- Hardware. These are the equipment that forms the components of a network, such as user devices (laptops, computers, mobile phones), routers, servers, and gateways. So, in a way, the goal of any network architecture is to find the most efficient way to get data from one hardware point to another.
- Transmission Media. Transmission media refers to the physical connections between the hardware devices on a network. Different media have various properties that determine how fast data travels from one point to another. They come in two forms: wired and wireless. Wired media involve physical cables for connection. Examples include coaxial and fibre optic. Wireless media, on the other hand, relies on microwave or radio signals. The most popular examples are WiFi and cellular.
- Protocols. Protocols are the rules and models that govern how data transfers between devices in a network. It’s also the common language that allows different machines in a network to communicate with each other. Without protocols, your iPhone couldn’t access a web page stored on a Linux server. There are many network protocols, depending on the nature of the data. Examples include the Transmission Control Protocol / Internet Protocol (TCP/IP) used by networks to connect to the internet, the Ethernet protocol for connecting one computer to another, and the File Transfer Protocol for sending and receiving files to and from a server.
- Topology. How the network is wired together is just as important as its parts. Optimising this is the goal of network topology. Topology is the structure of the network. This is important because factors like distance between network devices will affect how fast data can reach its destination, impacting performance. There are various network topologies, each with strengths and weaknesses. Today, most network architectures use a hybrid topology, combining different topologies to compensate for each individual’s weakness.
Neural networks are a set of algorithms, modeled loosely after the human brain, which are designed to recognise patterns. They interpret sensory data through a kind of machine perception, labelling or clustering raw input. The patterns they recognise are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated.
Neural networks help us cluster and classify. You can think of them as a clustering and classification layer on top of the data you store and manage. They help to group unlabelled data according to similarities among the example inputs, and they classify data when they have a labelled dataset to train on. Neural networks can also extract features that are fed to other algorithms for clustering and classification, so you can think of deep neural networks as components of larger machine-learning applications involving algorithms for reinforcement learning, classification and regression.)
- [Tie] Apache Cassandra
Artificial Intelligence
DataOps
DevOps
Machine Learning
Average pay premium: 17% of base salary equivalent
Market value increase: 6.3% (in the six months through July 1, 2022)
Apache Cassandra, a backbone for Facebook and Netflix, is a highly scalable, high-performance distributed NoSQL database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Cassandra offers robust support for clusters spanning multiple datacentres, with asynchronous masterless replication across cloud service providers, allowing low latency operations for all clients. It can handle petabytes of information and thousands of concurrent operations per second across hybrid cloud environments and is simple to configure, providing neat solutions for quite complex problems. Cassandra offers the distribution design of Amazon Dynamo with the data model of Google's Bigtable.
Artificial Intelligence is a term that means different things to different people, from robots coming to take your jobs to the digital assistants in your mobile phone and home. But it is actually a term that encompasses a collection of technologies that include machine learning, deep learning, natural language processing, computer vision, and more. Artificial intelligence can also be divided into ‘narrow AI’ and ‘general AI’. Narrow AI is the kind we most often see today -- AI suited for a narrow task. This could include recommendation engines, navigation apps, or chatbots. These are AIs designed for specific tasks. Artificial general intelligence is about a machine performing any task that a human can perform, and this technology rapidly expanding though still relatively aspirational for many organisations.
Machine learning (described below) is typically the first step for organisations that are adding AI-related technologies to their IT portfolio and one of the reasons why AI skills pay is growing. Deep learning, as explained earlier, takes machine learning a few steps further by creating layers of machine learning beyond the first decision point. These hidden layers are called a neural network—as described earlier--and are meant to simulate the way human brains operate. Deep learning works by taking the outcome of the first machine learning decision and making it the input for the next machine learning decision. Each of these is a layer. Python is the language of deep learning and neural networks.
DataOps is a set of practices, processes and technologies that combines an integrated and process-oriented perspective on data with automation and methods from agile software engineering to improve quality, speed, and collaboration and promote a culture of continuous improvement in the area of data analytics. It is not tied to a particular technology, architecture, tool, language or framework: tools that support DataOps promote collaboration, orchestration, quality, security, access and ease of use. And while DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics. It applies to the entire data lifecycle] from data preparation to reporting and recognises the interconnected nature of the data analytics team and information technology operations. It also incorporates the Agile methodology to shorten the cycle time of analytics development in alignment with business goals. DataOps utilises statistical process control (SPC) to monitor and control the data analytics pipeline. With SPC in place, the data flowing through an operational system is constantly monitored and verified to be working. If an anomaly occurs, the data analytics team can be notified through an automated alert.
DevOps is a set of practices that combines software development (Dev) and IT operations (Ops). It aims to shorten the systems development life cycle and provide continuous delivery with high software quality. DevOps is complementary with Agile software development and in fact several DevOps aspects come from the Agile methodology. In its broadest meaning, DevOps is a philosophy that promotes better communication and collaboration between these teams -- and others -- in an organisation. In its most narrow interpretation, DevOps describes the adoption of iterative software development, automation, and programmable infrastructure deployment and maintenance. The term also covers culture changes, such as building trust and cohesion between developers and systems administrators and aligning technological projects to business requirements. DevOps can change the software delivery chain, services, job roles, IT tools and best practices.
While DevOps is not a technology, DevOps environments generally apply common methodologies. These include the following:
- Continuous integration and continuous delivery or continuous deployment (CI/CD) tools, with an emphasis on task automation.
- Systems and tools that support DevOps adoption, including real-time monitoring, incident management, configuration management and collaboration platforms; and
- Cloud computing, microservices and containers implemented concurrently with DevOps methodologies.
DevOps jobs and skills. DevOps is often said to be more of a philosophy or collaborative IT culture rather than a strictly defined job description or skill set. Because the area is so broad, DevOps positions suit IT generalists better than specialists.
The role of DevOps engineer does not fall along one career track. Professionals can enter into the position from a variety of backgrounds. For example, a software developer can gain skills in operations, such as configuration of the hosting infrastructure, to become a DevOps engineer. Similarly, a systems administrator with coding, scripting and testing knowledge can become a DevOps engineer. Candidates benefit from knowledge of containers, cloud and CI/CD, as well as soft skills. A DevOps engineer might also need to change processes and solve organisational problems to achieve business outcomes. Other titles often found in DevOps organisations include infrastructure developer; site reliability engineer; build and release engineer; full-stack developer; automation specialist; and CI/CD platform engineer.
Most entry-level DevOps jobs require a degree in computer science or a related field that covers coding, QA testing and IT infrastructure components. Higher-level positions may require advanced degrees in systems architecture and software design. People on this career path who choose to take a more vendor-dependent path should consider these certifications, among others:
- Google Cloud DevOps Engineer
- AWS Certified DevOps Engineer - Professional
- IBM Certified Solution Advisor - DevOps V2
- Microsoft Certified DevOps Engineer Expert
Machine learning (ML) is a type of artificial intelligence that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms have been around for decades, but they’ve attained new popularity: Our research indicates that jobs and skills in AI and machine learning (and especially deep learning) will maintain their ‘hotness’ and support job creation and cash market values for skills for the foreseeable future. The fact is that machine learning platforms are among enterprise technology's most competitive realms, with many major vendors---Amazon, Google, Microsoft, IBM and others---racing to sign up hungry customers for platform services that cover the spectrum of machine learning activities, including data collection, data preparation, data classification, model building, training and application deployment.
Machine learning algorithms build a model based on historical sample data, known as training data, to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email spam filtering, speech recognition, malware threat and fraud detection, business process automation, predictive maintenance, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. Some implementations of machine learning use data and neural networks in a way that mimics the working of a biological brain. In its application across business problems, machine learning is also referred to as predictive analytics. Machine learning is important because it gives enterprises a view of trends in customer behaviour and business operational patterns, as well as supports the development of new products. Many of today's leading companies make machine learning a central part of their operations. Machine learning has become a significant competitive differentiator for many companies.
Machine learning has already seen so many use cases and they will increase in number. Popular uses right now include:
- Customer relationship management.Using machine learning models to analyse email and prompt sales team members to respond to the most important messages first. More advanced systems can recommend potentially effective responses.
- Business intelligence.BI and analytics vendors use machine learning in their software to identify potentially important data points, patterns of data points and anomalies.
- Human resource information systems.Using machine learning models to filter through applications and identify the best candidates.
- Self-driving cars.Machine learning algorithms make it possible for a semi-autonomous car to recognise a partially visible object and alert the driver.
- Virtual assistants.Smart assistants typically combine supervised and unsupervised machine learning models to interpret natural speech and supply context.
Continued research into deep learning and AI is increasingly focused on developing more general applications. Today's AI models require extensive training in order to produce an algorithm that is highly optimised to perform one task. As machine learning continues to increase in importance to business operations and AI becomes more practical in enterprise settings, the machine learning platform wars will only intensify. Companies are exploring ways to make models more flexible and are seeking techniques that allow a machine to apply context learned from one task to future, different tasks.