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Amazon Rekognition Custom Labels Chest X-ray Prediction Model Test Results

As a senior in secondary school in Nigeria, I wanted to become a medical doctor — we all know how this turned out. To be fair, I got into pre-medical school, but realized in the second year that I was not designed to cut through the human body. …


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Ever since the introduction of VARs (Video Assistant Referees) to football, many have expressed their doubts towards the objectives the initiative was supposed to fulfill. When introduced at the last FIFA World Cup in Moscow 2018, a number of football greats including Diego Maradona and Javier Zanetti expressed the improvements the technology would bring to the game they love. According to the FIFA website,


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One thing COVID-19 forced on everyone is digital transformation, largely by disrupting existing assumptions that humans needed to gather together in specific locations to create value. This assumption has been nullified by the use of technology platforms such as Zoom, BlueJeans, Google Meet, Instagram Live, Loom, YouTube and many more for holding meetings, conferences, seminars, lectures, and thesis defense.

At the heart of digital transformation, in 2020, is AI (Artificial Intelligence). According to AI expert Kai-Fu Lee says, “Implementation is what makes academic advances meaningful and what will truly end up changing the fabric of our daily lives.” AI is really transforming our lives from personalization to robotics to AV (autonomous vehicles). …


Four months ago, I wrote about maintaining hand hygiene with sanitizer bracelets, and asked people to pre-order. Today, after weeks of iterating over several designs, 3D prototypes, and injection mold designs, I am glad to announce that we the first version of SanitizerWristbands.

http://0f2cb4b26ab2.ngrok.io/

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SanitizerWristbands GT1 — SanitizerWristbands.com

With these our designs available, our online store is now live and accepting orders. And we start shipping and delivery to customers worldwide from August 4, 2020. Why wait, you may ask. We have already delivered samples for our initial customers for further feedback which for us has been super massive. …


CD4AutoML Cloudfromation Registry and CLI — Developer Preview
CD4AutoML Cloudfromation Registry and CLI — Developer Preview

Last month I published a tutorial on automating end-to-end AutoML lifecycle with Amazon SageMaker Autopilot and Amazon Step Functions including code sample on GitHub and the architecture.

CD4AutoML with Amazon Cloudformation

Today, I would like to introduce you to my newest project, CD4AutoML via Amazon Cloudformation for deploying managed end-to-end automated workflows for AutoML. CD4AutoML Cloudformation third-party resource type is now available in public beta — developer preview.

CD4AutoML, AWS Cloudformation and IaC

According to Gartner’s Magic Quadrant for Cloud AI Developer Services report

By 2023, 40% of development teams will be using automated machine learning services to build models that add AI capabilities to their applications, up from 2% in 2019. …


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CD4AutoML with Amazon SageMaker Autopilot — Olalekan Elesin

In my previous posts (linked below), I wrote about automating machine learning workflows on Amazon Web Services (AWS) with Amazon SageMaker and Amazon Step Functions. In those posts, I only provided GitHub Gists and minor code snippets but no fully working solutions. This left a lot of readers asking a lot of questions on the technical solutions either privately or via comments. I kinda solved a problem, but created more problems. This led to me ask myself:

How might I help my readers better achieve the job they wanted done anytime they employed my technical blog posts?

Answer is what you’re now reading. In this, I provide a working project on deploy automate an end-to-end AutoML lifecycle with Amazon SageMaker Autopilot and Amazon Step Functions, which I now call CD4AutoML. With end-to-end, I am not referring to calling the Amazon SageMaker Endpoint from a notebook. I am talking about having a publicly available serverless REST API (Amazon API Gateway) connected to your Amazon SageMaker Endpoint in a fully automated way. This means that you can serve predictions in your applications with a fully automated workflow requiring no developer input apart from committing code to GitHub. …


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This is going to be a really short post on what I learned in the past one week. For the past one week, I had been working on a mobile application to help women differentiate between synthetic and human hair extensions using artificial intelligence. Basically, operationalizing my image classification model trained on Amazon SageMaker. I will write about the details of the mobile application in a later post, however let me give you a hint of the technologies in the mix:


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Hair retailers that sell high volume of hair extensions must ensure that they hair types and quality are not compromised when fulfilling customer orders. This is often accomplished with human workers as sales agents which is often times error prone, and might not be the best use of the creativity of their sales persons. This task of identifying hair quality or type is accomplished based on experience of hair retailers looking at the hair extension strands. This is a classic case for computer vision.

To enable hair retailers focus on growing business, one can build an application that uses a custom image classification model to detect and notify a retailer the quality and type of an extension, at the point he/she is buying in bulk from the vendor. This provides a powerful, scalable, and simple solution for quality control. In this tutorial, we will make use of Amazon SageMaker to train and deploy a hair quality control machine learning model. In a later tutorial, we will automate this machine learning pipeline with Amazon Step Functions. …


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On weekdays, I have a day job that pays my rent (hopefully buying a house soon), internet, and occasional visits to nice restaurants (pre-social distancing period). However, on weekends I spend time speaking to people who want to get into data and AI world as well as figuring out ways to improve my engineering work — sometimes for other engineers as well.

Ever since the beta release of Amazon EMR 6.0.0-beta, I had always wanted to simplify my work. This weekend, everything changed, the first major release of Amazon EMR 6.0.0 was announced and my Eureka moment happened. Ever since I started deploying Spark jobs on Amazon EMR, my goal had always been to write my ETL jobs in self-contained environments without thinking about networking details on my AWS Cloud environment. One could argue that Amazon Glue ETL jobs provides this, but I argue otherwise. This is because in experience with Amazon Glue jobs, I have to properly package external libraries as zipped files, save to Amazon S3 and specify the S3 path when executing my Amazon Glue Job. …


How product managers should think of data science/AI initiatives — Olalekan Elesin
How product managers should think of data science/AI initiatives — Olalekan Elesin
How product managers should think of data science/AI initiatives — Olalekan Elesin

In the course of my career from a Software Engineer, Data Science Engineer, Data Landscape Engineer, Technical Product Manager, and now Product Manager, Data Science, I have built a series of failed and very successful data science projects. When I talk about successful, I mean huge business impact as measured by bottomline. See a few of my portfolio projects below:

Based on these, I can share with you what works and does not work — although I am yet to write about the one(s) that failed. As a product manager, many a time, you are informed that your company is adopting or rather developing an AI Strategy or something close. However, you wonder to yourself how this translates to improving lives for your users or its impact on your next product feature. Please be aware you are not alone. Many a product manager struggle with this as well. …

About

Olalekan Elesin

Product Person, AWS Certified SA, Associate. Lover of scientific experiments and love to get my hands dirty with code…

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