Most machine learning tasks are in the domain of supervised learning. In supervised learning algorithms, the individual instances/data points in the dataset have a class or label assigned to them. This means that the machine learning model can learn to distinguish which features are correlated with a given class and that the machine learning engineer can check the models performance by seeing how many instances were properly classified. Classification algorithms can be used to discern many complex patterns, as long as the data is labeled with the proper classes. For instance, a machine-learning algorithm can learn to distinguish different animals from each other based on characteristics like whiskers, tails, claws, etc.

In contrast to supervised learning, unsupervised learning involves creating a model that is able to extract patterns from unlabeled data. In other words, the computer analyzes the input features and determines for itself what the most important features and patterns are. Unsupervised learning tries to find the inherent similarities between different instances. If a supervised learning algorithm aims to place data points into known classes, unsupervised learning algorithms will examine the features common to the object instances and place them into groups based on these features, essentially creating their own classes.

Examples of supervised learning algorithms are Linear Regression, Logistic Regression, K-nearest Neighbors, Decision Trees, and Support Vector Machines.

Meanwhile, some examples of unsupervised learning algorithms are Principal Component Analysis and K-Means Clustering.

Linear Regression is an algorithm that takes two features and plots out the relationship between them. Linear Regression is used to predict numerical values in relation to other numerical variables. Linear Regression has the equation of `Y = a + bX`

, where b is the lines slope and a is where y crosses the X-axis.

Logistic Regression is a binary classification algorithm. The algorithm examines the relationship between numerical features and finds the probability that the instance can be classified into one of two different classes. The probability values are squeezed towards either 0 or 1. In other words, strong probabilities will approach 0.99 while weak probabilities will approach 0.

K-Nearest Neighbors assigns a class to new data points based on the assigned classes of some chosen amount of neighbors in the training set. The number of neighbors considered by the algorithm is important, and too few or too many neighbors can misclassify points.

Decision Trees are a type of classification and regression algorithm. A decision tree operates by splitting up a dataset into smaller and smaller portions until the subsets cant be split any further and what results is a tree with nodes and leaves. The nodes are where decisions about data points are made using different filtering criteria, while the leaves are the instances that have been assigned some label (a data point that has been classified). Decision tree algorithms are capable of handling both numerical and categorical data. Splits are made in the tree on specific variables/features.

Support Vector Machines are a classification algorithm that operates by drawing hyperplanes, or lines of separation, between data points. Data points are separated into classes based on which side of the hyperplane they are on. Multiple hyperplanes can be drawn across a plane, diving a dataset into multiple classes. The classifier will try to maximize the distance between the diving hyperplane and the points on either side of the plane, and the greater the distance between the line and the points, the more confident the classifier is.

Principal Component Analysis is a technique used for dimensionality reduction, meaning that the dimensionality or complexity of the data is represented in a simpler fashion. The Principal Component Analysis algorithm finds new dimensions for the data that are orthogonal. While the dimensionality of the data is reduced, the variance between the data should be preserved as much as possible. What this means in practical terms is that it takes the features in the dataset and distills them down into fewer features that represent most of the data.

K-Means Clustering is an algorithm that automatically groups data points into clusters based on similar features. The patterns within the dataset are analyzed and the data points are split into groups based on these patterns. Essentially, K-means creates its own classes out of unlabeled data. The K-Means algorithm operates by assigning centers to the clusters, or centroids, and moving the centroids until the optimal position for the centroids is found. The optimal position will be one where the distance between the centroids to the surrounding data points within the class is minimized. The K in K-means clustering refers to how many centroids have been chosen.

To close, lets quickly go over the key differences between supervised and unsupervised learning.

As we previously discussed, in supervised learning tasks the input data is labeled and the number of classes is known. Meanwhile, input data is unlabeled and the number of classes is not known in unsupervised learning cases. Unsupervised learning tends to be less computationally complex, whereas supervised learning tends to be more computationally complex. While supervised learning results tend to be highly accurate, unsupervised learning results tend to be less accurate/moderately accurate.

]]>Machine learning doesnt refer to just one thing, its an umbrella term that can be applied to many different concepts and techniques. Understanding machine learning means being familiar with different forms of model analysis, variables, and algorithms. Lets take a close look at machine learning to better understand what it encompasses.

While the term machine learning can be applied to many different things, in general, the term refers to enabling a computer to carry out tasks without receiving explicit line-by-line instructions to do so. A machine learning specialist doesnt have to write out all the steps necessary to solve the problem because the computer is capable of learning by analyzing patterns within the data and generalizing these patterns to new data.

Machine learning systems have three basic parts:

- Inputs
- Algorithms
- Outputs

The inputs are the data that is fed into the machine learning system, and the input data can be divided into labels and features. Features are the relevant variables, the variables that will be analyzed to learn patterns and draw conclusions. Meanwhile, the labels are classes/descriptions given to the individual instances of the data.

Features and labels can be used in two different types of machine learning problems: supervised learning and unsupervised learning.

In supervised learning, the input data is accompanied by ground truth. Supervised learning problems have the correct output values as part of the dataset, so the expected classes are known in advance. This makes it possible for the data scientist to check the performance of the algorithm by testing the data on a test dataset and seeing what percentage of items were correctly classified.

In contrast, unsupervised learning problems do not have ground truth labels attached to them. A machine learning algorithm trained to carry out unsupervised learning tasks must be able to infer the relevant patterns in the data for itself.

Supervised learning algorithms are typically used for classification problems, where one has a large dataset filled with instances that must be sorted into one of many different classes. Another type of supervised learning is a regression task, where the value output by the algorithm is continuous in nature instead of categorical.

Meanwhile, unsupervised learning algorithms are used for tasks like density estimation, clustering, and representation learning. These three tasks need the machine learning model to infer the structure of the data, there are no predefined classes given to the model.

Lets take a brief look at some of the most common algorithms used in both unsupervised learning and supervised learning.

Common supervised learning algorithms include:

- Naive Bayes
- Support Vector Machines
- Logistic Regression
- Random Forests
- Artificial Neural Networks

Support Vector Machines are algorithms that divide up a dataset into different classes. Data points are grouped into clusters by drawing lines that separate the classes from one another. Points found on one side of the line will belong to one class, while the points on the other side of the line are a different class. Support Vector Machines aim to maximize the distance between the line and the points found on either side of the line, and the greater the distance the more confident the classifier is that the point belongs to one class and not another class.

Logistic Regression is an algorithm used in binary classification tasks when data points need to be classified as belonging to one of two classes. Logistic Regression works by labeling the data point either a 1 or a 0. If the perceived value of the data point is 0.49 or below, it is classified as 0, while if it is 0.5 or above it is classified as 1.

Decision Tree algorithms operate by dividing datasets up into smaller and smaller fragments. The exact criteria used to divide the data is up to the machine learning engineer, but the goal is to ultimately divide the data up into single data points, which will then be classified using a key.

A Random Forest algorithm is essentially many single Decision Tree classifiers linked together into a more powerful classifier.

The Naive Bayes Classifier calculates the probability that a given data point has occurred based on the probability of a prior event occurring. It is based on Bayes Theorem and it places the data points into classes based on their calculated probability. When implementing a Naive Bayes classifier, it is assumed that all the predictors have the same influence on the class outcome.

An Artificial Neural Network, or multi-layer perceptron, is a machine learning algorithm inspired by the structure and function of the human brain. Artificial neural networks get their name from the fact that they are made out of many nodes/neurons linked together. Every neuron manipulates the data with a mathematical function. In artificial neural networks, there are input layers, hidden layers, and output layers.

The hidden layer of the neural network is where the data is actually interpreted and analyzed for patterns. In other words, it is where the algorithm learns. More neurons joined together to make more complex networks capable of learning more complex patterns.

Unsupervised Learning algorithms include:

- K-means clustering
- Autoencoders
- Principal Component Analysis K-means clustering is an unsupervised classification technique, and it works by separating points of data into clusters or groups based on their features. K-means clustering analyzes the features found in the data points and distinguishes patterns in them that make the data points found in a given class cluster more similar to each other than they are to clusters containing the other data points. This is accomplished by placing possible centers for the cluster, or centroids, in a graph of the data and reassigning the position of the centroid until a position is found that minimizes the distance between the centroid and the points that belong to that centroids class. The researcher can specify the desired number of clusters.

Principal Component Analysis is a technique that reduces large numbers of features/variables down into a smaller feature space/fewer features. The principal components of the data points are selected for preservation, while the other features are squeezed down into a smaller representation. The relationship between the original data potions is preserved, but since the complexity of the data points is simpler, the data is easier to quantify and describe.

Autoencoders are versions of neural networks that can be applied to unsupervised learning tasks. Autoencoders are capable of taking unlabeled, free-form data and transforming them into data that a neural network is capable of using, basically creating their own labeled training data. The goal of an autoencoder is to convert the input data and rebuild it as accurately as possible, so its in the incentive of the network to determine which features are the most important and extract them.

]]>But the biggest selling point of Python for data science is its wide variety of libraries that can help programmers solve a range of problems.

Lets take a look at the 10 best Python libraries for data science:

Topping our list of the 10 best Python libraries for data science is TensorFlow, developed by the Google Brain Team. TensorFlow is an excellent choice for both beginners and professionals, and it offers a wide range of flexible tools, libraries, and community resources.

The library is aimed at high-performance numerical computations, and it has around 35,000 comments and a community of more than 1,500 contributors. Its applications are used across scientific fields, and its framework lays the foundation for defining and running computations that involve tensors, which are partially defined computational objects that eventually produce a value.

TensorFlow is especially useful for tasks like speech and image recognition, text-based applications, time-series analysis, and video detection.

Here are some of the main features of TensorFlow for data science:

- Reduces error by 50 to 60 percent in neural machine learning
- Excellent library management
- Flexible architecture and framework
- Runs on a variety of computational platforms

Another top Python library for data science is SciPy, which is a free and open-source Python library used for high-level computations. Like TensorFlow, SciPy has a large and active community numbering in the hundreds of contributors. SciPy is especially useful for scientific and technical computations, and it provides various user-friendly and efficient routines for scientific calculations.

SciPy is based on Numpy, and it includes all of the functions while turning them into user-friendly, scientific tools. SciPy is excellent at performing scientific and technical computing on large datasets, and its often applied for multidimensional image operations, optimization algorithms, and linear algebra.

Here are some of the main features of SciPy for data science:

- High-level commands for data manipulation and visualization
- Built-in functions for solving differential equations
- Multidimensional image processing
- Large data set computation

Another one of the most widely used Python libraries for data science is Pandas, which provides data manipulation and analysis tools that can be used to analyze data. The library contains its own powerful data structures for manipulating numerical tables and time series analysis.

Two of the top features of the Pandas library are Series and DataFrames libraries, which are fast and efficient ways to manage and explore data. These represent data efficiently and manipulate it in different ways.

Some of the main applications of Pandas include general data wrangling and data cleaning, statistics, finance, date range generation, linear regression, and much more.

Here are some of the main features of Pandas for data science:

- Create your own function and run it across a series of data
- High-level abstraction
- High-level structures and manipulation tools
- Merging/joining of datasets

Numpy is a Python library that can be seamlessly utilized for large multi-dimensional array and matrix processing. It uses a large set of high-level mathematical functions that make it especially useful for efficient fundamental scientific computations.

NumPy is a general-purpose array-processing package providing high-performance arrays and tools, and it addresses slowness by providing multidimensional arrays and functions and operators that operate efficiently on them.

The Python library is often applied for data analysis, the creation of powerful N-dimensional arrays, and forming the base of other libraries like SciPy and scikit-learn.

Here are some of the main features of NumPy for data science:

- Fast, precompiled functions for numerical routines
- Supports object-oriented approach
- Array-oriented for more efficient computing
- Data cleaning and manipulation

Matplotlib is a plotting library for Python that has a community of over 700 contributors. It produces graphs and plots that can be used for data visualization, as well as an object-oriented API for embedding the plots into applications.

One of the most popular choices for data science, Matplotlib has a variety of applications. It can be used for the correlation analysis of variables, to visualize confidence intervals of models and the distribution of data to gain insights, and for outlier detection using a scatter plot.

Here are some of the main features of Matplotlib for data science:

- Can be a MATLAB replacement
- Free and open source
- Supports dozens of backends and output types
- Low memory consumption

Scikit-learn is another great Python library for data science. The machine-learning library provides a variety of useful machine-learning algorithms, and it is designed to be interpolated into SciPy and NumPy.

Scikit-learn includes gradient boosting, DBSCAN, random forests within the classification, regression, clustering methods, and support vector machines.

The Python library is often used for applications like clustering, classification, model selection, regression, and dimensionality reduction.

Here are some of the main features of Scikit-learn for data science:

- Data classification and modeling
- Pre-processing of data
- Model selection
- End-to-end machine learning algorithms

Keras is a highly popular Python library often used for deep learning and neural network modules, similar to TensorFlow. The library supports both the TensorFlow and Theano backends, which makes it a great choice for those who dont want to get too involved with TensorFlow.

The open-source library provides you with all of the tools needed to construct models, analyze datasets, and visualize graphs, and it includes prelabeled datasets that can be directly imported and loaded. The Keras library is modular, extensible, and flexible, making it a user-friendly option for beginners. On top of that, it also offers one of the widest ranges for data types.

Keras is often sought out for the deep learning models that are available with pre-trained weights, and these can be used to make predictions or to extract its features without creating or training your own model.

Here are some of the main features of Keras for data science:

- Developing neural layers
- Data pooling
- Activation and cost functions
- Deep learning and machine learning models

Scrapy is one of the best-known Python libraries for data science. The fast and open-source web crawling Python frameworks are often used to extract data from the web page with the help of XPath-based selectors.

The library has a wide range of applications, including being used to build crawling programs that retrieve structured data from the web. It is also used to gather data from APIs, and it enables users to write universal codes that can be reused for building and scaling large crawlers.

Here are some of the main features of Scrapy for data science:

- Lightweight and open source
- Robust web scraping library
- Extracts data from online pages with XPath selectors
- Built-in support

Nearing the end of our list is PyTorch, which is yet another top Python library for data science. The Python-based scientific computing package relies on the power of graphics processing units, and it is often chosen as a deep learning research platform with maximum flexibility and speed.

Created by Facebooks AI research team in 2016, PyTorchs best features include its high speed of execution, which it can achieve even when handling heavy graphs. It is highly flexible and capable of operating on simplified processors or CPUs and GPUs.

Here are some of the main features of PyTorch for data science:

- Control over datasets
- Highly flexible and fast
- Development of deep learning models
Statistical distribution and operations

**BeautifulSoup**

Closing out our list of the 10 best Python libraries for data science is BeautifulSoup, which is most often used for web crawling and data scraping. With BeautifulSoup, users can collect data that are available on a website without a proper CSV or API. At the same time, the Python library helps scrape the data and arrange it into the required format.

BeautifulSoup also has an established community for support and comprehensive documentation that allows for easy learning.

Here are some of the main features of BeautifulSoup for data science:

- Community support
- Web crawling and data scraping
- Easy to use
- Collect data without proper CSV or API

Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models.

The data used for analysis can come from many different sources and be presented in various formats.

Now that you know what data science is, lets see why data science is essential to todays IT landscape.

Now that you know what is data science, next up let us focus on the data science lifecycle. Data sciences lifecycle consists of five distinct stages, each with its own tasks:

- Capture: Data Acquisition, Data Entry, Signal Reception, Data Extraction. This stage involves gathering raw structured and unstructured data.
- Maintain: Data Warehousing, Data Cleansing, Data Staging, Data Processing, and Data Architecture. This stage covers taking the raw data and putting it in a form that can be used.
- Process: Data Mining, Clustering/Classification, Data Modeling, Data Summarization. Data scientists take the prepared data and examine its patterns, ranges, and biases to determine how useful it will be in predictive analysis.
- Analyze: Exploratory/Confirmatory, Predictive Analysis, Regression, Text Mining, Qualitative Analysis. Here is the real meat of the lifecycle. This stage involves performing various analyses of the data.
- Communicate: Data Reporting, Data Visualization, Business Intelligence, Decision Making. In this final step, analysts prepare the analyses in easily readable forms such as charts, graphs, and reports.

Here are some of the technical concepts you should know about before starting to learn what is data science.

Machine Learning Machine learning is the backbone of data science. Data Scientists need to have a solid grasp of ML in addition to basic knowledge of statistics.

Modeling Mathematical models enable you to make quick calculations and predictions based on what you already know about the data. Modeling is also a part of Machine Learning and involves identifying which algorithm is the most suitable to solve a given problem and how to train these models.

Statistics Statistics are at the core of data science. A sturdy handle on statistics can help you extract more intelligence and obtain more meaningful results.

Programming Some level of programming is required to execute a successful data science project. The most common programming languages are Python, and R. Python is especially popular because its easy to learn, and it supports multiple libraries for data science and ML.

Databases A capable data scientist needs to understand how databases work, how to manage them, and how to extract data from them.

**Business Managers**

The business managers are the people in charge of overseeing the data science training method. Their primary responsibility is to collaborate with the data science team to characterize the problem and establish an analytical method. A data scientist may oversee the marketing, finance, or sales department, and report to an executive in charge of the department. Their goal is to ensure projects are completed on time by collaborating closely with data scientists and IT managers.

**IT Managers**

Following them are the IT managers. If the member has been with the organization for a long time, the responsibilities will undoubtedly be more important than any others. They are primarily responsible for developing the infrastructure and architecture to enable data science activities. Data science teams are constantly monitored and resourced accordingly to ensure that they operate efficiently and safely. They may also be in charge of creating and maintaining IT environments for data science teams.

**Data Science Managers**

The data science managers make up the final section of the tea. They primarily trace and supervise the working procedures of all data science team members. They also manage and keep track of the day-to-day activities of the three data science teams. They are team builders who can blend project planning and monitoring with team growth.

Data scientists are among the most recent analytical data professionals who have the technical ability to handle complicated issues as well as the desire to investigate what questions need to be answered. They're a mix of mathematicians, computer scientists, and trend forecasters. They're also in high demand and well-paid because they work in both the business and IT sectors.

On a daily basis, a data scientist may do the following tasks:

- Discover patterns and trends in datasets to get insights.
- Create forecasting algorithms and data models.
- Improve the quality of data or product offerings by utilizing machine learning techniques.
- Distribute suggestions to other teams and top management.
- In data analysis, use data tools such as R, SAS, Python, or SQL.
- Top the field of data science innovations.

You know what is data science, and you must be wondering what exactly is this job role like - here's the answer. A data scientist analyzes business data to extract meaningful insights. In other words, a data scientist solves business problems through a series of steps, including:

- Before tackling the data collection and analysis, the data scientist determines the problem by asking the right questions and gaining understanding.
- The data scientist then determines the correct set of variables and data sets.
- The data scientist gathers structured and unstructured data from many disparate sourcesenterprise data, public data, etc.
- Once the data is collected, the data scientist processes the raw data and converts it into a format suitable for analysis. This involves cleaning and validating the data to guarantee uniformity, completeness, and accuracy.
- After the data has been rendered into a usable form, its fed into the analytic systemML algorithm or a statistical model. This is where the data scientists analyze and identify patterns and trends.
- When the data has been completely rendered, the data scientist interprets the data to find opportunities and solutions.
- The data scientists finish the task by preparing the results and insights to share with the appropriate stakeholders and communicating the results.

Now we should be aware of some machine learning algorithms which are beneficial in understanding data science clearly.

You learned what is data science. Did it sound exciting? Here's another solid reason why you should pursue data science as your work field. According to Glassdoor and Forbes, demand for data scientists will increase by 28 percent by 2026, which speaks of the professions durability and longevity, so if you want a secure career, data science offers you that chance.

Furthermore, the profession of data scientist came in second place in the Best Jobs in America for 2021 survey, with an average base salary of USD 130,500.

So, if youre looking for an exciting career that offers stability and generous compensation, then look no further!

- Data science may detect patterns in seemingly unstructured or unconnected data, allowing conclusions and predictions to be made.
- Tech businesses that acquire user data can utilize strategies to transform that data into valuable or profitable information.
- Data Science has also made inroads into the transportation industry, such as with driverless cars. It is simple to lower the number of accidents with the use of driverless cars. For example, with driverless cars, training data is supplied to the algorithm, and the data is examined using data Science approaches, such as the speed limit on the highway, busy streets, etc.
- Data Science applications provide a better level of therapeutic customization through genetics and genomics research.

Data science offers you the opportunity to focus on and specialize in one aspect of the field. Heres a sample of different ways you can fit into this exciting, fast-growing field.

**Data Scientist**

- Job role: Determine what the problem is, what questions need answers, and where to find the data. Also, they mine, clean, and present the relevant data.
- Skills needed: Programming skills (SAS, R, Python), storytelling and data visualization, statistical and mathematical skills, and knowledge of Hadoop, SQL, and Machine Learning.

**Data Analyst**

- Job role: Analysts bridge the gap between the data scientists and the business analysts, organizing and analyzing data to answer the questions the organization poses. They take the technical analyses and turn them into qualitative action items.
- Skills needed: Statistical and mathematical skills, programming skills (SAS, R, Python), plus experience in data wrangling and data visualization.

**Data Engineer**

- Job role: Data engineers focus on developing, deploying, managing, and optimizing the organizations data infrastructure and data pipelines. Engineers support data scientists by helping to transfer and transform data for queries.
- Skills needed: NoSQL databases (e.g., MongoDB, Cassandra DB), programming languages such as Java and Scala, and frameworks (Apache Hadoop).

**Data Science Tools**

The data science profession is challenging, but fortunately, there are plenty of tools available to help data scientists succeed at their job.

- Data Analysis: SAS, Jupyter, R Studio, MATLAB, Excel, RapidMiner
- Data Warehousing: Informatica/ Talend, AWS Redshift
- Data Visualization: Jupyter, Tableau, Cognos, RAW
- Machine Learning: Spark MLib, Mahout, Azure ML studio

Data science has found its applications in almost every industry.

**Healthcare**

Healthcare companies are using data science to build sophisticated medical instruments to detect and cure diseases.

**Gaming**

Video and computer games are now being created with the help of data science and that has taken the gaming experience to the next level.

**Image Recognition**

Identifying patterns in images and detecting objects in an image is one of the most popular data science applications.

**Recommendation Systems**

Netflix and Amazon give movie and product recommendations based on what you like to watch, purchase, or browse on their platforms.

**Logistics**

Data Science is used by logistics companies to optimize routes to ensure faster delivery of products and increase operational efficiency.

**Fraud Detection**

Banking and financial institutions use data science and related algorithms to detect fraudulent transactions.

**Internet Search**

When we think of search, we immediately think of Google. Right? However, there are other search engines, such as Yahoo, Duckduckgo, Bing, AOL, Ask, and others, that employ data science algorithms to offer the best results for our searched query in a matter of seconds. Given that Google handles more than 20 petabytes of data per day. Google would not be the 'Google' we know today if data science did not exist.

**Speech recognition**

Speech recognition is dominated by data science techniques. We may see the excellent work of these algorithms in our daily lives. Have you ever needed the help of a virtual speech assistant like Google Assistant, Alexa, or Siri? Well, its voice recognition technology is operating behind the scenes, attempting to interpret and evaluate your words and delivering useful results from your use. Image recognition may also be seen on social media platforms such as Facebook, Instagram, and Twitter. When you submit a picture of yourself with someone on your list, these applications will recognize them and tag them.

**Targeted Advertising**

If you thought Search was the most essential data science use, consider this: the whole digital marketing spectrum. From display banners on various websites to digital billboards at airports, data science algorithms are utilized to identify almost anything. This is why digital advertisements have a far higher CTR (Call-Through Rate) than traditional marketing. They can be customized based on a user's prior behavior. That is why you may see adverts for Data Science Training Programs while another person sees an advertisement for clothes in the same region at the same time.

**Airline Route Planning**

As a result of data science, it is easier to predict flight delays for the airline industry, which is helping it grow. It also helps to determine whether to land immediately at the destination or to make a stop in between, such as a flight from Delhi to the United States of America or to stop in between and then arrive at the destination.

**Augmented Reality**

Last but not least, the final data science applications appear to be the most fascinating in the future. Yes, we are discussing something other than augmented reality. Do you realize there's a fascinating relationship between data science and virtual reality? A virtual reality headset incorporates computer expertise, algorithms, and data to create the greatest viewing experience possible. The popular game Pokemon GO is a minor step in that direction. The ability to wander about and look at Pokemon on walls, streets, and other non-existent surfaces. The makers of this game chose the locations of the Pokemon and gyms using data from Ingress, the previous app from the same business.

]]>**Kubernetes defined**

Kubernetes (sometimes shortened to K8s with the 8 standing for the number of letters between the K and the s) is an open-source system to deploy, scale, and manage containerized applications anywhere.

*Kubernetes automates operational tasks of container management and includes built-in commands for deploying applications, rolling out changes to your applications, scaling your applications up and down to fit changing needs, monitoring your applications, and moremaking it easier to manage applications.*

Kubernetes has built-in commands to handle a lot of the heavy lifting that goes into application management, allowing you to automate day-to-day operations. You can make sure applications are always running the way you intended them to run.

When you install Kubernetes, it handles the compute, networking, and storage on behalf of your workloads. This allows developers to focus on applications and not worry about the underlying environment.

Kubernetes continuously runs health checks against your services, restarting containers that fail, or have stalled, and only making available services to users when it has confirmed they are running.

Often misunderstood as a choice between one or the other, Kubernetes and Docker are different yet complementary technologies for running containerized applications.

Docker lets you put everything you need to run your application into a box that can be stored and opened when and where it is required. Once you start boxing up your applications, you need a way to manage them; and that's what Kubernetes does.

Kubernetes is a Greek word meaning captain in English. Like the captain is responsible for the safe journey of the ship on the seas, Kubernetes is responsible for carrying and delivering those boxes safely to locations where they can be used.

Kubernetes can be used with or without Docker

Docker is not an alternative to Kubernetes, so its less of a Kubernetes vs. Docker question. Its about using Kubernetes with Docker to containerize your applications and run them at scale

The difference between Docker and Kubernetes relates to the role each play in containerizing and running your applications

Docker is an open industry standard for packaging and distributing applications in containers

Kubernetes uses Docker to deploy, manage, and scale containerized applications

Kubernetes is used to create applications that are easy to manage and deploy anywhere. When available as a managed service, Kubernetes offers you a range of solutions to meet your needs. Here are some common use cases.

Kubernetes helps you to build cloud-native microservices-based apps. It also supports the containerization of existing apps, thereby becoming the foundation of application modernization and letting you develop apps faster.

Kubernetes is built to be used anywhere, allowing you to run your applications across on-site deployments and public clouds; as well as hybrid deployments in between. So you can run your applications where you need them.

Kubernetes can automatically adjust the size of a cluster required to run a service. This enables you to automatically scale your applications, up and down, based on the demand and run them efficiently.

]]>