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Fargate vs Lambda : Qui sera le vainqueur ?

Actualités, Méthodes et organisation des process IT

Fargate et Lambda sont deux technologies très populaires parmi les développeurs cloud. Quel est le meilleur pour votre projet ? Découvrons qui sera le vainqueur !

## Comparaison Fargate vs Lambda dans l’espace sans serveur

Quelles sont les différences entre Fargate et Lambda ?

Fargate et Lambda sont deux options de calcul sans serveur populaires disponibles dans l’écosystème AWS. Bien que les deux outils offrent un calcul sans serveur, ils diffèrent en ce qui concerne les cas d’utilisation, les limites opérationnelles, les allocations de ressources d’exécution, le prix et les performances. Fargate est une moteur de calcul sans serveur proposé par Amazon qui vous permet de gérer efficacement les conteneurs sans les tracas de la mise en provision des serveurs et de l’infrastructure sous-jacente. Lambda, quant à lui, est une plateforme de calcul sans serveur qui vous permet d’exécuter du code sans avoir à gérer des serveurs. Lambda est conçu pour prendre en charge des charges de travail à courtes durées et à faible consommation de ressources.

Quelle est la meilleure option pour l’architecture ?

Lorsqu’il s’agit de choisir entre Fargate et Lambda, il est important de comprendre leurs différences et leurs avantages. Pour les applications à longue durée et à haute consommation de ressources, Fargate est la meilleure option car il offre une gestion des conteneurs plus efficace et une meilleure performance. Cependant, pour les applications à courtes durées et à faible consommation de ressources, Lambda est la meilleure option car il offre une exécution plus rapide et une meilleure utilisation des ressources. En fin de compte, le choix entre Fargate et Lambda dépend des exigences spécifiques de votre application et de votre architecture. Il est important de prendre en compte le coût, la performance et les fonctionnalités avant de prendre une décision finale.

Quelle que soit l’application ou l’architecture que vous souhaitez mettre en place, Fargate et Lambda sont tous deux des outils puissants qui peuvent vous aider à atteindre vos objectifs. En tant qu’informaticien enthousiaste, je trouve que ces outils sont très utiles pour créer des applications modernes et évolutives. Fargate et Lambda offrent tous les deux des fonctionnalités avancées qui peuvent être utilisées pour créer des architectures robustes et flexibles. Les deux outils sont faciles à utiliser et peuvent être intégrés à d’autres services AWS pour offrir une expérience utilisateur optimale. En fin de compte, le choix entre Fargate et Lambda dépendra des exigences spécifiques de votre application et de votre architecture.

Source de l’article sur DZONE

17 mars 2023/par Service comm.

Editors’ Picks: Summer 2020

Actualités, Méthodes et organisation des process IT

Agile

The Boy Scout Software Development Principle by Thomas Hansen — Short, simple, to the point, and a great mentality to have when building software.
The 20%: A Collection of Interviews by Lauren Hempen— Interviews with four women where they discuss their careers, challenges, successes, and advice related to being a woman in the software development industry.
Hanlon’s Razor: How to Be Less Judgemental and Build Better Relationships by Vinita Bansal — Interesting explanation of a shortcoming we are all subject to—preemptively judging others. This article serves as a reminder to judge less and listen more.
My Journey From Israeli Military Intelligence to VP of R&D: 7 Secrets Learned by Dan Lines— Dan Lines’ articles are always engaging and personal—this one, especially. This interview with Nadav Lev reveals all the secrets to getting promoted and building "elite dev teams."

AI

OpenAI GPT-3: How It Works and Why It Matters by Dana Kozubska — GPT-3 has many strengths, but it also has some weaknesses. Explore why it matters and how to use it to write code, design an app, and compose music.
How to Build Your Own Chatbot With Dialogflow by Jay Hogan — Stop thinking about that chatbot and actually build it. Jay Hogan created a step-by-step instruction on how to build a chatbot using Dialogflow.
Top 5 Open-Source and Commercial RPA Tools to Use in 2020 by Sindhu Priya D —Find out the top 5 RPA tools and learn more about what RPA is.
Running Machine Learning Applications on Docker by Emrah Mete —One Core member’s example of how to transfer a machine learning model to the living environment in the fastest and most effective way of using container infrastructures.

Big Data

Apache Kafka in the Gaming Industry — Use Cases + Architectures by Kai Wähner — This blog post explores how event streaming with Apache Kafka provides a scalable, reliable, and efficient infrastructure to make gamers happy and Gaming companies successful.
Design a Real-Time ETA Prediction System Using Kafka, DynamoDB, and Rockset by Kartik Khare — See how to design a real-time ETA prediction system using Kafka, DynamoDB, and Rockset.
Processing Large Messages With Apache Kafka by Kai Wähner — Kafka wasn’t built for large messages, but files and payloads keep getting bigger. This article covers use cases, architectures, and trade-offs with Kafka.
Looking Under the Hood of Apache Pulsar: How Good Is the Code? by Akansha Yadav — Ever wondered what’s under the hood of some well-known open-source software?

Cloud

Event-Driven Architectures by Ravi Purohit: A brief yet meaningful explanation of event-driven architecture and what makes it so valuable for cloud and microservices applications.
AWS vs Cloud vs Azure: Which Does Fit In? by Ivan Shulak: The battle of the cloud vendor heavyweights never seems to stop. Here’s another take on what benefits you should look for in each.

Database

Moving Toward Smarter Data: Graph Databases and Machine Learning by Frank Eaves — A brief overview of database solutions, an introduction to using machine learning and graph databases, and real-world use cases for putting context back into your data.
Database Architectures and Use Cases – Explained by Margo McCabe — This article provides an overview on database architectures, including use cases and pros and cons for each of them.
MySQL Workbench vs DVConvert — Migrate From SQL Server to MySQL by Dmitry Narizhnykh — Migration databases is not easy. In this article, one of our Core members compares two various tools for migrating from SQL Server to MySQL.
Apache Kafka Is Not For Event Sourcing by Jesper Hammarbäck — Apache Kafka is great for delivering messages, but if you’re using it for event sourcing, think again. Learn about why Kafka isn’t suitable for event sourcing.

DevOps

Top 20 Git Commands With Examples by Sahiti Kappagantula — This is one of our top articles of ALL TIME! It is a simple description of the Git commands you’ll need when working with Git, what they do, and how to format them.
To Automate, or Not to Automate? That is the Question by Simon Knight — This article contains some guidelines to help decide which and whether or not to automate tests in pursuit of a continuous testing outcome. It is thorough and well-written. A great example of a good DZone article.
The Key to Continuous Testing in DevOps by Maria Homann — In this article we take a look at continuous testing to understand why it plays a key role in DevOps, and why test automation is essential in enabling continuity.

Integration

Mulesoft 4: Continuous Delivery/Deployment With Maven by Ashok S — This article is a great example of what we want every tutorial to look like on DZone. The main aim of this article is to provide a standard mechanism to release project artifacts and deploy to Anypoint Platform, from the local machine or configure in continuous delivery pipelines.
Integration With Social Media Platforms Series (Part 1) by Sravan Lingam — This article helps you to build a RESTful API through MuleSoft that integrates with LinkedIn and shares a post on behalf of one’s personal account. I like this article because, in the age of social media, it’s so important for businesses to be connected and integrated!

IoT

Shaking Down the Raspberry Pi High-Quality Camera by Jeremy Morgan — This Core member tries out the new Raspberry Pi HQ Camera with full instructions so you can replicate his results. Here’s how you can put the new Raspberry Pi HQ camera to work right away!
Best Raspberry Pi IDEs by Moe Long — From Geanny and Greenfoot to Thonny and Adafruit WebIDE, these are the best Raspberry Pi IDEs for all languages!
IoT Security: Issues and Ways to Overcome It by Eugenia Kuzmenko — Learn how to make your IoT device and cloud infrastructure secure and see why is it important.
Raspberry Pi Emulation With Docker Compose by Sudip Sengupta — This guide, by one of our Core members, discusses everything needed to build a simple, scalable, and fully binary compatible Raspberry Pi cluster using QEMU, Docker, Docker Compose, and Ansible.

Java

42: The Answer To Life, The Universe and Everything by Karl Heinz Marbaise: A cool and engaging article that demonstrates the learning opportunities that diving into a niche topic can have. (Plus the title’s pretty hard to beat.)
The Silent Villains of the Coding Universe: A Review of Anti-Patterns by Akansha Yadav: There are so many articles online that discuss anti-patterns but few that really do it well. Akansha’s is one of them. Whether you’re a beginner or a seasoned developer, there’s content here that you can come back to again and again.

Microservices

Distributed Saga and Resiliency of Microservices by Binildas Christudas: Ah, the Saga pattern. Take a look at how this distributed pattern strengthens microservices with a travel-based sample application.

Open Source

Virtual Conference [Comic] – Daniel Stori — As more and more virtual conferences are coming up, Daniel Stori’s comic perfectly captures 2020.
Top 5 Free and Open Source Game Engines You Should Try — A unique and fun addition to the Open Source zone, highlighting the advantages of open-source game engines vs. proprietary gaming software.
Open Source Tool Options in Web Application Development — Helpful resources to which open-source tools might help you on your web application projects.

Performance

What Is Big O Notation? by Huyen Pham — Aside from a silly name, this article is an example of an in-depth analysis on a little-spoken-about concept. In this article, take a look at a short guide to get to know Big O Notation and its usages.
Is Python the Future of Programming? by Shormisthsa Chatterjee — Where is programming going? This article attempts to answer this question in a well-rounded way. The author writes, "Python will be the language of the future. Testers will have to upgrade their skills and learn these languages to tame the AI and ML tools".

Security

JSON Web Token: How to Secure a Spring Boot REST API by Yogesh Mali: Security, Java, and integration intersect in this article abou

Web Dev

A Better Way to Learn Python by Manas Dash: There’s so many resources available for learning Python — so many that it’s difficult to find a good and flexible place to start. Check out Manas’ curated list of courses, articles, projects, etc. to get your Python journey started today.
Discovering Rust by Joaquin Caro: I’m a sucker for good Rust content, as there’s still so many gaps in what’s available. Joaquin does a great job of giving readers his perspective of the language’s features in a way that traditional docs just

Source de l’article sur DZONE

18 septembre 2020/par Service comm.

Making Machine Learning Accessible for Enterprises: Part 2

Actualités, Développement IoT, Innovation

In Part 1 of this series, we discussed the need for automation of data science and the need for speed and scale in data transformation and building models. In this part, we will discuss other critical areas of ML-based solutions like:

Model Explainability
Model Governance (Traceability, Deployment, and Monitoring)

Model Explainability

Simpler Machine Learning models like linear and logistic regression have high interpretability, but may have limited accuracy. On the other hand, Deep Learning models have time and again produced high accuracy results, but are considered black boxes because of the machine’s inability to explain their decisions and actions to human users. With regulations like GDPR, model explainability is quickly becoming one of the biggest challenges for data scientists, legal teams, and enterprises. Explainable AI, commonly referred to as XAI, is becoming one of the most sought-after research areas in Machine Learning. Predictive accuracy and explainability are frequently subject to a trade-off; higher levels of accuracy may be achieved but at the cost of decreased levels of explainability. Unlike Kaggle, competitions where complex ensemble models are created to win competitions, for enterprises, model interpretability is very important. Loan Default Prediction model cannot be used to reject loan to a customer until the model is able to explain why a loan is being rejected. Also, it is often required at the model level as well as individual test instance level. At Model level, there is need to explain key features which are important and how variation in these features affect the model decision. Variable Importance and Partial Dependence plots are popularly used for this. For an individual test instance level, there are packages like “lime,” which help in explaining how black box models make a decision.

Source de l’article sur DZONE (AI)

8 août 2018/0 Commentaires/par Service comm.