Top 9 Real-World Artificial Intelligence That Will Dominate in the Future

Top 9 Real-World Artificial Intelligence That Will Dominate in the Future

Explore the top 9 real-world AI applications and the various fields these applications have impacted.

Just the mention of AI and the brain invokes pictures of Terminator machines destroying the world. Thankfully, the present picture is significantly more positive. So, let’s explore how AI is helping our planet and at last benefiting humankind. In this blog on Artificial Intelligence applications, I’ll be discussing how AI has impacted various fields like healthcare, finance, agriculture, and so on.

Banking

AI in banking is growing faster than you think! A lot of banks have already adopted AI-based systems to provide customer support and detect anomalies and credit card fraud. An example of this is the HDFC Bank.

HDFC Bank has developed an AI-based chatbot called EVA (Electronic Virtual Assistant), built by Bengaluru-based Senseforth AI Research.

Since its launch, Eva has addressed over 3 million customer queries, interacted with over half a million unique users, and held over a million conversations. Eva can collect knowledge from thousands of sources and provide simple answers in less than 0.4 seconds.

The use of AI for fraud prevention is not a new concept. In fact, AI solutions can be used to enhance security across a number of business sectors, including retail and finance.

By tracing card usage and endpoint access, security specialists are more effectively preventing fraud. Organizations rely on AI to trace those steps by analyzing the behaviors of transactions.

Companies such as MasterCard and RBS WorldPay have relied on AI and deep learning to detect fraudulent transaction patterns and prevent card fraud for years now. This has saved millions of dollars.

Marketing

Marketing is a way to sugar coat your products to attract more customers. We humans are pretty good at sugar coating, but what if an algorithm or a bot is built solely for the purpose of marketing a brand or a company? It would do a pretty awesome job!

In the early 2000s, if we searched an online store to find a product without knowing it’s exact name, it would become a nightmare to find the product. But now when we search for an item on any e-commerce store, we get all possible results related to the item. It’s like these search engines read our minds! In a matter of seconds, we get a list of all relevant items. An example of this is finding the right movies on Netflix.

One reason why we’re all obsessed with Netflix is that it provides highly accurate predictive technology based on customer’s reactions to films. It examines millions of records to suggest shows and films that you might like based on your previous actions and choices of films. As the data set grows, the technology gets smarter and smarter every day.

With the growing advancement in AI, it may be possible in the near future for consumers on the web to buy products by snapping a photo of it. Companies like CamFind and their competitors are experimenting with this already.

Finance

Ventures have been relying on computers and data scientists to determine future patterns in the market. Trading mainly depends on the ability to predict the future accurately.

Machines are great at this because they can crunch a huge amount of data in a short span. Machines can also learn to observe patterns in past data and predict how these patterns might repeat in the future.

In the age of ultra-high-frequency trading, financial organizations are turning to AI to improve their stock trading performance and boost profit.

One such organization is Japan’s leading brokerage house, Nomura Securities. The company has been reluctantly pursuing one goal, i.e. to analyze the insights of experienced stock traders with the help of computers. After years of research, Nomura is set to introduce a new stock trading system.

The new system stores a vast amount of price and trading data in its computer. By tapping into this reservoir of information, it will make assessments. For example, it may determine that current market conditions are similar to the conditions two weeks ago and predict how share prices will be changing a few minutes down the line. This will help to take better trading decisions based on the predicted market prices.

Healthcare

When it comes to saving lives, a lot of organizations and medical care centers are relying on AI. There are many examples of how AI in healthcare has helped patients all over the world.

An organization called Cambio Health Care developed a clinical decision support system for stroke prevention that can give the physician a warning when there’s a patient at risk of having a heat stroke.

Another such example is Coala Life, which is a company that has a digitalized device that can find cardiac diseases.

Similarly, Aifloo is developing a system for keeping track of how people are doing in nursing homes, home care, etc. The best thing about AI in healthcare is that you don’t even need to develop a new medication. By using an existing medication in the right way, you can also save lives.

Agriculture

Here’s an alarming fact, the world will need to produce 50 percent more food by 2050 because we’re literally eating up everything! The only way this can be possible is if we use our resources more carefully. With that being said, AI can help farmers get more from the land while using resources more sustainably.

Issues such as climate change, population growth, and food security concerns have pushed the industry into seeking more innovative approaches to improve crop yield.

Organizations are using automation and robotics to help farmers find more efficient ways to protect their crops from weeds.

Blue River Technology has developed a robot called See & Spray, which uses computer vision technologies like object detection to monitor and precisely spray weedicide on cotton plants. Precision spraying can help prevent herbicide resistance.

Apart from this, Berlin-based agricultural tech start-up called PEAT has developed an application called Plantix that identifies potential defects and nutrient deficiencies in the soil through images.

The image recognition app identifies possible defects through images captured by the user’s smartphone camera. Users are then provided with soil restoration techniques, tips, and other possible solutions. The company claims that its software can achieve pattern detection with an estimated accuracy of up to 95%.

Space Exploration

Space expeditions and discoveries always require analyzing vast amounts of data. Artificial intelligence and machine learning is the best way to handle and process data on this scale. After rigorous research, astronomers used Artificial Intelligence to sift through years of data obtained by the Kepler telescope in order to identify a distant eight-planet solar system.

Artificial intelligence is also being used for NASA’s next rover mission to Mars, the Mars 2020 Rover. The AEGIS, which is an AI-based Mars rover, is already on the red planet. The rover is responsible for the autonomous targeting of cameras in order to perform investigations on Mars.

Gaming

Over the past few years, artificial intelligence has become an integral part of the gaming industry. In fact, one of the biggest accomplishments of AI is in the gaming industry.

DeepMind’s AI-based AlphaGo software, which is known for defeating Lee Sedol, the world champion in the game of GO, is considered to be one of the most significant accomplishments in the field of AI.

Shortly after the victory, DeepMind created an advanced version of AlphaGo called AlphaGo Zero, which defeated the predecessor in an AI-AI face off. Unlike the original AlphaGo, which DeepMind trained over time by using a large amount of data and supervision, the advanced system, taught itself to master the game.

Other examples of artificial intelligence in gaming include the First Encounter Assault Recon, popularly known as F.E.A.R, which is a first-person shooter video game.

But what makes this game so special?

The actions taken by the opponent AI are unpredictable because the game is designed in such a way that the opponents are trained throughout the game and never repeat the same mistakes. They get better as the game gets harder. This makes the game very challenging and prompts the players to constantly switch strategies and never sit in the same position.

Chatbots

These days, virtual assistants are a very common technology. Almost every household has a virtual assistant that controls their appliances at home. A few examples include Siri, Cortana, and Alexa, which are gaining popularity because of the user experience they provide.

Amazon’s Echo is an example of how artificial intelligence can be used to translate human language into desirable actions. This device uses speech recognition and NLP to perform a wide range of tasks on your command. It can do more than just play your favorite songs. It can be used to control the devices at your house, book cabs, make phone calls, order your favorite food, check the weather conditions, and so on.

Another example is the newly released Google’s virtual assistant called Google Duplex, which has astonished millions of people. Not only can it respond to calls and book appointments for you, but it also adds a human touch.

The device uses Natural language processing and machine learning algorithms to process human language and perform tasks such as manage your schedule, control your smart home, make a reservation, and so on.

Artificial Creativity

Have you ever wondered what would happen if an artificially intelligent machine tried to create music and art?

An AI-based system called MuseNet can now compose classical music that echoes the classical legends, Bach and Mozart.

MuseNet is a deep neural network that is capable of generating 4-minute musical compositions with 10 different instruments and can combine styles from country to Mozart to the Beatles.

MuseNet was not explicitly programmed with an understanding of music, but instead discovered patterns of harmony, rhythm, and style by learning on its own.

Another creative product of artificial intelligence is a content automation tool called Wordsmith. Wordsmith is a natural language generation platform that can transform your data into insightful narratives.

Tech giants such as Yahoo, Microsoft, Tableau, are using WordSmith to generate around 1.5 billion pieces of content every year.

Social Media

Ever since social media has become our identity, we’ve been generating an immeasurable amount of data through chats, tweets, posts, and so on. And wherever there is an abundance of data, AI and machine learning are always involved.

In social media platforms like Facebook, AI is used for face verification wherein machine learning and deep learning concepts are used to detect facial features and tag your friends. Deep learning is used to extract every minute detail from an image by using a bunch of deep neural networks. On the other hand, machine learning algorithms are used to design your feed based on your interests.

Another such example is Twitter’s AI, which is being used to identify hate speech and terroristic language in tweets. It makes use of machine learning, deep learning, and natural language processing to filter out offensive content. The company discovered and banned 300,000 terrorist-linked accounts, 95% of which were found by non-human, artificially intelligent machines.

Conclusion:

Artificial Intelligence is shaping today and tomorrow. The technology has benefited the modern society with an outlook of a better world that not only peep out of the curtain at present but give a significant and clear picture of an improved and happy world.

I’d like to conclude by asking you how you think AI will benefit us in the future?

Thanks for reading. Keep Visiting

Artificial Intelligence Tutorial

Future Of Artificial Intelligence For 2020

Data Science Vs Machine Learning Vs Artificial Intelligence

☞ The Data Science Course 2019: Complete Data Science Bootcamp

☞ Data Science A-Z™: Real-Life Data Science Exercises Included

Docker + Jupyter for Machine Learning

Docker + Jupyter for Machine Learning

The best practice for setting up such a container is using a docker file, which I have written following the best practices in less than 1 minute. I hope this would help anyone engaging in data science applications with docker.
The project is structured as follows

├── Prject_folder           
│ ├── Dockerfile           #Primary imga building 
│ ├── docker-compose.yml   #Describing the files to mount etc     
│ ├── requirements.txt     #Required python packages for the image

Step 1:

Download the following 3 scripts to your project directory on your computer. The GitHub repo can be found here.

FROM "ubuntu:bionic"

MAINTAINER [email protected]

RUN useradd -ms /bin/bash docker
RUN su docker

ENV LOG_DIR_DOCKER="/root/dockerLogs"
ENV LOG_INSTALL_DOCKER="/root/dockerLogs/install-logs.log"

RUN mkdir -p ${LOG_DIR_DOCKER} \
 && touch ${LOG_INSTALL_DOCKER}  \
 && echo "Logs directory and file created"  | sed -e "s/^/$(date +%Y%m%d-%H%M%S) :  /" 2>&1 | tee -a ${LOG_INSTALL_DOCKER}

RUN apt-get update | sed -e "s/^/$(date +%Y%m%d-%H%M%S) :  /" 2>&1 | tee -a ${LOG_INSTALL_DOCKER} \
  && apt-get install -y python3-pip python3-dev | sed -e "s/^/$(date +%Y%m%d-%H%M%S) :  /" 2>&1 | tee -a ${LOG_INSTALL_DOCKER} \
  && ln -s /usr/bin/python3 /usr/local/bin/python | sed -e "s/^/$(date +%Y%m%d-%H%M%S) :  /" 2>&1 | tee -a ${LOG_INSTALL_DOCKER} \
  && pip3 install --upgrade pip | sed -e "s/^/$(date +%Y%m%d-%H%M%S) :  /" 2>&1 | tee -a ${LOG_INSTALL_DOCKER}

COPY requirements.txt /root/datascience/requirements.txt
WORKDIR /root/datascience
RUN pip3 install -r requirements.txt
 
RUN jupyter notebook --generate-config --allow-root
RUN echo "c.NotebookApp.password = u'sha1:6a3f528eec40:6e896b6e4828f525a6e20e5411cd1c8075d68619'" >> /root/.jupyter/jupyter_notebook_config.py

CMD ["jupyter", "notebook", "--allow-root", "--notebook-dir=.", "--ip=0.0.0.0", "--port=8888", "--no-browser"]

Dockerfile

object_detection:
  image: datascience
  container_name: datascience
  restart: always
  environment:
     - TERM=xterm
  hostname: '127.0.0.1'
  ports:
     - "8888:8888"         #JupyterNB

  volumes:
     - /Users/chamalgomes/Documents/Python/GitHub/medium/JupyterWithDocker/test.txt:/root/datascience/test.txt

docker-compose.yml

Update it as {absolutePATH_to_yourFile}/{fileNameame}:/root/datascience/{fileName} to mount you preferred files.

tensorflow==1.14.0 
jupyter==1.0.0

requirements.txt

Update it to include the packages you require i.e matplotlib==1.0.0

Step 2:

Install Docker on your computer https://docs.docker.com/install/,

Step 3:

docker build -t datascience .

Step 4:

Update the docker-compose.yml file to mount files you require from your host to the container following the example I have given already with the test.txt file.

Step 5:

docker-compose up 

DONE Now Visit http://localhost:8888, The default password is set as “root”. Feel free to change it.

Addendum

If you run into trouble run the following command to download the log file to your current directory. Post this as a response to this article and I will get back to with a solution as soon as possible.

docker cp <container-name>:/root/dockerLogs/install-logs.log .

If you want to start an interactive session inside the container type in the following command

docker exec -it <container name> /bin/bash

If you want delete the image completely from your computer

Step 1: Stop the container

docker container stop <container-id>

Step 2: Remove the container

docker container rm <container-id>

Step 3: Delete the image

docker image rm datascience

Thanks for reading !

Originally published by Chamal Gomes at towardsdatascience.com

How to building Python Data Science Container using Docker

How to building Python Data Science Container using Docker

Artificial Intelligence(AI) and Machine Learning(ML) are literally on fire these days. Powering a wide spectrum of use-cases ranging from self-driving cars to drug discovery and to God knows what. AI and ML have a bright and thriving future ahead of them.

On the other hand, Docker revolutionized the computing world through the introduction of ephemeral lightweight containers. Containers basically package all the software required to run inside an image(a bunch of read-only layers) with a COW(Copy On Write) layer to persist the data.

Python Data Science Packages

Our Python data science container makes use of the following super cool python packages:

  1. NumPy: NumPy or Numeric Python supports large, multi-dimensional arrays and matrices. It provides fast precompiled functions for mathematical and numerical routines. In addition, NumPy optimizes Python programming with powerful data structures for efficient computation of multi-dimensional arrays and matrices.

  2. SciPy: SciPy provides useful functions for regression, minimization, Fourier-transformation, and many more. Based on NumPy, SciPy extends its capabilities. SciPy’s main data structure is again a multidimensional array, implemented by Numpy. The package contains tools that help with solving linear algebra, probability theory, integral calculus, and many more tasks.

  3. Pandas: Pandas offer versatile and powerful tools for manipulating data structures and performing extensive data analysis. It works well with incomplete, unstructured, and unordered real-world data — and comes with tools for shaping, aggregating, analyzing, and visualizing datasets.

  4. SciKit-Learn: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. It is one of the best-known machine-learning libraries for python. The Scikit-learn package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. The primary emphasis is upon ease of use, performance, documentation, and API consistency. With minimal dependencies and easy distribution under the simplified BSD license, SciKit-Learn is widely used in academic and commercial settings. Scikit-learn exposes a concise and consistent interface to the common machine learning algorithms, making it simple to bring ML into production systems.

  5. Matplotlib: Matplotlib is a Python 2D plotting library, capable of producing publication quality figures in a wide variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, the Jupyter notebook, web application servers, and four graphical user interface toolkits.

  6. NLTK: NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning.

Building the Data Science Container

Python is fast becoming the go-to language for data scientists and for this reason we are going to use Python as the language of choice for building our data science container.

The Base Alpine Linux Image

Alpine Linux is a tiny Linux distribution designed for power users who appreciate security, simplicity and resource efficiency.

As claimed by Alpine:

Small. Simple. Secure. Alpine Linux is a security-oriented, lightweight Linux distribution based on musl libc and busybox.

The Alpine image is surprisingly tiny with a size of no more than 8MB for containers. With minimal packages installed to reduce the attack surface on the underlying container. This makes Alpine an image of choice for our data science container.

Downloading and Running an Alpine Linux container is as simple as:

$ docker container run --rm alpine:latest cat /etc/os-release

In our, Dockerfile we can simply use the Alpine base image as:

FROM alpine:latest

Talk is cheap let’s build the Dockerfile

Now let’s work our way through the Dockerfile.

The FROM directive is used to set alpine:latest as the base image. Using the WORKDIR directive we set the /var/www as the working directory for our container. The ENV PACKAGES lists the software packages required for our container like git, blas and libgfortran. The python packages for our data science container are defined in the ENV PACKAGES.

We have combined all the commands under a single Dockerfile RUN directive to reduce the number of layers which in turn helps in reducing the resultant image size.

Building and tagging the image

Now that we have our Dockerfile defined, navigate to the folder with the Dockerfile using the terminal and build the image using the following command:

$ docker build -t faizanbashir/python-datascience:2.7 -f Dockerfile .

The -t flag is used to name a tag in the 'name:tag' format. The -f tag is used to define the name of the Dockerfile (Default is 'PATH/Dockerfile').

Running the container

We have successfully built and tagged the docker image, now we can run the container using the following command:

$ docker container run --rm -it faizanbashir/python-datascience:2.7 python

Voila, we are greeted by the sight of a python shell ready to perform all kinds of cool data science stuff.

Python 2.7.15 (default, Aug 16 2018, 14:17:09) [GCC 6.4.0] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>>

Our container comes with Python 2.7, but don’t be sad if you wanna work with Python 3.6. Lo, behold the Dockerfile for Python 3.6:

Build and tag the image like so:

$ docker build -t faizanbashir/python-datascience:3.6 -f Dockerfile .

Run the container like so:

$ docker container run --rm -it faizanbashir/python-datascience:3.6 python

With this, you have a ready to use container for doing all kinds of cool data science stuff.

Serving Puddin’

Figures, you have the time and resources to set up all this stuff. In case you don’t, you can pull the existing images that I have already built and pushed to Docker’s registry Docker Hub using:

# For Python 2.7 pull
$ docker pull faizanbashir/python-datascience:2.7# For Python 3.6 pull
$ docker pull faizanbashir/python-datascience:3.6

After pulling the images you can use the image or extend the same in your Dockerfile file or use it as an image in your docker-compose or stack file.

Aftermath

The world of AI, ML is getting pretty exciting these days and will continue to become even more exciting. Big players are investing heavily in these domains. About time you start to harness the power of data, who knows it might lead to something wonderful.

You can check out the code here.

Python Face Recognition Tutorial

Python Face Recognition Tutorial

In this video we will be using the Python Face Recognition library to do a few things

7 DevOps Tools You Should Know In 2020

7 DevOps Tools You Should Know In 2020

DevOps culture is now an integral part of every tech-savvy business and plays a role in many business processes, ranging from project planning to software delivery.As cloud services are prevailing today, the requirement of related supplementary services is growing rapidly. DevOps technologies are increasing as well, so how one should choose the right tools to automate work? There are a lot of opinions.

There are a lot of tools that make DevOps possible, and it will be near impossible to cover them in one article. However, the 7 tools you’ll learn about in this article are some of the most popular and powerful DevOps tools.

1. Jenkins

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A lot of DevOps engineers call Jenkins the best CI/CD tool available in the market, since it’s incredibly useful. Jenkins is an automation server that is written in Java and is used to report changes, conduct live testing and distribute code across multiple machines. As Jenkins has a built-in GUI and over 1,000 plugins to support building and testing your application, it is considered a really powerful, yet easy to use tool. Thanks to these plugins, Jenkins integrates well with practically every other instrument in the continuous integration and continuous delivery toolchain.

  • Easy to install and a lot of support available from the community.

  • 1,000+ plugins are available and easy to create your own, if needed.

  • It can be used to publish results and send email notifications.

2- Terraform

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Terraform is an infrastructure-as-code tool that lets you build, change, and manage infrastructure properly. You can consider Terraform to be a provisioning tool. It helps you set up servers, databases, and other kinds of infrastructure that powers full-scale applications.

The code that manages infrastructure using Terraform is written in the Hashicorp Configuration Language (HCL). All of the configurations you need should be in this file as it will include dependencies to let the application run. HCL is declarative, such that you only need to specify the end state you desire, and Terraform will do the rest of the job.

Terraform is not restricted to any particular cloud service provider as it works with multiple cloud providers and environments. There are no issues with compatibility when using Terraform.
Cloud services providers such as AWS, Microsoft Azure, Google Cloud all integrate seamlessly with Terraform. Version Control System hosting providers such as Github and Bitbucket all also work fine with it.

There is an enterprise and open source version and Terraform can be installed on macOS, Linux and Windows systems.

3- Ansible

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Similar to Terraform, Ansible is also an infrastructure-as-code tool. Ansible is a tool that helps with the deployment of applications, the provisioning and configuration management of servers. Ansible is built in Python and maintained by RedHat. But it remains free and open source.

As a configuration management system, you can use Ansible to set up and build multiple servers. You get to install Ansible on a control machine, without requiring Ansible running on the other servers which can vary from web to app to database servers.

Unlike Terraform, Ansible doesn’t make use of HCL for its code. Instead, the configurations are written in Ansible playbooks which are YAML files. Ansible uses a hybrid of a declarative and procedural pattern. This is different from Terraform, which is solely declarative.

Since Ansible works on a control machine that administers others, it requires a mode of communicating with them. In this case, Ansible uses SSH. It pushes the modules to the other servers from the dominant server. Ansible is an agentless system, as it doesn’t require a deployment agent on the different machines.

Linux is the most suitable operating system for installing Ansible. However, it also works fine on macOS. For Windows users, it is possible to use Ansible through the bash shell from the Windows Subsystem for Linux.

4- Docker

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Docker is a software containerization platform that allows DevOps to build, ship, and run distributed processes within containers. This gives developers the ability to create predictable environments that are isolated from the rest of the applications and can be run anywhere. Containers are isolated but share the same OS kernel. This way you get to use hardware resources more efficiently compared to virtual machines.

Each container can hold a single process, like a web server or database management system. You can create a cluster of containers distributed across different nodes to have your application up and running in both load balancing and high availability modes. Containers can communicate on a private network, as you most likely want to keep some of your application parts private for security purposes. Simply expose your web server to the Internet and you are good to go.

What I like most is that you can install Docker on your computer to run containers locally to make some ad-hoc software tests without installing its dependencies globally. When you are done, you simply terminate your Docker container and your computer is as clean as new.

  • Build once, run anywhere! You can package an application from your laptop and run it unmodified on any public/private cloud or bare metal server.

  • Containers are lightweight and fast.

  • Docker Hub offers many official and community-built public Docker images.

  • Separating different components of a large application into containers have security benefits: if one container is compromised, others remain unaffected.

5- Kubernetes

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Kubernetes (K8s) is a Google open-source tool that lets you administer Docker containers. Since there are often a lot of containers running in production, Kubernetes makes it possible to orchestrate those containers.

It is, however, important to understand the reason to orchestrate Docker containers in the first place. When there are many containers running, it is hard to manually monitor these containers and have them communicating with each other. Asides, this scaling also becomes difficult as well as load balancing.

With Kubernetes, it is possible to bring all these containers under control so this cluster of machines can be administered as one machine. Often compared to Docker Compose, Kubernetes is different as it makes it easier to deploy, scale, and monitor the containers. When any of them crash, they can self-heal, and Kubernetes can spin up new ones as replacements. With K8s, it is possible to do storage orchestration, service discovery, and load balancing easily.

You can install Kubernetes on macOS, Linux, and Windows and use it through the Kubernetes command-line tool.

6- RabbitMQ

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RabbitMQ is a great messaging and queuing tool that you can use for applications that runs on most operating systems. Managing queues, exchanges and routing with it is a breeze. Even if you have an elaborate configuration to be built, it’s relatively easy to do so, since the tool is really well-documented. You can stream a lot of different high-performance processes and avoid system crashes through a friendly user interface. It's a durable and robust messaging broker that is worth your attention. As RabbitMQ developers like to say, it’s "messaging that just works."

  • Guaranteed message delivery.

  • Push work into background processes, freeing your web server up to handle more users.

  • Scale the most frequently used parts of your system, without having to scale everything.

  • Handling everything with ease even if it seems to be a huge crash.

7- Packer

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Packer is another DevOps tool from Hashicorp on the list. Written in Golang, Packer helps you automate the creation of virtual images. The process of manually building images can be frustrating as it is error-prone, but Packer eliminates all of that.

With a single JSON file, you can use Packer to create multiple images. So when it works the first time, there’s a guarantee that it will work the hundredth time since nothing interferes in the automation process. Many cloud service providers work with images, so you can seamlessly work with those providers since Packer standardizes the creation of images for the cloud environments.

Packer doesn’t work as a standalone tool. You can integrate it with Ansible, Chef, and Jenkins so the images can be used further down in the deployment pipeline. The installation process is not complicated, and you can learn to get started with the tool.

Conclusion

The concept of DevOps is can be very beneficial to getting large-scale applications to be performant under different kinds of load or traffic. It also makes the software deployment pipeline easy to manage.
However, the concepts DevOps are hard to implement without the availability of tools. There are many tools in this space and companies have varying choices.

Thanks for reading