Artificial intelligence (AI) is very quickly expanding in the healthcare industry. AI’s primary role has been to automate processes to make it easier for employees to work on more complex people-related concerns. Yet as the technology develops, Healthcare Central reports that AI is also being used as a diagnostic tool. With a faster, more accurate way of detecting illnesses, and administering treatments, the mortality rate could be significantly reduced. This includes the treatment of conditions like cancer.
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!
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.
Two of a kind: Digital Transformation and Data Strategy – Part 1
“A chain is only as strong as its weakest link” – English proverb
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.
Build your own Apache Ignite persistence with Scala
In the previous post about Apache Ignite, we learnt how to set up and create either a simple cache or a sql cache, and share the cached data between different nodes. In this post, we will dig a little deeper. We will see what to do if our app crashes because the cached data has disappeared. How could Ignite help us avoid this problem?
Stratio secures a €13m Series B round led by Adara Ventures bringing on-board new investor GP Bullhound
Madrid, 21 September 2018 – Today Stratio has announced the closing of a €13m Series B round led by Adara Ventures with the participation of new investor GP Bullhound. The funds raised will go toward the continued evolution of the Stratio Big Data product suite while accelerating the growth of their software subscription business internationally.
Stratio’s CEO, Oscar Mendez, said: “Stratio’s growth in recent years has been on an exciting trajectory. This investment will enable us to continue to evolve our product roadmap and international footprint on the journey to reinventing companies around their most valuable asset – their data. We are delighted to continue to work with Adara Ventures and have built a fruitful relationship with GP Bullhound for the past few years. We look forward to working together with them on our international expansion and growth strategy.”
Stratio is the largest Big Data and Artificial Intelligence software company in Europe. Since its launch in 2014, it has developed a datacentric super platform, which enables customers to place data in the centre of their operations, obtaining the maximum value of this new gold. The innovative aspects of its product have allowed Stratio to acquire flagship customers in the banking, insurance, retail, media, and utility industries such as BBVA, Banco Santander, Mutua Madrileña, Bupa Group, Carrefour, El Corte Ingles, Mediaset, or Red Electrica. In only four years, Stratio has been able to grow its revenues up to 20M€ this year and has commercial offices in Madrid, Silicon Valley, Sao Paolo, Bogota, and Vancouver.
Joakim Dal, Partner at GP Bullhound, commented: “Oscar, Ernesto, Román and Jose Manuel have built a platform that makes your company work like a digitally native entity. Stratio makes next generation technologies usable for IT heavy enterprises like banks or insurance companies with added security, governance, and support at a fraction of the cost of doing it themselves. We see significant opportunities for growth ahead for Stratio.”
“Stratio has matured into a key player in the Big Data Infrastructure landscape and we are delighted to back the team again in this next phase of their growth and evolution” added Alberto Gómez, Managing Partner at Adara Ventures.
About Stratio
Stratio is a leading Big Data and Artificial Intelligence software company, helping customers with their Digital Transformation by placing their data in the centre of their operations. From data intelligence to corporate culture, Stratio’s goal is to help the biggest sectors face the myriad of challenges and seize the opportunities that the digital revolution offers. The company has offices in Silicon Valley, Madrid, Sao Paolo, Bogota, and Vancouver. For more information please visit www.stratio.com
About GP Bullhound
GP Bullhound is a leading technology advisory and investment firm, providing transaction advice and capital to the best entrepreneurs and founders. Founded in 1999, the firm today has offices in London, San Francisco, Stockholm, Berlin, Manchester, Paris, Hong Kong, Madrid, and New York. For more information, please visit www.gpbullhound.com, or follow on Twitter @GPBullhound.
About GP Bullhound Asset Management
GP Bullhound Asset Management is the firm’s independent investment arm. It currently manages four funds, investing in high-potential and fast-growing technology businesses, from early-stage to pre-IPO.
About Adara Ventures
Adara Ventures is a venture capital firm managing over €100 million in the capital, investing in the European Atlantic rim (Spain, Portugal, UK, & Ireland principally) in early-stage, Deep-Tech companies, with a particular focus on Cybersecurity, Big Data, AI, and other Digital Enterprise areas.
For inquiries, please contact:
- Media Contact GP Bullhound
Chris Björk christopher.bjork@bjork-brown.
- Media Contact Stratio
Gonzalo Alamar – galamar@stratio.com
- Media Contact Adara Ventures
Viviana De Donato / José M. García Villardefrancos
Grupo Albión
+34 91 531 23 88
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!”
Wild Data Part 2: Transfer Style
This is the second post of our Wild Data series. In this post, we are going to expose how to transfer style from one image to another. Here, the most interesting point is to know that we won’t use a neural network to classify a set of classes as usual. I mean, we don’t need to train a network for a specific approach. Transfer style is based on pre-trained networks such as it could be a VGG19 trained with ImageNet (one million of images). Thus, a good understanding of transfer style will help you to better understand how convolutional neural networks works for vision. Let’s go on!
Wild Data Part 1: Augmentation
Let’s imagine that you want to buy a new car, and you fall in love with this new car’s brand. Because you really want that car, the car’s brand comes out everywhere in your daily life, even though the amount of these cars remain the same. Our brain is trained to focus on what it wants to see.
Swarm Intelligence Metaheuristics, part 1: Ant Colony Optimization
In a previous post, we reviewed the taxonomy of metaheuristic algorithms for optimization within the context of feature selection in machine learning problems. We explained how feature selection can be tackled as a combinatorial optimization problem in a huge search space, and how heuristic algorithms (or simply metaheuristics) are able to find good solutions -although not necessarily optimal- in a reasonable amount of time by exploring such space in an intelligent manner. Recall that metaheuristics are especially well fitted when the function being optimized is non-differentiable or does not have an analytical expression at all (for instance, the magnitude being optimized is the result of a randomized complex simulation under a parameter set that constitutes a candidate solution). Note that maths cannot help us in such cases and metaheuristics can be the only way to go.