Modern cloud environments are ever-changing, and so is the nature of cloud computing. The growing cloud assets accompany the attack surface expansion problem for organizations, which unveils the need for visibility of cloud resources. AWS Config addresses that exact demand. It can be challenging to understand resources within your infrastructure like:

  • Seeing what resources you have
  • Understanding your current configurations
  • Knowledge of configuration changes and change histories
  • Assessing if your resources are compliant with specific governances controls 
  • Having accurate and up-to-date audit information

Depending on the size of your AWS resources or deployment, overcoming these challenges and obtaining this information can become time-consuming and budget-intensive unless you use resource visibility and auditing tool like AWS Config

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API management solutions, also known as API gateways, are a must in the day and age of APIs. However, once you’ve set up such a gateway, you can use it for different purposes unrelated to APIs. Today, I want to show you how to improve the security of web apps.

Prevent Sniffing

Browsers are fantastic pieces of technology that try to make the life of users as comfortable as possible. However, the balance between ease of use and security may sometimes tip on the former to the latter’s detriment. For example, if an HTTP response doesn’t set the content type, the browser may try to infer it:

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Rightsizing resource requests is an increasing challenge for teams using Kubernetes—and especially critical as they scale their environments. Overprovisioning CPU and memory lead to costly overspending, but underprovisioning risks CPU throttling and out-of-memory errors if requested resources aren’t sufficient. Dev and engineering teams that don’t thoroughly understand the live performance profile of their containers will usually play it safe and request vastly more CPU and memory resources than required, often with significant budget waste.

The open source Kubecost tool ( has had a Request Sizing dashboard to help Kubernetes users bring more cost efficiency to their resource requests. One of the tool’s most popular optimization features, the dashboard identifies over-requested resources, offers recommendations for appropriate per-container resource requests, and estimates the cost-savings impact of implementing those recommendations. The dashboard utilizes actual usage data from live containers to provide accurate recommendations. However, leveraging the dashboard has included some hurdles, requiring users to manually update YAML requests to align resource requests with Kubecost recommendations or introduce integrations using a CD tool. 

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Today no less than 60% of companies are either exploring the possibilities of adopting artificial intelligence or trying to realize its potential to transform the way they do business. The problem is that a significant portion of them (one-third) struggle to produce substantial change with AI.

The lifecycle of an AI solution usually consists of problem definition, data collection, model building, model fine-tuning, and applying the solution to solve a specific problem. Various experts build the solution to solve business problems. Still, a problem solved by a data scientist does not automatically translate into a constant stream of actual value for the business. Once deployed to production, the AI solution cannot be left as-is. Like any other system, it requires continuous maintenance. However, any AI solution’s maintenance differs significantly from the maintenance of other systems (e.g., microservice-based applications). The performance of any AI solution can be affected by many factors, and if the maintenance work is not done, the solution will cause problems instead of solving them.

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There are several software products on the market that are used for their varied applications. This software makes the different tasks easier and allows for increased efficiency and performance. Development of any software is a tedious and long process, and it undergoes a series of quality and performance tests before its release and use. With the development in technology, the software gets upgraded with the latest updates.

As the technological world evolves, so do user expectations for handling applications; it is essential to test the performance of the applications before deploying them on a large scale.

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Levallois-Perret, le 21 juillet 2022. SAP annonce la nomination d’Olivier Nollent au poste de Managing Director de SAP France. Olivier Nollent reporte à Rohit Nagarajan, Président EMEA North de SAP.

Olivier Nollent dirigera l’activité de l’une des plus importantes filiales commerciales de SAP, leader mondial des logiciels d’entreprise. Il aura pour mission :

  • de renforcer les synergies avec les clients, ainsi qu’avec l’écosystème de partenaires, dans leurs grands enjeux de transformation numérique (gestion de l’expérience, gestion intelligente des dépenses, réduction de l’empreinte sur l’environnement…), et d’innovation (intelligence artificielle, big data, blockchain…) grâce à ses technologies avancées et ses centres de R&D (SAP Labs) et avec son accélérateur de startups SAP.iO basé en France.
  • de poursuivre la croissance de SAP en France via le développement de ses activités Cloud auprès des grands comptes et des PME. Il s’appuiera à cet effet sur l’expertise du Groupe dans les domaines des logiciels ERP, Analytics, Supply Chain Management, RH et de gestion de l’expérience client, ainsi que sur le plus large portefeuille de solutions modulaires disponibles sur site, Cloud ou hybrides.

Olivier est diplômé de l’INSEEC et a débuté sa carrière chez HP, puis a passé 13 années chez Microsoft où il a occupé avec succès plusieurs rôles de direction commerciale, jouant un rôle clé dans la transition de l’entreprise vers le Cloud.

En avril dernier, après 5 années chez Salesforce, il rejoint SAP en tant que Senior Vice President Industries.

Son expérience des grands acteurs du secteur, sa solide expertise dans le développement des business de la Tech et son leadership éprouvé auprès de grandes organisations commerciales sont autant d’atouts pour accompagner la dynamique de croissance de SAP France.

Gérald Karsenti reste Président du Conseil d’Administration, assurant la direction générale de l’entreprise. Il accompagnera également Olivier Nollent dans sa transition vers son nouveau poste, en consolidant la notoriété de SAP sur le marché et en cultivant les relations avec ses clients et son écosystème.

The post Nomination d’Olivier Nollent au poste de Managing Director de SAP France appeared first on SAP France News.

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Undertaking a digital transformation journey is easier said than done. Efforts to transform an organization often face significant challenges, including integration of legacy platforms with modern technology stacks, the skill sets required for implementation, and an organizational structure that can communicate and embrace change. According to a 2020 study by the Boston Group, 72% of digital transformations fail, falling short of their objectives, and are often met with radical consequences after. 

With so much at stake when it comes to transforming an organization, Adecco Group’s Sabine Laute suggests that a dedicated transformation office could be a viable solution.

Source de l’article sur DZONE

What Is Text Classification?

Text Classification is the process of categorizing text into one or more different classes to organize, structure, and filter into any parameter. For example, text classification is used in legal documents, medical studies, and files, or as simple as product reviews. Data is more important than ever; companies are spending fortunes trying to extract as many insights as possible.

With text/document data being much more abundant than other data types, new methods of utilizing them are imperative. Since data is inherently unstructured and extremely plentiful, organizing data to understand it in digestible ways can drastically improve its value. Using Text Classification with Machine Learning can automatically structure relevant text in a faster and more cost-effective way.

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When I first heard the term DevOps, What I understood from my peers is that,

“The process of deploying the application in any environment (dev/QA/prod) is called DevOps. It’s just another synonym for operations.”

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IT modernization and innovative new technologies change the healthcare industry significantly. This blog series explores how data streaming with Apache Kafka enables real-time data processing and business process automation. Real-world examples show how traditional enterprises and startups increase efficiency, reduce cost, and improve the human experience across the healthcare value chain, including pharma, insurance, providers, retail, and manufacturing. This is part five: Machine Learning and Data Science. Examples include Recursion and Humana.

Blog Series – Kafka in Healthcare

Many healthcare companies leverage Kafka today. Use cases exist in every domain across the healthcare value chain. Most companies deploy data streaming in different business domains. Use cases often overlap. I tried to categorize a few real-world deployments into different technical scenarios and added a few real-world examples:

Source de l’article sur DZONE