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Understanding Automation Software

Automation refers to the use of technology for performing tasks with reduced human assistance. It can be applied to any industry that involves repetitive tasks. However, it is more profoundly implemented in the industries of robotics, manufacturing, automotives and technology.

In the technology industry, automation is used for developing IT systems and business decision software.

  • IT Automation: In case of IT, automation can be integrated with and applied to anything from network automation to infrastructure, methodologies, DevOps, cloud, edge computing, security, testing, monitoring, and alerting.
  • Business Automation: It involves the alignment of business process management and business rules management with the process of modern application development. The underlying goal of business automation is to meet changing market demands.

The current market scenario requires businesses to undergo Digital Transformation. Instead of focusing on streamlining processes like automating customer records for sales, businesses now need to focus on developing new opportunities like automating complete business operations. This requires business and IT leaders to partner together for developing automation software and applications for business operations.

However, a simple question that needs to be answered is: Why Should a Business Adopt Automation Software?

In modern day scenario, businesses face multiple challenges such as supporting their employees, reaching out to new customers, providing innovative products & services at a faster speed. Automation software helps the business in managing, changing and adapting its IT infrastructure as well as business operations. Simplifying basic operational processes frees up time for businesses to focus on innovation and creativity.

Here are some other reasons that support the decision of adopting automation software for businesses:

  • It is hard to manage IT operations and processes while adopting new processes and staying in compliance with dynamic legal systems.
  • Requirements and demand are growing exponentially faster as compared to IT and business capabilities.
  • New methodologies such as DevOps are forcing changes in business culture.
  • The scaling up of business technology including virtualization, Cloud, etc. is too extensive to be performed manually.

An automation software for businesses holds its importance in improving productivity, consistency, and efficiency. Some advantages of automation software for businesses are:

  • Higher Productivity: As the automation software handles the repetitive tasks, the IT team is free to use the skills for more productive tasks such as developing new opportunities.
  • Better Reliability: Reducing the amount of human intervention in repetitive tasks helps in reducing the errors. A software brings reliability to the tasks as the processes, testing, updates, and workflow happen in the same order and time, making the results more reliable.
  • Easier Governance: A software can be coded easily to implement any changes making it easier to oversee the implementation and processes.

For more information on automation software, call Centex Technologies at (972) 375 - 9654.      

Understanding The Difference Between AI, Machine Learning & Deep Learning

Artificial Intelligence (AI), Machine Learning and Deep Learning are commonly used interchangeably. However, in technological context; Machine Learning and Deep Learning are subsets of AI. In order to understand the difference between these terms, it is important to know the actual meaning of individual term.

Artificial Intelligence (AI): Artificial Intelligence is a term that defines the simulation of human intelligence processes by computer systems. The processes include learning, reasoning and self-correction. AI is broadly classified as weak (narrow) AI and strong AI. Weak AI systems are designed to do a particular task. The most common example of weak AI is the virtual personal assistants. On the contrary, strong AI systems are equipped with generalized human cognitive abilities. These systems are able to find a solution to any problem independent of human intervention.

Machine Learning (ML): ML is an application or subset of Artificial Intelligence. Under this application of AI, a machine is programmed to access and manipulate data. The machine can analyze the data to identify patterns and learn from these patterns. This allows the machine or computer system to modify decisions as per any change in data without explicit programming. Machine Learning is driven by algorithms and stat models. The common usage of Machine Learning can be found in apps such as email filtering, optimization, internet fraud detection, etc. Machine Learning methods are widely grouped as supervised and unsupervised ML.

  • Unsupervised ML: These methods group interpret data based only on input data. Clustering methods are an example of unsupervised ML.
  • Supervised ML: Supervised ML methods use both input and output data to develop a predictive model. Classification and Regression methods are listed as supervised ML.

Deep Learning (DL): It is a broader subset of AI. Deep Learning involves collection of large unstructured data and combing through it to generate classified structured information. The basic difference between Machine Learning and Deep Learning is that ML is task oriented learning, whereas DL is more general. It is used to derive meaning or identify patterns in unstructured data. This, in turn, helps in spotting large scale trends or irregularities. Some common applications of Deep Learning include self-driving cars, fraud news detection, natural language processing, visual recognition, etc.

For more information about Artificial Intelligence, Machine Learning and Deep Learning, call Centex Technologies at (972) 375-9654.