Search for a command to run...
Outdoorsman | Networking | SRE | Chaos & Platform Engineering | DevOps @toyotaracing | former @aws @splunk @verizon @gm | Thoughts are my own
Personal: Husband | GirldadX3 | BBQ | Outdoors | Nascar | Baseball | Triathlons
No comments yet. Be the first to comment.
How to scale, modernize, and mature your observability practice
Part 1 of a 2 part series
A Guide for Software Architects
Introduction In the rapidly evolving world of software development, Continuous Integration (CI) and Continuous Deployment (CD) have become cornerstone practices that ensure software quality and agility. CI/CD pipelines serve as the backbone of modern...
Introduction In software development, documentation is the spinach of coding—vital yet often sidelined. It's the unsung hero, the nutrient-rich foundation that sustains projects and teams. Imagine each software project accompanied by a precise recipe...
Digital transformation isn’t just a trendy term you hear on the news and read in magazine articles. It’s real. Some companies have built their strategies around it. Others are still trying to figure out what it means. The bottom line is, every organization needs to become a digital-ready enterprise capable of enabling itself with the latest tech-based tools, processes and business models. Here’s why digital transformation matters and how cloud native companies are differentiating themselves in this market.
Cloud Native businesses are easier to manage, more adaptable and more profitable. Taken together, these changes bring enormous opportunities for growth, new markets and disruptive innovations. The key is embracing digital transformation to help you move beyond your competition.
Cloud Native refers to businesses that leverage cloud computing services and are able to leverage technologies such as containers, serverless architectures, microservices, DevSecOps, and AI/ML. The Cloud Native business model is one that is more agile, scalable and cost effective.
By focusing on creating scalable solutions, businesses get a high pace of delivery and lower costs. Scalable: Something that can grow or expand as needed to handle a growing business. For example, if you have an online store for selling clothing, your back-end systems need to be able to handle millions of new orders in hours or days, not months. If you’re constructing an application to run on smartphones, you need a system that can run on multiple devices and platforms. In both cases, a scalable approach minimizes costs because you aren’t trying to build a single application that needs to work for everyone. Instead, you’re creating technology that can be tailored for each unique scenario.
The term is used so liberally these days that it seems to have lost its meaning, but the core idea is simple: using technology to transform customers’ experiences. It’s not just about introducing new technologies; instead, it’s about using existing technologies together in a way that brings value to customers. And this shift isn’t just limited to companies — it’s affecting entire industries. The healthcare industry, for example, has been hit particularly hard by the rise of digital transformation.
Treating patients online isn’t a new idea. In fact, online doctor visits have been around for quite some time. What has changed recently is the technology available and the way doctors are embracing it. Instead of treating patients on their own websites or via email, doctors are using specialized software and programs funded by big-name FANNG brands. These tools allow doctors to administer virtual check ups without ever leaving their offices, saving them time and money. A growing number of these programs are also accessible via mobile devices and tablets, allowing those with chronic illnesses to access their medical records at any time. Not only? does this make it easier for patients to get the care they need; it also saves hospitals significant amounts of money as well.
Photo by Alex Knight on Unsplash
AI/ML (Artificial Intelligence/Machine Learning) is a combination of technologies that allows computers to mimic cognitive functions of humans. These cognitive functions include learning from past experiences, recognizing patterns, identifying objects, performing tasks and making decisions.
However, recent developments in the field have led to significant advancements in software development methods such as deep learning, which has made it an attractive way to solve problems in a wide range of industries.
In the past decade, digitization has been the buzzword that has defined all technological advancements. The world is rapidly moving towards digitalization and artificial intelligence (AI) is a major contributor in this trend. With AI, you can build chatbots, translate languages, and make self-driving cars. These are just a few examples of what machine learning can do for you.How does it work? Such machines have the ability to learn without being programmed. This means that machines learn from data, rather than from rules written by humans. Machine learners are able to handle tasks on their own after being presented with data sets. The most common applications of AI/ML are in the business field and have the potential to change how organizations work and how they interact with customers.
Through AI and machine learning, organizations can gain insights from their existing data sets which can help them make informed decisions about their business growth. It can also be used in customer service by assisting customer care executives on collecting information about customers’ feedback and then dealing with them accordingly so as to provide a personalized experience to each customer.
Artificial intelligence (AI) and machine learning (ML) are two terms that are often used interchangeably, but there is a distinct difference between the two. ML is a subset of AI; it is all about the ability of computers to learn and improve with experience. AI focuses on the intelligence of machines and the ability for them to carry out tasks independently. Deep learning (also known as deep structured learning or hierarchical learning) Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing and audio recognition. These systems are capable of performing competitively with handcrafted knowledge in many domains such as computer vision, speech recognition and natural language processing.
All of this expounds the fact that there is no one-size-fits-all approach when it comes to digital transformation. Every company has a different level of maturity or capability, and each must assess its own needs before taking any action. Cloud native companies are well positioned in this regard as they have already built out their own digital platforms with capabilities such as automated deployment and scaling, microservices and containerization, and APIs that help cultivate new business models. They can also use these capabilities to help other enterprises move forward with their respective digital transformations.
