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Can low-code enable machine learning? Certainly. In fact, it’s an excellent method for simplifying and accelerating the use of machine learning–and enabling developers without data science backgrounds to build software powered by machine learning. In this blog, I make the case for using low-code to develop machine learning apps. By Forsyth Alexander.

This blog post then delves into:

• Understanding low-code and machine learning
• How low-code and machine learning can accelerate innovation
• Enabling machine learning in app development with outOutSystems

For most organizations, the challenges of application development are well-known. Building enterprise applications is hard, there are too few developers, and the business environment changes faster than the software and systems can handle. By enabling machine learning with low-code AI, your company can change the way it delivers enterprise applications.

Low-code is revolutionizing the way businesses innovate and use AI and ML capabilities. By combining the speed and simplicity of low-code development with the intelligence of machine learning, you can create cutting-edge applications that meet customer expectations and stay ahead of the competition. Good read!

In the realm of software development and IT operations, the role of a DevOps professional is paramount. They are the architects of efficient software delivery, combining development, operations, and quality assurance to accelerate a company’s technological prowess. By @4dayweek.io.

DevOps professionals shape the software delivery pipeline, making critical decisions that impact speed, efficiency, and quality. They are the heartbeat of a company’s software infrastructure, ensuring smooth deployment and minimal downtime.

Further in the blog post:

• Why choose a career as a DevOps professional?
• Is DevOps a good career path?
• Steps to become a DevOps professional
• Career progression for a DevOps professional
• Different DevOps career tracks
• Essential skills for a DevOps professional
• Educational requirements for a DevOps professional

The DevOps role is evolving with the integration of new technologies, methodologies, and tools. Tomorrow’s DevOps professionals will not just be tech experts but also strategic visionaries, efficient coordinators, and proactive problem solvers. For those with the ambition and the right skill set, the DevOps position promises a fulfilling and influential career. Good read!

If you are looking for Twilio alternatives to create better communications for your apps and websites, then here are top nine options to choose from. By Hardik Shah.

From video conferencing to voice calls in mobile applications and SMS facilities, businesses rely on communication API providers like Twilio. But, no brand is perfect, and Twilio has its problems. If you seek the best Twilio alternatives, then here are the top nine options for your projects.

The article then compares these alternatives, including when to choose them, pricing, similarities and differences:

• Vonage
• Plivo
• Amazon Connect and SNS
• Bandwidth
• Sinch
• Telnyx
• Restcomm
• InfoBip
• Agora.AI

Twilio is a market leader but lacks free inbound SMS and live customer support for basic plans, making it less appealing for small businesses on a budget. The Twilio alternatives mentioned above are comparatively affordable in terms of customer support and service. Some good competitors here!

In this article author would like to get into the gory details and see how we can create a CI/CD pipeline that deploys a Kubernetes application using the blue-green methodology. The gist of blue-green deployments is to have two identical environments, conventionally called blue and green, to do continuous, risk-free updates. This way, users access one while the other receives updates. By Tomas Fernandez.

Source: https://semaphoreci.com/blog/continuous-blue-green-deployments-with-kubernetes

The content is split into these sections:

• Why Kubernetes?
• Blue-green deployments with Kubernetes
• Getting ready
• Preparing the manifests
• Istio manifests
• Kubernetes manifests
• Setting up your continuous integration pipelines
• How to organize releases

… and much more. Upgrading is always a risky business. No matter how much testing we do, there’s still a chance for something to go wrong. But with a few carefully placed tests and a robust CI/CD workflow, we can avoid a lot of headaches. Good read!

In a rapidly evolving digital transformation landscape, the success of software-based businesses often hinges on their ability to deliver new features seamlessly and efficiently. Two key strategies that have gained significant traction in this context are canary releases and blue-green deployments. This article aims to thoroughly understand these essential software deployment techniques, highlighting their advantages, challenges, and best practices.. By [x]cube LABS.

Tutorial then pays attention to:

• Understanding the basics of software deployment strategies
• Canary release: An overview
• Blue-green deployment: An overview
• Canary release versus blue-green deployment: Key differences

Blue-green deployment ensures minimal user impact by thoroughly testing the new version before switching. However, in case of issues, the entire user base is exposed. Canary release exposes a small percentage of users initially, allowing early detection and resolution of problems before they affect the whole user base.

Selecting between canary release and blue-green deployment depends on your needs, resources, and risk tolerance. If you prioritize speed and ease of deployment and have sufficient resources to maintain two identical environments, blue-green deployment may be ideal. Conversely, if your resources are limited, or you prefer a more controlled and iterative approach to risk management, canary deployment might be a better fit. Nice one!

Today, we’re diving deep into serverless databases as one solution for aligning these goals. Specifically, we’ll check out what they are, how they work, and what benefits they offer. See, when we build any kind of solution, a big portion of the cost is the infrastructure we use to store and host relevant data. This also has an outsized impact on the quality and success of whatever we’re building. By Ronan McQuillan.

What’s strange is that serverless computing isn’t really serverless at all. At least, we can’t take from this description that there are no servers involved - because there are. The distinction is that the servers that host data aren’t really our problem as users or developers. A serverless database is a database that’s provided as a service by a third party - including management of the relevant infrastructure and hardware.

The article also reads about:

• Advantages of serverless databases
• Challenges
• Top 8 serverless databases for 2024
  • NeonDB
  • FaunaDB
  • CockroachDB
  • MongoDB Atlas
  • DynamoDB
  • Firestore
  • AWS Aurora
  • Azure SQL

Serverless databases also enable us to develop reliable solutions. Because of their inherent fault tolerance, serverless solutions offer very high levels of availability. They also introduce the possibility of geographic scale. In the most basic cases, this means that we can select which regions we want our data to be hosted from. Beyond this, there’s what’s known as geo-partitioning. Interesting read!

SQL and NoSQL are in demand, and learning these tech skills can help improve one’s career prospects. These skills are essential for developers, data scientists, business analysts, or anyone working in related fields with data. By Durga Prasad Acharya.

A NoSQL database is non-relational, with its data organized in a non-tabular structure. Mostly, the data here does not consist of a schema. Instead, it has a key-value and is document-based. Looking at the high demands, it makes sense to learn SQL and NoSQL for better career prospects. And to gain the skills, let’s look at some of the resources you can take up and get certified.

The article then describes and compares these resources:

• SQL Bootcamp
• Datacamp
• CodeAcademy
• SQLBolt
• Oracle Certification
• SQL Essential

… and more. SQL and NoSQL are essential skills for data scientists, analysts, software developers, etc. Thus, if you want to start your career in these fields, learning SQL and NoSQL would be helpful, both in terms of knowledge and paycheck. Good read!

Implementing an event-driven architecture (EDA) is a road riddled with challenges. Among them is choosing the right tooling for the job. Many event-driven tools seem quite similar, at least at first glance, and you’d expect they could be used equally well for the same purposes. But that’s often not the case and choosing the solution best suited to your needs can be tricky. By Tun Shwe.

Article focuses specifically on message queueing and event streaming, highlighting their differences, common denominators and suitability for various use cases:

• Understanding messages queues and event streams
• Message queueing vs. event streaming tech: Comparing capabilities
• Message queueing and event streaming use cases
• Message queueing is sometimes a stepping stone to event streaming

If you’re dealing with small and medium workloads, you’re looking to reliably and flexibly route messages between components and your system is primarily interested in the current state, a message queueing technology is an adequate choice.

On the other hand, if you want to handle high-volume, high-frequency event streams in a scalable and reliable way, you need to do complex processing on data as it arrives to gain actionable real-time insights and your system is concerned not only with the current state, but a historical record of state changes, then event streaming is the right way to go. Nice one!

Intel’s momentus “AI Everywhere” campaign to show it is a strong competitor in the AI chip arena moved into high gear Thursday. CEO Pat Gelsinger and top executives argued the company can help customers with AI training and inference jobs across huge data centers as well as aboard AI PCs and even on smaller mobile devices. By Matt Hamblen.

The document says that Intel is making new chips for data centers and mobile devices to handle the increasing demand for AI workloads. The company is launching Core Ultra and 5th Gen Xeon processors. Intel is also working on software tools to make it easier for developers to use AI. Some analysts believe that Intel’s AI Everywhere strategy could be successful, but it will take time for the market to mature.

While most of the application merits of AI PCs from various chipmakers are yet to be realized, Intel also briefed reporters on side-by-side performance attributes of its Fifth Generation Xeon Scalable server processors, code-named Emerald Rapids, and compared them head-to-head with what AMD offers, while also asserting the qualitative value of its CPUs next to wildly successful GPUs from Nvidia. Good read!

Transitioning to the cloud means more than just migrating your workloads or building innovative new apps. While it’s a great first step for your organization, it’s just the beginning. To truly maximize the value of your cloud investments requires a fundamental shift in mindset and culture to rethink how your organization operates when imbued with technology. By Eric Lam and Pathik Sharma.

There are five core building blocks of Cloud FinOps:

• Accountability and enablement: Sets up a cross-functional team, standards, and enablement for managing cloud spend at scale
• Measurement and realization: Promotes cost transparency, using KPIs and value metrics to drive success
• Cost optimization: Drives continuous cost optimization effort focusing on resources, pricing, and architecture on cloud
• Planning and forecasting: Modernizes budgeting, allocation, forecasting, and chargeback methods to allow for cost effective development practices
• Tools and accelerators: Enables data-driven business decisions with near real-time cost reporting, automation, and tool integration

Cloud FinOps is an operational framework and cultural shift that brings technology, finance, and business together to drive financial accountability and accelerate business value realization through cloud transformation. Said differently, it’s a culture shift that promotes cost consciousness and agility in the cloud. Contrary to popular belief, Cloud FinOps isn’t just a practice in saving money. Rather, it’s about getting the most value out of the cloud to drive efficient growth. referred to by the FinOps Foundation. Good read!