DEV Community: Delia Ayoko

My AWS Bill Gave Me Heartburn😖

Delia Ayoko — Thu, 29 May 2025 16:43:34 +0000

How I learned to tame my machine learning costs, and how you can avoid my mistakes!

I still remember the pit in my stomach when I opened my first AWS bill after a month of just experimenting with SageMaker. $956. For a side project. My gadget budget for the month—gone.

Sounds familiar? Well, that makes two.
The hard truth is, AWS won’t stop you from accidentally spending your rent money on machine learning. But you can.

Here Come My Painful Lessons:

The 'Always On' Notebook: I often leave my SageMaker notebook running overnight. "I need to finish that experiment first before I can close that notebook". In reality, notebook instances bill per hour, even while you sleep. That ml.t3.medium costs ~$0.058/hr. Doesn’t sound like much? Leave it on 24/7 for a week: $9.74. Do nothing for a month? $41.76.
STOP your notebooks when you're done using them. To do this, go to AWS Console > Notebook Instances > Stop. Alternatively, you can use SageMaker Studio Lab (it's free!)
The K!ller Training Job: I picked the biggest GPU (I mean, who doesn't like fast?) for my tiny cat/dog model. Sorry to burst your bubble, but ml.p3.16xlarge (that fancy GPU) costs $24.48/hour. Training for 4 hours? $97.92. For a model you’ll tweak tomorrow? Ouch.
Start small. ml.m5.large = $0.115/hr will be okay, no matter how small you think it is. Use SageMaker Debugger to spot early failure. It'll save you hours.
The Zombie Endpoint: I deployed my model to test the API... and forgot about it. Hehe. You should know that endpoints charge per hour, including compute. A small ml.m5.large endpoint left running = ~$83/month. Yes. Per model.
DELETE your endpoints after testing them and thank yourself later. Also, you can make use of SageMaker Serverless Inference for sporadic traffic.
The S3 Black Hole: This one may not be relatable to many ML folks, but hello big data people! (smiles in don't say it).
I saved 10 versions of my 50GB dataset. "Just in case, you know...". There was no case. And no, I don't know anything. I never returned to it. What I didn't know was that S3 charges $0.023/GB/month. 500GB = $11.50/month. Glacier storage is cheaper, true, but the retrieval fees bite.
You can enable S3 Lifecycle Rules to auto-archive/delete your old datasets or models from S3, say if you don't use them after some period.

The Cost Survival Toolkit

Billing Alarms: Set an alarm for whatever hurts for you. If it's $50 (hello bestie!), call it $50 alarm. Also, you can open your AWS Console, open AWS Cost Management > Budgets > Create Budget, pick "Monthly Budget", set your amount, and add email/SMS alerts. Do this NOW and thank yourself tomorrow for a better sleep tonight.
Cost Explorer: I call this my spending detective. You can find it in AWS Cost Management. You can filter by Service (SageMaker, S3, EC2) and Usage Type.
Pro Tip: You can add tags to projects (e.g., Project=cat-classifier) to track your costs per experiment. You're welcome.
The Shutdown Ritual: Shutdown your notebook instances, delete your trial endpoints, terminate your unused training jobs, and empty your S3 buckets from your failed jobs. Do these EVERY TIME. It's not a ritual if you do it only sometimes. All of it can be a little hectic at first, but with time, it becomes your routine. Plus, bookmarking the 'special' consoles (Training Jobs, Notebooks) can also speed things up.

The Golden Rule

Never sign out of your AWS account without asking yourself: "Did I leave anything running?"

Now, go build amazing stuff—without the heartburn, of course.

Let's make money with Amazon Mechanical Turk!

Delia Ayoko — Fri, 12 Apr 2024 13:09:59 +0000

Emma does her assignment on her little study table on a random day after work. Then, acting really curious, she leaves the table and sits beside you while you knit.

Emma: "Last Friday, you talked about a group of people worldwide doing tasks."

You: "When was that?"

Emma: "We were detecting flowers. We had to label the images."

You: "Oh yeah. Amazon Mechanical Turk."

Emma: "I wanna know more about it. Sounds interesting."

You: "Okay. In simple terms, it's a platform provided by Amazon where you can give humans tasks to complete in exchange for money. So, they complete the tasks and they get paid."

Emma: "Woah! Means I can have my own money?" She beams.

You: "Not yet. Not for 5-year-olds."

Emma: (puts on a sad face). "But, how does it work?"

You: "Let's use our example of labeling images we had last week. We put the task out there for human workers to label the images. This task is called a Human Intelligence Task, HIT."

Emma: "And how do workers work on it?"

You: "They just find it on their profiles. They choose what tasks they can do, the rewards they get for achieving the task, and the requirements for working on it."

Emma: "Requirements?"

You: "Yes. Like location and some other conditions. Sometimes, education."

Emma: "That's interesting. Are the prices the same for every task?"

You: "Of course not! Not every HIT is the same, so they should have different prices attached. The same goes for the allocated time. Some tasks are tougher than others and so should have higher prices."

Emma: "Cool."

You: "And, when you sign up for an account, your account needs review. when you first open your account, you get 100 points. As you achieve tasks, your points increase. The number of points is sometimes required to complete the task."

Emma: "Are you an Amazon Mechanical Turk worker?"

You: "Yes. In my spare time, I complete tasks for some cash."

Emma: (she beams) "Woah! That's some cash you've got right there!"

You: "Oh yes, girl. Saving up to get you nice Christmas toys."

She dances. You smile.

Emma: "When I grow up, I'll be an Amazon Mechanical Turk Worker too!"

Who am I?

Hii! I'm Delia Ayoko. I'm an AWS Community Builder in the Machine Learning Category and the first Cloud Club Captain in Cameroon, at the University of Bamenda. I am also a computer engineering student, data scientist, content creator, Wattpad author, and mentor. I love building models, especially using AWS. If you loved this article, please react to it so that I write another one next week. Thank you!

P.S: If you're looking forward to starting a career in machine learning, I have videos where I explain ML concepts on my Instagram. Feel free to check it out and follow me there for more content in your feed! :)

Let's detect flowers! (with SageMaker and DeepLens)

Delia Ayoko — Fri, 05 Apr 2024 15:09:09 +0000

As an AWS-Machine-Learning-Oriented parent, you decide to something fun with your 5 year old Emma. You decide to open your list of "Wanna try" projects, and you pick Flower Detection.

Getting the data

You: "Emma, wanna detect flowers with me?"
Emma: "Yes! Detective Emma!"
giggles
Emma: "Let's use AWS!"
You: "Of course we will. First, let's set up an S3 bucket."
Emma: "What's that?"
You: "An S3 bucket. It's a container of objects. The objects are files and the bucket is just a folder. It is different from a normal bucket in that you can store as many things as you want in it. Here, I'm going to put in the flowers."
Emma: "But we don't have flowers!"
You: "Oh yes we do. But first, let's organize the bucket."

after organizing the data and storing in the S3 bucket...

Labeling the data

Emma: "What do we do now?"
You: "Let's label the data."
Emma: "Why?"
You: "We want to use a model to detect these images. So it important that the model learns from the labels we give the model for it not to suffer."
Emma: "Oh."
You: "Yeah. We'll use Amazon Mechanical Turk to label the images. It's a group of people Amazon pays to label data. They're all over the world. Only, you can't see them."
Emma: "Let's do it!"

6 hours later, when the labels are ready...

Model Training

Emma: "Our model is so dumb!"
You: "Exactly. That's why we're going to teach it."
Emma: "What if it doesn't learn?"
You: "We keep teaching. Until it learns."
Emma: "Strenuous."
You: "It is. But I can bet that you'll be proud of the result. Detectives don't give up."
Emma: "YES! Never!"
giggling
Emma: "What nutcracker are we using now?"
You: "Amazon SageMaker."
Emma: "Wow."
You: "It is a service used to train models like every other tool, but it is super fast, easy and has many 'toys'."
Emma: (eyes light up) "Toys?"
You: "Yeah, You can play with them to build a really nice model. In formal terms, they're called libraries. Ready-made stuff made available to use."
Emma: "That makes it easier."
You: "What do you think we're going to do to keep teaching our model?"
Emma: "Maybe adjust its settings?"
You: "Exactly. We call them parameters. If we're not happy with the results, we can fix these parameters and then teach the model again. This time, we expect the result to be better than the first time we thought."
Emma: "Understandable. It's just like grounding Alex for partying on Friday till 2 a.m. and telling him to come home earlier."
You: "You're smarter than I am. And yes, that is very true."

after training the model
Emma: "Voila!"
You: "Now, let's see the results."
Emma: "How?"
You: Using a set of metrics. They let you know how performant your model is. One very common metric we'll use to evaluate the model is accuracy."

You both check the score and to find 96%. While you celebrate in victory, Emma frowns.

You: "You're not happy?"
Emma: "I wanted a 100% score!"
You: (laughs) "I forgot to explain. A good model is never perfect. Any model with an accuracy score of 100% is not a good one."
Emma: "Huh?"
You: "If our model had an accuracy of 100%, it means it'll say that it detected a flower when it sees a dog."
Emma: "How's that?"
You: "It means it learnt too much from the data. In this case, it cannot differentiate between flowers and non-flowers. Any image you ask it to detect, it tells you flower, or hibiscus or dandelion when it saw either a kitten or a phone charger or even you. The same rule applies to life: no one is perfect."
Emma: "This is really interesting."
You: "A score between 80 and 99% is usually the trend for good models."

She smiles.

Model Integration

Emma: "How do we link our model to a device?"
You: "We use SDKs, a set of tools used to integrate models. Based on what we're integrating it into, we need to set up the communication between the model and the environment it'll sit in. If it sits in an environment where it know nobody, it won't communicate. Like the first day you went to kindergarten and refused to play with the other kids. You behaved like that because you didn't know any Olivia or Chloe."
Emma: (smiles shyly)

Model Deployment

Emma: "Now, I really want to see how much my model has learnt from my instructions. So that I can ground him too if he makes any silly mistakes."
You: (chuckles) "Yes. Let's use DeepLens."
Emma: "Let me guess. It's another service that uses a device's camera to detect images."
You: "Exactly. Except that it is a deep learning video camera."
Emma: "What?"
You: "A deep learning model is more powerful and can do complex instructions. If a video camera is deep learning based, it means it can perform more complex tasks than our flower model."
Emma: "Reason why it's called DeepLens.
You: (nods) "Yes."

After deploying the model, Emma decides to test it. You both go out into the garden.

You: "Here, take the iPad. Let's analyse the flowers here. Start the video."

As she roams around, she sees live predictions on the video screen and she says the names of the flower she sees. You both share a high five.

Who am I?

Hii! I'm Delia Ayoko. I'm an AWS Community Builder in the Machine Learning Category and the first Cloud Club Captain in Cameroon, at the University of Bamenda. I am also a computer engineering student, data scientist, content creator, Wattpad author and mentor. I love building models especially using AWS. If you loved this article, please react to it so that I write another one next week. Thank you!

There’s Serverless for Everyone!

Delia Ayoko — Fri, 28 Oct 2022 22:25:14 +0000

When you reunite your family for brunch one Saturday morning, you leave the kitchen with two plates of steak in both hands, shouting at the top of your lungs, "There's Serverless for Everyone!”

Your spouse is devouring whatever. Emma, on the other hand, can't help but stare into space.

"You're not eating anymore, Emma."

“What exactly is Serverless, Mom? How come we're having it for breakfast?”

AWS Lambda is an event-based compute service offered by Amazon Web Services that allows you to run code without controlling or provisioning servers. It was created to upload photos and objects to Amazon S3, update DynamoDB tables, respond to website clicks, as well as react to sensor readings on a smart device. AWS Lambda is essentially a service that runs code in response to events while also automatically managing the compute resources required by that code.

Serverless does not imply the absence of servers. Serverless means that if a server crashes at 3:54 am, it’s not your problem.

How Lambda Works

The Deployment: Deploying a function is as simple as configuring it in a container. In the event that the function is a package with dependencies, it must first be zipped and uploaded to an S3 bucket. AWS is the step's active character. So that the developer can concentrate on their code, AWS builds the container, configures it, and manages it.

AWS creates an instance, which is a container made from Amazon Linux AMIs (Amazon Machine Images) that has a configurable runtime of 1–900 seconds, a RAM range of 128MB–10240 MB, and temporary storage ranging from 512MB–10GB.

The Event: The function you write should be set to trigger an event. It might be a simple SMS or call, a fire alarm, or both, but it needs to cause stimuli.

You don't have access to any infrastructure resources that would let you take control of the cloud infrastructure because serverless computing helps to abstract it.
The Operations involved: Only when the event is triggered will your code run, and only during the time-span of the runtime will payments be made. This specific feature is advantageous for Lambda users who wish to use Lambda to provision back-end services prompted by HTTP requests (for example), as it preserves resources when not in use. As a result, since you only pay for the compute time you actually use when using Lambda, cost is almost never an issue.

In addition to being entirely event-driven, Lambda also conducts autonomous vertical scaling. Within milliseconds, it can scale up to many parallel resources. The /tmp folder contains temporal storage, which is only locally accessible for the duration of the instance. The scaling down process is also handled automatically, and active operations are discarded once they are complete.

AWS Lambda natively supports numerous programming languages and frameworks, and also offers customization options based on uploaded functions. It is used for serverless websites, log analysis, bulk real-time data processing, automated backups, back-end cleaning, and any other use case that calls for on-demand use.

In the following blog-post, Ryan Kauffman shows how to use Python, AWS IOT, AWS Lambda, and TwilioWireless to save a toddler's life.

“You still haven't explained what serverless is, mom.”

“Okay Emma. Serverless is a concept.”

“What concept?”

"Serverless is comparable to driving to the mall. Fuel is the only thing you used to arrive there.”

“Okay. That begins to make some sense.”

“There’s also this serverless quote: ‘Serverless also means that it's not your problem if a server crashes at two in the morning’. Let's use Bruno as an example. Bruno's duty is to watch after us when we sleep at night. Because Bruno is the one who should be chasing thieves away, it is not your responsibility if you wake up one night and notice one trying to sneak around this property. Bruno is Lambda because while we’re asleep, he takes care of us.”

Understanding Amazon RDS to explain to a 5-year-old.

Delia Ayoko — Wed, 26 Oct 2022 00:27:12 +0000

Amazon Relational Database Service is a distributed and managed SQL database service that simplifies the set-up, operation, and scaling of relational databases in the cloud. It uses features that consider hardware provisioning, database set-up, patching and backing up database software while being cost-effective, highly scalable, secure, compatible, and swift. Note that Amazon RDS itself is not a database: it is a service used to manage relational databases.

And hurrah! RDS is 13 today, being one of the Saas products of Amazon that functions across many operating systems.

By default, Amazon RDS has six database engines; Aurora, PostgreSQL, MySQL, MariaDB, Oracle, and MS-SQL Server. It makes it easy for the client to choose if they should stick to the default engines or use their preferred choice.

Features of RDS

Read Replicas: A read replica is a reflection of changes made to an original instance in real time. RDS uses this feature to create read-only models of database instances without changing the master production database. This is useful in performance metrics and monitoring because an instance of the database is created, which shows the state of that instance at the said time. Instances use the local, serial replication functionality of their respective database engines. According to this, the read replica will only be used by an Aurora instance if its master database was created using an Aurora database. Instances are helpful for read scaling even though they don't come with backups enabled by default. Read replicas do not support replication at the database level.
Multi-AZ Deployment: Multi-AZ stands for Multi-Availability Zone Deployment. Users can automatically maintain the physical standby replica no matter where they are thanks to Multi-AZ deployment. However, the database engine in use will determine the geographical location of the standby instance. The user is prompted to choose whether they would like to use a Multi-AZ instance when they create an RDS instance. This is beneficial since the standby instance performs a backup to avoid the scenario of halted input and output. In the second scenario, where there are numerous outages, Amazon RDS swiftly transitions to the latest standby instance, enabling operations to continue without interruption.
Multi-AZ Deployment aims at providing improved accessibility and data endurance for database engine instances. The instances of this feature have an associated cost with them.
Costs: RDS instances are billed hourly, just like Amazon EC2. They come in two packages: On-Demand DB Instances and Reserved DB Instances. On-Demand DB Instances are debited at a rolling hourly rate. Reserved DB Instances require a one-time upfront payment, which gives you a discount on the hourly usage charges for that instance.
In addition, users are taxed for the amount of storage supplied. RDS supports three types:
General purpose SSD (which is the default choice for Amazon), Provisioned IOPS (used for intensive workloads), and Magnetic storage (which is the lowest cost option). IOPS is the most expensive storage option because the user has to define how many input/output
operations per second are required by their application.
The good news for every new AWS client is that they can begin with managed database service in the cloud for free as part of the AWS Free Tier; either for the purpose of developing new applications, testing existing ones, or just gaining hands-on experience with RDS.
Backup: RDS provides automated instance backups with transaction logs for retrieval. This goes back to stating that computerized backups are performed in the standby instance, ensuring that the likelihood of any losses occurring is extremely low. Backup also allows users to manually take snapshots of instances, where the first snapshot of a database instance contains the data of the complete database instance. As long as they are not manually destroyed, these snapshots are preserved. Users can select a maximum retention period of 35 days during the backup process and restore databases at any moment within that time. RDS also gives users the option to define the length of the backup process and the retention period for backups and snapshots.
Monitoring and Performance metrics: The enhanced monitoring feature provides an expanded set of metrics for database engines that lets users view the capacity and input/output metrics from the AWS Management Console or the Amazon CloudWatch API.

Below is a visual demonstrating how features of RDS function.

When she overheard you discussing the Amazon Relational Database Service with a coworker, your five-year-old Emma became inquisitive and wondered what it was all about. Just remember that she has no understanding of what a relational database is. Since you would not want her to be perplexed, you must satisfy her curiosity. However, your kitchen has a fridge with a lot of contents in it. You inspire her to generate an odd mental image.

"There are several parts in the refrigerator that is in the kitchen. If a section must contain more than one kind of item, they are organized systematically, with one kind of vegetable on one side and the other kind on the other. The refrigerator is a relational database since it comprises compartments with things of various types that may or may not be related to one another. But as time goes on, your fridge becomes empty. You want to increase the number of things, but you do not want to reshuffle the sections you have created.

"Therefore, on Sunday, we go grocery shopping. We buy everything, including tomatoes, jam, carrots, spinach, Halloween pumpkins, eggs, and water. You organize each item so that it is in its proper location when we get home—carrots, jam, lettuce, and aubergines all in the appropriate portions. You become an Amazon RDS by doing that. When you take something out of the refrigerator, you take care of the contents. You make a mental note to get rid of the items that are rotting and add those that are rapidly getting exhausted. You are maintaining your refrigerator, keeping an eye on its contents, and determining which item is running low on supplies. You have changed to Amazon Relational Database Service from Emma."

Amazon Web Services

Delia Ayoko — Sun, 18 Sep 2022 21:47:52 +0000

Amazon Web Services Inc., is a subsidiary of Amazon aimed at providing on-demand cloud computing platforms and APIs to individuals, companies and governments on a pay-as-you-go basis. These services offered by Amazon provide distributed computing processing capacity and software tools through AWS server farms(An AWS server farm is a collection of AWS servers maintained by Amazon to supply server functionality far beyond the capability of a single machine). Amazon Elastic Compute Cloud(EC2), one of the most popular of these services, allows users to have a virtual group of computers available at their disposal through the Internet. These virtual computers are a simulation of most attributes of a real computer, like hardware CPUs and GPUs for processing; local and RAM memory; hard disk and SSD storage; a set of operating systems, networking and pre-loaded application software like web servers, databases and customer relationship management. AWS subscribers can pay for a single virtual AWS computer, a dedicated physical computer, or clusters of both.

Subscribers pay for hardware, OS, software or networking features as desired, regarding availability, redundancy, security and service options.

Amazon offers its services to its clients at a large scale computing capacity, and at a very affordable price. For this reason, AWS got 33% market share for cloud infrastructure as of 2021. These services comprise over 200 products, including storage, database, networking, analytics, application services, deployment, management, machine learning, mobile, developer tools, RobOps and IOT.

NFTs in the world of digital art

Delia Ayoko — Wed, 31 Aug 2022 14:52:59 +0000

An NFT(non-fungible token) is a a digital asset on a blockchain that allows for guaranteed proof of organization and ownership of music, videos, images and even text. In other words, it is a permanent tracking device that proves ownership of a digital file, or could be seen as a cryptographic token connected to a digital asset to preserve ownership rights in the metaverse.

Digital art is becoming something else.

On the 11th March 2021, Vignesh Sundaresan commonly known as Metakovan, an Indian cryptocurrency investor, bought Beeple's Everydays: The First 5000 Days (a collection of his digital art) for $69 million in cryptocurrency. It was the first NFT sold at auction, the most expensive NFT ever sold and one of the 20 most expensive works of art ever sold at auction. The auction, artwork and currency were all virtual, but their transaction was real.

Crypto entrepreneur Sina Estavi bought Twitter founder Jack Dorsey's first-ever tweet just setting up my twttr as an NFT for $2.9 million last year. Now, it is worth only about $14,000 or less, as a result of the reflection of a maturing NFT market.

NFTs are not only lucrative, but also represent the strength in the relationships we have with the public.

Usually, an NFT starts from a digital file. The creator of an NFT adds another layer of code on it and that code registers that file on a blockchain. The value of that NFT is found in its uniqueness, and although anybody can buy it, it does not change the fact that the original owner still remains the primary owner of his/her art.

An artwork is a digital file, and to protect it and capture the value of your labor in perpetuity, you go to a digital goods marketplace like OpenSea, and "mint it"(register it as a token for a fee between $70 and $300 on blockchain). After minting, it will contain your signature, your time stamp, and any rules around its resale. After minting, it becomes ready for sale. Connect with your community and let them know that it has dropped; meaning that it's for sale. This has to do with creating a strong bond with your clients, which is advantageous because there is no middleman or intermediary between you and your audience. Usually, clients are attracted to art that have a high chance to be sold at an even higher price in the future. Your client purchases your NFT when he is convinced enough, and stores it in his digital wallet.

The underlying decentralized technology that enables digital currencies is blockchain. Blockchain prevents digital money from being duplicated or hacked, and it removes the need for a centralized authority(such as a bank) to validate transactions. The record of ownership of an NFT is kept in a public database on the Internet that is maintained by millions of computers that keep track of all content origination and transactions.

NFTs in futurism

In the metaverse, humans are represented using avatars, and you can buy clothes for your avatar as well. Fashion companies have started diving into the metaverse. For example, Gucci sells sneakers that only exist in the virtual world, and you can never wear them; but your avatar can. Alternatively, you could use augmented reality to overlay virtual sneakers onto your real feet. Gucci has gone as far as offering land in The SandBox, a gaming platform. Nike bought virtual sneaker designer RTFKT (pronounced "artifact") on the 13th of December 2021. Now, over $11 million has been spent on its first virtual sneaker set.

It has been said that online dress up games have used its users to dress up their models for them to sell out the clothes as NFTs for use in the metaverse. A lot of people and companies have seen the future from Zuckerberg's point of view and have started amassing sounds, images, videos and even text for use in the future. Statistics show that at a particular point, in a world of evolving technology, humans will no longer be able to live without experiencing a digital world. So, companies have started piling up a huge amount of art to sell at very high rates during those periods.

Not only have NFTs provided a way for people to interact in a virtual world, but it serves as a huge source of income to artists, who in the past were considered as a set of people who gave out joy to others using their imaginations, with almost nothing in return. And because of NFTs, anyone can create digital fashion to be bought, sold and worn in games, online platforms, and even in avatar fashion shows and musical concerts.

The Metaverse - Above and beyond the Universe

Delia Ayoko — Mon, 29 Aug 2022 15:14:00 +0000

The metaverse is a speculative replica of the Internet that can be viewed as a virtual universe facilitated by the use of virtual reality and augmented reality. An interconnection of three-dimensional virtual worlds focused on companionship, is a metaverse.

The term emerged from the 1992 novel Snow Crash that showed characteristics of futurism and science-fiction. Components of the metaverse technology have long been developed within video games and are slowly creeping into e-commerce, e-learning and other aspects that concern the real world. Examples of metaverse simulations include the eUniverse 1999 LivePlace social interaction platform, and the 2003 virtual world platform Second Life, both of which are often described as the first metaverse, containing many aspects of the 3D social media.

Dockerizing your Docker with Docker

Delia Ayoko — Fri, 05 Aug 2022 13:24:00 +0000

Docker was founded by Solomon Hykes in 2010 and was launched in 2011, using the Go programming language. The reason behind its creation was due to the fact that developers kept asking for the underlying technology powering the DotCloud platform, now known as Docker Inc.

What is Docker?

The Oxford dictionary explains the word "docker" as a person employed in a port to load and unload ships. Note that according to Oxford, a docker is a person that can perform many activities; loading and unloading.

That said, Docker is a set of Paas(Platform as a Service) products that uses OS-level virtualization to deliver software in packages called containers. In simpler terms, Docker is a software platform that simplifies the process of building, running, managing and distributing applications.

Docker Operations

Containers bundled up by docker are hosted by a software called the docker engine. Docker can bundle an application and its dependencies in a virtual container that can be executed on a Linux, Windows or macOS computer. The application can then run in a variety of locations like on-premises(like IBM private cloud), in public cloud(like AWS) or private cloud(like OpenStack). Docker uses the resource isolation features of the Linux kernel when running on Linux, and a union-capable file system like OverlayFS to permit containers run within a single Linux instance, avoiding the expenses of starting and maintaining virtual machines. On macOS, Docker uses a Linux virtual machine to run these containers. A single virtual machine can run several containers simultaneously, due to weightlessness of Docker containers.
Docker implements a high-level API(the Docker Engine API) to provide lightweight containers that run processes in isolation.

Docker consists of three components:

Software: Docker software can be split into the daemon and the client program.
The Docker daemon, called "dockerd" is a continuous process that handles Docker containers and controls container objects. It listens for requests sent through the Docker API. The "docker" which is the client program, provides users with a command line that permits them to interact with daemons.
Objects: They are units used to assemble an application in Docker. Its main classes are images, containers and services.
- A Docker image is a read-only template used to build containers, to store and to ship applications.
- A Docker container is a standardized encapsulated environment that runs applications. It is managed using the Docker API or CLI.
- A Docker service is a group of containers of the same image that enables the scaling of applications. It allows containers to be sealed across multiple daemons, which results in a swarm.
Registries: A Docker registry can be visualized as a repository for Docker images, where clients connect to pull images for use, or to push the ones they have built. Like Github repos, Docker registries can either be public or private. Docker Hub and Docker Cloud are two main public registries on Docker. Registries also allow for the creation of notifications based on events.

Summarily,

Imagine your 5 year old Emma saw you reading this article and asked you to explain what Docker is. What would you say? That Docker is a software used to ship containers? Of course not. What if she didn't know what a software was? Notice the puzzle on her face when you tell her that. You have to explain it like a real life situation.

Last week Thursday, I was so busy that I wish I'd passed out. I had to cook breakfast for you and your dad, cleaned the dishes, dropped you at school and arrived work very late. After my seminars at work, I went shopping and picked you up after school. I cooked lunch and I went to visit a colleague who was not feeling very well. I came back, helped you out with your assignments and cooked dinner. After dinner, I wrote a report for my boss the next day. Then I read you a bedtime story and tucked you in bed. Funniest thing was, I slept for only two hours the night before. Now, if I gave you a red magic cloak to tie around your neck for you to help me with all those things while I laid back with a cup of coffee behind my desk, I would call you Super Emma. That's what Docker is.

Amazon Elastic Compute Cloud(EC2)

Delia Ayoko — Mon, 13 Jun 2022 04:01:19 +0000

Amazon Elastic Compute Cloud (EC2) is a virtual private server that allows users to rent virtual computers on which to run their own computer applications, and provides scalable computing capacity in the AWS cloud. EC2 encourages scalable development and deployment of applications by providing a web service through which a user can boot an AMI (Amazon Machine Image) to configure a virtual machine called an instance. As the term elastic explains, this instance permits a user to create, launch and terminate server instances as needed. Active servers pay for each second.

EC2, like S3 buckets, provides users with control over the geographical location of instances that allows for latency optimization and high levels of redundancy.

Instance Types

An EC2 instance is a virtual server in EC2 for running applications on the AWS infrastructure. These instances are created from Amazon Machine Images(AMI), where the images are like templates. They are configured with an operating system and other software which determine the user's operating environment. EC2 instances are classified into categories based on target application profiles.

General purpose instances: It is a virtual machine designated to handle a variety of workloads. Common use cases are webserver hosting and software development and testing.
Compute optimized instances: These instances are designed to run computational and data-intensive applications that require fast network performance, extensive availabilty and high I/O operations per second (IOPS). Examples of such instances include scientific and financial modeling and simulation, machine learning enterprise data warehousing and business intelligence.
Graphics Processing Unit(GPU) instances: They provide a way of running graphic-intensive software faster than with main EC2 instances. Systems that rely on GPUs include gaming and design work.
Memory Optimized instances: They use a high speed, solid state drive to provide ultra-fast access to data and deliver high performance, and are ideal for software that require more memory and less CPU power.
Storage optimized instances: Ideal instances for applications that require high I/O performance, and we are well suited for memory-intensive applications and are well suited for memory-intensive applications like data processing, analytics, workloads and log processing.
Micro instances: A micro instance is used for applications with low throughput. It may serve as a small database server, a platform for software testing or a web server that does not necessitate high rates of transaction.

Amazon charges about 0.0058 dollars per hour for the smallest nano instance virtual instance running the Linux or Windows OS. The EC2 price varies from $2.5 per month for nano instance with one vCPU and 0.5GB RAM on board to x large type of instances with 32 vCPU and 488GB RAM for $3997.19 per month.

TechTwitter Boosts:- Rick

Delia Ayoko — Thu, 26 May 2022 18:16:55 +0000

Hi. My name’s Rick.

I’m an iOS software Developer with an unusual background. I went to prison at 18. I was a senior in high school. I was involved in a street fight and stabbed a man who later died at the hospital. It was a horrible decision by a foolish teenager.

I thought I was protecting my friends, but the truth is, I was simply a fool acting irrationally in the heat of the moment. I wish I could undo that night but I can’t. I haven’t been in trouble since but obviously that fateful decision has shaped my life.

In prison I discovered that I liked programming. It kinda save me by giving me a life and career I probably couldn’t have in many other industries. Since I was hired as a developer, I’ve been teaching others from similar backgrounds in hopes that it does the same for them.

Out of that interest grew a passion project turned non-profit called Underdog Devs that helps the formerly incarcerated become software engineers. For many getting out, there are obstacles that make reintegration difficult.

With a skill and a potential career, the odds of acclimating back to the society as a productive member are greatly increased. If you are interested in helping us through mentoring/pair programming, financially sponsoring mentees, or some other form of support, reach out. If you are looking for skilled diverse developers, reach out.

We’ve done the vetting and training process for you. These developers are gritty, smart, and overlooked. Web, iOS and Android. There is no placement fee. We do ask that you consider sponsoring the next cohort though.

Amazon S3 Buckets

Delia Ayoko — Thu, 26 May 2022 18:14:54 +0000

An Amazon S3 bucket is a scalable, high speed web-based resource available on Amazon Web Services(AWS). S3 stands for Simple Storage Service and it is designed for online backup and archiving data and applications on AWS. It is built to store, protect and retrieve data from “buckets” at any time, from anywhere, on any device. It is just like a file folder and stores objects which consists of data and its descriptive metadata.

How to use an S3 bucket

An Amazon s3 client initially creates a bucket in the AWS region of his/her choice and gives it a globally unique name. AWS usually advises clients to choose regions with closer proximity in order to reduce latency and costs. After creating the bucket, the user selects a tier of the data, alongside other S3 tiers with different levels of redundancy, prices and accessibility. This means that one S3 bucket can store objects from different S3 storage tiers. After that, the user can specify access privileges for the objects stored in a bucket through AWS IAM(Identity and Access Management) bucket policies and access control lists. A client can interact with an S3 bucket via the AWS Management Console, the AWS Command Line Interface or through APIs.

S3 bucket features

AWS offers several features for S3 buckets like versioning, set-up server access logs, object-level API logs, tags and encryption and transfer acceleration.

S3 bucket limits and pricing

There is no limit to the amount of objects a client can store in a bucket(although buckets cannot exist in others). No matter how many buckets an individual creates, S3 performance remains the same. Each AWS account can create 100 buckets but an unlimited number of objects, and the account that creates a bucket is the admin of that bucket, and administration is not transferable. An S3 customer can delete a bucket but another user can claim that unique name. To request an increase in number of buckets above 100, visit the service Quotas Console.

AWS charges customers for storing objects in a bucket and for the movement of objects in and out of buckets. The pricing varies by region.