About: The 8 Ops

Hello, World!

Welcome to The 8 Ops—your go-to resource for all things: DevOps, Cloud Computing, and Machine Learning! Here, we explore the infinite possibilities of modern technology and its transformative impact on the way we work.

In a world that’s continuously evolving, understanding how to integrate cloud operations and development practices is essential for success. At The 8 Ops, we aim to bridge the gap between traditional development and operational practices, highlighting strategies and tools that empower teams to deliver high-quality software efficiently and effectively.

Our mission is to provide you with valuable insights, tutorials, and resources that demystify the complexities of:

DevOps: Learn about the principles and practices that drive collaboration between development and operations teams, fostering a culture of continuous integration and deployment.
Cloud Computing: Dive into the world of cloud infrastructure, exploring platforms, services, and best practices to harness the power of the cloud for scalable and resilient solutions.
Machine Learning: Discover how machine learning intersects with DevOps to optimize workflows, enhance decision-making, and unlock new opportunities for innovation.

Whether you're a seasoned professional or just starting your journey in tech, The 8 Ops is here to guide you. Join us as we navigate the landscape of technology, sharing knowledge, experiences, and insights that inspire growth and creativity.

Thank you for being a part of our community. We encourage you to engage, share your thoughts, and embark on this exciting journey together!

Comments

Types of Machine Learning problems

In the previous blog, we had discussed brief about What is Machine Learning? In this blog, we are going to learn about the types of ML. ML is broadly classified into four types: Supervised Learning Unsupervised Learning Semi-supervised Learning Reinforcement Learning 1. Supervised Learning Supervised learning is where there are input variables, say X and there are corresponding output variables, say Y. We use a particular algorithm to map a function from input(X) to output(Y). Mathematically, Y=f(X). Majority of the ML models use this type of learning to feed itself and learn. The goal of supervised learning is to approximate the said function so well that whenever we enter any new input, it's output is accurately predicted. Here, we can say that there is a teacher who guides the model if it generates incorrect results and hence, the machine will keep on learning until it performs to desired results. Supervised Learning can be further classified into: Classification : He...

Statistics in Data Science

Introduction Statistics is one of the popularly regarded disciplines this is particularly centered on records collection, records organization, records analysis, records interpretation and records visualization. Earlier, facts become practiced through statisticians, economists, enterprise proprietors to calculate and constitute applicable records of their field. Nowadays, facts have taken a pivotal position in diverse fields like records technology, system learning, records analyst position, enterprise intelligence analyst position, pc technology position, and plenty more. Statistics is a type of mathematical analysis that uses quantified models and representations to analyze a set of experimental data or real-world research. The fundamental benefit of statistics is that information is provided in an easy-to-understand style. Statistical & Non-Statistical Analysis Statistical analysis is used to better understand a wider population by analyzing data from a sample. Statistical analy...

What is Machine Learning?

Arthur Samuel, firstly coined the term "Machine Learning". He defined the term as, "Field of study that gives computers the capability to learn without being explicitly programmed." Explaining in layman terms, Machine learning means improving the process of learning for computers which is based on it's experiences to do a certain task without further guidance through programs. In other words, we can say that machine learns through initial program and feeds itself the data which obtained from the experiences while executing a particular task. Let's take an example to understand this. A father and a baby went to a park to make the baby learn how to walk. Initially, the father hold the hands of his baby so that the baby can walk without tripping. As the baby can now stand on it's own legs, the father did not hold hands of the baby, thus the baby kept going on and tripped as stone hit the toes. The baby stood up and learned not to walk over stones. The next...

The 8 Ops

Search This Blog