so here you have a recap of the 3 most-read posts published on our blog!

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Swarm Intelligence Metaheuristics, part 2: Particle Swarm Optimization

Welcome back to our series on Swarm Intelligence Metaheuristics for Optimization! In part 1, we talked about a family of metaheuristic algorithms known generically as Ant Colony Optimization (ACO), which were specially well-suited for combinatorial optimization problems, i.e. finding the best combination of values of many categorical variables. 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.

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Wild Data Part 3: Transfer Learning

Did you know that the word “hippopotamus” is a word of Greek origin? Hippos- comes from “horse” and -potamos means “river”. The funny thing here would be to imagine when Greeks run into this animal for the very first time. There was not a word for every single animal around the world, so they probably thought something like “what a strange horse…!!! Maybe the river has something to do with it. Got it! It will be a hippo-potamus!”

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