DEV Community: Simbo Asante Ntiro

How do you do ML on AWS?

Simbo Asante Ntiro — Tue, 02 Aug 2022 09:23:00 +0000

In the last decade, there has been a huge leap forward in Artificial Intelligence and Machine Learning mainly because of the massive increase in data being produced today by web/mobile applications and IOT devices on the internet, the great improvement in ML algorithms, and the availability of the Cloud's scalable compute resources for training and deploying models.

I'll be telling you about managed services on Amazon Web Services that don't require AI/ML expertise and services you can use to run custom models as well.

What is Machine Learning anyways?

Machine learning can be described as methods that leverage data to improve performance on a set of tasks. It's a really important part of Artificial Intelligence.
Machine learning behind services like Alexa, Google Maps' commute estimation, and even your FYP on Tik-Tok!
So its a really huge part of our modern lives.

Managed services on AWS

If you are interested in AI/ML and don't know much about it you can use Amazon Web Service's managed services such as:

Amazon Textract
This service automatically extracts printed text, handwriting, and other data from scanned documents.
Amazon Translate
As the name implies, Amazon Translate is a text translation service that uses ML to provide high-quality translation on demand. It can be used to translate unstructured text or build apps that work in many languages.
Amazon Comprehend
This service uses something called Natural Language Processing (NLP) to find insights and relationships in text. It can be used for things like Sentiment Analysis.
Amazon Personalize
This is a Machine Learning service that helps create individual recommendations for customers. It can really improve customer engagement through product recommendations and even tailored search results.
Amazon Polly
This service turns text into lifelike speech, making it possible to create applications that talk. This is called Text-to-Speech (TTS). This is great for improving accessibility of an app. Unfortunately it only supports 8 languages (at the time of
posting).
AWS Rekognition
This is a service that makes it easy to add video and image analysis to applications. It uses proven highly scalable deep learning technology developed by Amazon's computer vision scientists and requires no machine learning expertise to use.
By using Rekognition Custom Models you can identify objects and scenes in images that are specific to your business needs. For example, you can build a model to detect unhealthy plants.

Build your own models on AWS

You can use AWS's large amount of compute and GPU's to train your custom models or you can use Sagemaker.

Amazon Sagemaker

Amazon Sagemaker can be used to train, then directly deploy ML models to a production ready environment. It has very many features one of which is:

SageMaker Studio
If you have played around with Data Science and Machine Learning you are very familiar with Jupyter Notebooks and Google Colab, well SageMaker has you covered! You can; prepare data for machine learning ,build and train machine learning models, deploy the models and monitor the performance of their predictions and even track and debug the machine learning experiments!
SageMaker Training Compiler
This service allows you to create clusters if GPU containers that can be used to train & tune your models. It can also help you automate your Deep Learning Workflows.

After you build and train your models, Amazon SageMaker also makes it easy for you to deploy them for predictions using SageMaker hosting services.

You can programmatically deploy a model using an AWS SDK,You can programmatically deploy a model using an AWS SDK or even through the SageMaker console. You can deploy a model with a Python wrapper file and get back a HTTP endpoint for your inference.

Fully Managed ML Services Vs Building Your Own Model

There are clear advantages of using managed services over the non-managed ones.
As is the norm when dealing with cloud services the difference between Fully Managed Services and Bespoke solutions usually comes down to a Cost-Performance trade-off. Usually a Managed Service will cost more upfront but has no Operational Overhead, and the opposite would be true for a Bespoke solution. But if you have no experience with ML or have a very small scale application the best option would be to use AWS' managed services for ML.

Cheers! And happy tinkering!

Web3 vs Web2? What does that even mean!?

Simbo Asante Ntiro — Sat, 05 Mar 2022 17:38:42 +0000

Everyone is talking about Web2 vs Web3 these days. But what do they mean by Web2 and Web3? I hope this article will help you understand the basic differences between the two.

Web2 is a term that describes the internet we know and use today. It allows us to dynamically interact with websites on the internet. An example of a web2 app would be YouTube.

The fundamental difference between web2 and web3 technologies is their architecture.

Web2 architecture consists of;

A place to store essential data (user credentials, likes, comments, etc)
A backend to define business logic.
A frontend to display what the app/website looks like and what happens when a user interacts with elements of the app

Web3 architecture is very different because it;

Uses smart contracts for backend logic
Uses blockchain to build apps on a decentralized state machine (eg. EVM) that is maintained by nodes on the network.

By looking at the difference between Web2’s & Web3’s architecture we can see that the main issue here is centralization vs decentralization.

In Centralized Systems participation in the network is controlled by the central authority but in Decentralized Systems ideally, anyone can participate in the network.

In the past few years, we have seen issues with web2 companies harvesting search histories, email topics, likes, political and religious affiliations, sexual orientation, relationship status, health information, purchase histories of all sorts, and selling this data to marketing and advertising firms as part of their business models. Owning the user is part of the web2 “ethos”.

Essentially, Web3 can be regarded as the next-generation internet, where everything that could be done in Web2 can still be done but in a decentralized way meaning
participants can interact without permission from a trusted intermediary or governing body.
no central authority can control content and impose indiscriminate censorship

Web3 technologies are hoping to shift the power away from big tech corporations to individual users.

The Nigerian startup Nestcoin is helping bring web3 technologies to Africa by building web3 apps and helping the ecosystem grow by training new blockchain developers in collaboration with ZuriTeam and Blockgames. You can check out their youtube here.

Another resource I REALLY like (that is also one of Nestcoin’s products) is the Breach blog which you can check out here.

Getting Started With Python

Simbo Asante Ntiro — Thu, 10 Feb 2022 10:38:35 +0000

Introduction:

Python is a general purpose, high level, interpreted language with easy syntax. It was developed in 1989 by Guido Van Rossum, it is named after the British comedy group Monty Python (I'm also a big fan of their movies and sketches). In the past decade Python has seen an exponential increase in its popularity among developers.

Why is Python so popular?

The biggest reason is its easy-to-learn syntax. Python also boasts a great number of use cases including but not limited to:

AI and Machine Learning
Web Development
Data Visualization
Game Development
Data Analytics
Financial-Technology
Cyber Security
Automation
Testing

Getting Started

Download the latest version of Python for your operating system here. You can also read more about setting up a python development here
Create a new folder where you will be saving you python files, let say FirstApp.
Open your text-editor, then navigate to the folder you created (FirstApp).
Create a new file called app.py then enter the following code into the file:

print("Hello, World!")

Save the file.

That was easy! Now for the REAL magic.

Using Command Prompt on Windows or Terminal on UNIX (macOS or Linux) Navigate to the FirstApp folder and type the following command

Python3 app.py

If everything went according to plan, you'll see the following message on screen:

Hello, World!

There you go! You just wrote your first program in Python!
I hope you can learn more soon!