Chief Technology Officer at Neosperience. Loves speaking about Serverless, ML, and Blockchain. ServerlessDays Milano co-organizer. Opinions are my own.

IMPROVE MACHINE LEARNING DEPLOYMENT WITH NEW RE:INVENT 2020 AWS LAMBDA FEATURE

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Photo by frank mckenna on Unsplash

AWS Lambda was released back in 2014, becoming a game-changing technology. By adopting Lambda, many developers have found a new way to build micro-services that could be easily achieved. It comes with many additional advantages such as event-based programming, cloud-native deployment, and the development of the now well-known infrastructure-as-code paradigm.


RE:INVENT 2020 SESSIONS PICKS FOR DEVELOPERS AND MACHINE LEARNING PRACTITIONERS

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Photo by Julian Paefgen on Unsplash

This year will be remembered for many reasons. It has been a year of big changes in our lives and habits and a time when we found new ways to do the things we love. 2020 will be remembered as the first year without Amazonians from everywhere gathering in Vegas for the traditional re Invent.


CAN YOU OBTAIN THE SAME ACCURACY OF A CUSTOM TRAINED ML MODEL WITH AMAZON REKOGNITION CUSTOM LABELS?

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Heating, ventilation, and air conditioning (HVAC) pipes. Image from Wikipedia by Aidan from Brisbane, Australia

ML classification: Amazon Rekognition or Amazon SageMaker?

Computer vision problems have been tackled using neural networks in recent years, obtaining unprecedented results and continuously raising the bar of accuracy to near-human performances.
In this article, the focus is set on image classification in an uncommon context related to a Neosperience customer that provided the opportunity to compare two different approaches: Amazon Rekognition Custom Labels and Amazon SageMaker custom model.
Both approaches have advantages and could find their spot in a given context, but Amazon Rekognition Custom Labels offer an interesting tradeoff between time-to-market and cost.

Alisea

Machine Learning applications are steadily shifting from research domains to industry, opening…


DEMOCRATIZING AI FOR EVERY DATA SCIENTIST, WITH MODERN TOOLS

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Image by Comfreak from Pixabay. The power of Lightning can fire a torch.

In real-world applications, managed AI services such as Amazon Rekognition and Amazon Comprehend offer a viable alternative to dedicated data science teams building models from scratch. Even when a use case requires model re-training with purpose-built datasets such as custom image labels or text entities, it can be easily achieved with Amazon Rekognition Custom Labels or Amazon Comprehend Custom Entities.


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Neosperience aims to become the foundation of partners' product strategy and the backbone of our services. Photo by Mirko Blicke from Unsplash

Some of the companies joining us in the last few months brought non only strong domain expertise and technical competencies in their field (i.e., MIkamai, LinkMe) but also widely adopted products, like Workup RubinRed Digital Commerce platform. Many to come in the next months as soon as we find top performers in their fields, willing to scale up and join our family. On a product strategy basis, it has not been easy to think which evolution model was the best to provide the best value for our customers. On one side, we could adopt a centralized model with firm product…


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Photo by Ian Schneider from Unsplash.

Since we started scaling up Neosperience from a single company to a group, while onboarding people with different stories and experiences, making everyone feel comfortable has been one of our top priorities. We know this posed several challenges we had to consider to reach the ambitious goal of having everyone “feel like being at home” within Neosperience. We know that the three keys driving people’s motivation are autonomy, mastery, and purpose. Such principles are good at keeping a single person engaged with a company or a team. Unfortunately, they do not provide sufficient enough guidelines about the strategies to help…


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Image analysis ML models can be run smoothly from a serverless function, democratizing access to AI. Photo by Alisa Anton on Unsplash

On June, 16th Amazon Web Services released the long-awaited feature of AWS Elastic File System (EFS) support for their AWS Lambda service. It is a huge leap forward in serverless computing, enabling a whole set of new use cases, and could have a massive impact on our AI infrastructure, thus reducing machine learning inference costs down to pennies for a wide number of applications.


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Majestic Reflections of Shaik Zayed Grand Mosque. One of the world's most astonishing example of Islamic architecture. Photo by Photo by Farhan Khan.

Il mondo IT non è esente dal fascino delle “mode del momento”: soluzioni che magari sono state adottate da aziende considerate punti di riferimento e che assumono nell’immaginario collettivo il ruolo di “no brainer” diventando il nuovo status quo, almeno per alcuni mesi, in attesa che la nuova “moda” prenda il posto della precedente. Il più delle volte si tratta di soluzioni che hanno più di un razionale forte nella loro adozione da parte delle aziende che le hanno proposte per prime. …


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Photo by Cristina Gottardi on Unsplash

Amazon Web Services ha rafforzato la sua presenza in Europa mediante l’introduzione di una region dedicata agli utilizzatori dei servizi AWS in Italia, prevedendo nei prossimi anni uno sviluppo significativo di questo mercato.


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Monolithics aren’t always bad, but this doesn‘t mean it is a viable solution for the majority of architectures: they instead apply to edge cases. Photo by Dawn Armfield on Unsplash

A couple of days ago, software company Istio confirmed they are moving back from a microservices architecture to something much close to a monolith to ease their product development and match some business requirements with less effort than before. The subtle irony is that Istio is a widely adopted product by DevOps teams to manage microservices architectures indeed. This fact is not ancillary to their choice, as we’ll discuss in the following.

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