Forem: Fawaz Siddiqi

What's next in the world of technology? Let's talk about all the hype around distributed cloud!

Fawaz Siddiqi — Fri, 28 Jan 2022 15:19:55 +0000

We’ve heard about so many cloud models over the past decade, and we have learned to use them efficiently. Around 7/10 organizations have adopted cloud technologies, especially during the pandemic to support different types of workloads, and utilized hybrid cloud and multi-cloud deployment models, but what is lacking is the consistency between environments.

Let’s talk about some of the issues that organizations face when adopting public cloud technologies. Firstly, data residency, organizations want to keep the data within the country or at least on-premises to ensure that the data falls under the right data regulations per the country they are operating in. Secondly, latency, real-time workloads require low latency connections that would allow them to process the data, and the workloads have to be closer to the data itself, a primary example here would be running applications on the edge. Irrespective of these issues, other issues need focus, such as the inability to instantly customize environments and applications, and the lack of visibility, management, and agility.

So, what is distributed cloud? How is it going to transform the world of cloud computing?

As you have heard about distributed computing, where applications are scaled across different computers networks together to improve performance and overall efficiency.

Similarly, the distributed cloud has a similar aspect, it is a public cloud computing service that allows you to run public cloud infrastructure in multiple and different locations, may that be your own data center, but even on your cloud provider's infrastructure. The idea here is to expand public cloud services to various locations, allowing you to manage everything from a single control plane.

As distributed cloud services can run on your infrastructure, it also allows you to run cloud applications on the edge, as Gartner stated that by 2025, they predict that 75% of enterprise-generated data will be created and processed outside the traditional data center or cloud.

Moreover, it also helps you overcome the issues highlighted earlier regarding latency and data residency including the inability to instantly customize environments and applications, and the lack of visibility, management, and agility. As a result, distributed cloud resolves the operational and management inconsistencies that occur in hybrid cloud and multi-cloud environments.

Once again, in simple terms, the distributed cloud is you extending public cloud services into your infrastructure or on top of your existing cloud provider’s infrastructure.

You can also check out this really interesting video talking all about distributed cloud!

How does distributed cloud work from a responsibility point of view?

As you are aware by now, that distributed cloud leaps into providing a public cloud provider’s services and computing stack to wherever a customer might need it, may that be on-premises or off-premises in one or more public cloud data centers that may or may not belong to the cloud provider and even on the edge.

The cloud provider takes responsibility for the operations, updates, governance, security, and reliability of the distributed infrastructure. The customer focuses on their infrastructure, where the public cloud services are being extended, which may be virtual machines, disaster recovery, hypervisors, and much more, in other words, when we are talking about distributed cloud, we are looking towards a shared responsibility model.

A primary example here would be the operating systems that we use today, we are holding the responsibility for the things that we do on the operating system, whereas the operating system provider is responsible for providing security, updates, and reliability.

So, what does IBM provide when it comes to distributed cloud services?

IBM Cloud Satellite helps you deploy and run applications across all on-premises, edge computing, and public cloud environments from any cloud vendor. It standardizes a core set of Kubernetes, data, AI, and security services to be centrally managed as a service by IBM Cloud, with full visibility across all environments through a single pane of glass. The result is greater developer productivity and development velocity.

With IBM Cloud Satellite, you are in full control of your data, and you can also deploy Red Hat OpenShift on IBM Cloud which would further allow you to expand your application modernization and deployment. With Red Hat OpenShift on IBM Cloud, you can deploy IBM Cloud Paks which are AI-powered software that can help organizations build, modernize, and manage applications securely.

Check out this cool video about an introduction to IBM Cloud Satellite!

And check out these cool use cases explaining how IBM Cloud Satellite fit into your environment!

Want to talk more about distributed cloud use cases and other IBM Cloud services?

You can reach out to me on LinkedIn & Twitter

Your Path to Deep Learning

Fawaz Siddiqi — Thu, 02 Sep 2021 07:12:06 +0000

The majority of data in the world is unlabeled and unstructured, for instance, images, sound, and text data. Shallow neural networks cannot easily capture relevant structures within this type of data, but deep networks are capable of discovering the hidden structures. With the help of various frameworks we can build neural networks that allow us to demystify various use cases based on various industries and predict outcomes, thus they have now become an integral part of our day to day lives.

As a follow-up to our Your Path to AI series which was conducted in 2020, we are now conducting Your Path to Deep Learning. A series which is mainly focused on the fundamentals of deep learning and with this series you will get an understanding of deep learning concepts, deep learning architectures, a comparison of deep learning frameworks and you will build various deep learning models throughout the series for linear and logistic regression, recurrent neural networks and Restricted Boltzmann Machines.

This series consists of 4 workshops that really dive deep into the essentials and working of various types of neural networks.

Workshop 1: User Reviews Sentiment Analysis using Logistic Regression

In this first workshop of the Your Path to Deep Learning series, we talk about how to use Natural language processing to classify user reviews from the IMDB Dataset. Developers can leverage user reviews for a given product or service from social media sites and customer reviews, and use Deep Learning models & identify patterns to predict sentiments, or other statistics to be used for business decisions. In this workshop, the user reviews will be classified using Logistic Regression to predict whether the review is positive or negative.

Workshop 1 Resources

Workshop 2: Identify Handwritten Digits using Convolutional Neural Networks with TensorFlow

Recognising handwritten numbers is a piece of cake for humans, but it’s a non-trivial task for machines. Currently, however, with the advancement of machine learning, people have made machines more capable of performing this task.

In this second workshop of the Your Path to Deep Learning series, we will used the MNIST Dataset to build two Neural Networks capable to perform handwritten digits classification. The first Network is a simple Multi-layer Perceptron (MLP) and the second one is a Convolutional Neural Network (CNN). We will show you the step by step guide to create a simple handwritten digit recognizer in Watson Studio with TensorFlow.

Workshop 2 Resources

Workshop 3: Language Processing using Recurrent Neural Networks with TensorFlow

Language modelling is the task of assigning probabilities to sequences of words, and is one of the most important tasks in natural language processing. Given the context of one word or a sequence of words in the language that the language model was trained on, the model should provide the next most probable words or sequence of words that follows from the given sequence of words in the sentence.

In this third workshop of the Your Path to Deep Learning series, we will talk about how to perform language modeling on the Penn Treebank data set by creating a Recurrent Neural Network using long short-term memory (LSTM) units in a Jupyter notebook.

Workshop 3 Resources

Workshop 4: Personalized Recommendation Engines with TensorFlow

Recommendation engines are an integral part of our lives and touch us with nearly every digital service we use these days. They allow users to gain a personalised experience based on their behaviour and are used by most popular media & e-commerce platforms.

In this fourth and last workshop of the Your Path to Deep Learning series, you will learn how to build a Restricted Boltzmann Machine using TensorFlow that will give you recommendations based on movies that have been watched. The data sets used in the workshops are from GroupLens, and contain movies, users, and movie ratings.

You'll learn to use a sigmoid activation function for the neural network, and the recommendations returned are based on the recommendation score generated by a Restricted Boltzmann Machine (RBM) using Collaborative Filtering.

Workshop 4 Resources

For us, we really enjoyed conducting this series as we got to interact with different people within the industry having different viewpoints.

Did I forget to mention? You can also get a badge and a certificate for the series!

And to get started sign up or login to IBM Cloud at https://ibm.biz/YourPathToDeepLearning

Workshops included in the series

23rd August - 6PM-8PM (GST)
- User Reviews Sentiment Analysis using Logistic Regression
- https://www.crowdcast.io/e/ypdl-1
25th August - 6PM-8PM (GST)
- Identify Handwritten Digits using Convolutional Neural Networks with TensorFlow
- https://www.crowdcast.io/e/ypdl-2
30th August - 6PM-8PM (GST)
- Language Processing using Recurrent Neural Networks with TensorFlow
- https://www.crowdcast.io/e/ypdl-3
1st September - 6PM-8PM (GST)
- Personalized Recommendation Engines with TensorFlow
- https://www.crowdcast.io/e/ypdl-4

You can access all the workshop resources here: https://github.com/IBMDeveloperMEA/YourPathToDeepLearning/

Check out our partners for the series!

Linkedin: University of Sharjah, Arab Women in Computing, Phi Science Institute, Exordium Technologies, Business Innovation SARL AU, Prince Sultan University, Bayan Data
Facebook: University of Sharjah,Arab Women in Computing, Phi Science Institute, Prince Sultan University
Twitter: Arab Women in Computing, Bayan Data, Prince Sultan University
Instagram: University of Sharjah, Arab Women in Computing, Phi Science Institute, Exordium Technologies, Bayan Data, Prince Sultan University

Build a predictive machine learning model quickly and easily

Fawaz Siddiqi — Sun, 01 Aug 2021 08:56:15 +0000

Organizations are always looking forward to accelerate their Data Science pipeline, but have a major obstacle that occurs is towards the skills gap as well as the time taken to implement tasks that could be automated.

With SPSS Modeler, data scientists can accelerate tasks in a way that it's following a certain pipeline in a way that their solution follows the entire data science methodology, from attaching the data set to cleaning and filtering the data and finally creating machine learning model, and the possibilities are limitless.

With Modeler Flows you can visually create pipelines for your data science & AI needs, and recently Anam Mahmood and I conducted a workshop on how you can easily build a predictive model that can detect loan risk for an applicant.

If you're looking forward to explore more, do check out the workshop at https://www.crowdcast.io/e/build-a-predictive-2

For all the workshop resources:

Login/Sign Up for IBM Cloud: https://ibm.biz/EasyModels
Hands-On Guide: https://ibm.biz/EasyModels-Workshop
Slides: https://ibm.biz/EasyModels-Slides
Workshop Replay: https://www.crowdcast.io/e/build-a-predictive-2

Data Science & AI for Everyone

Fawaz Siddiqi — Thu, 17 Jun 2021 09:15:25 +0000

As a part of the Digital Developer Conference: Data & AI (https://ibm.biz/devcon-ai) our team conducted a regional event Data Science & AI for Everyone.

Data Science & AI for Everyone was targeted for all types of developer audience, from beginners to advanced and consisted of various sessions including Machine Learning, Deep Learning and No Code tools as well. Moreover, we were joined by some of our amazing guest speakers who gave an overview of the practical use cases of various technologies & algorithms.

Our first workshop was regarding Predicting Fraud using Automated Machine Learning

Automation and artificial intelligence (AI) are transforming businesses and will contribute to economic growth via contributions to productivity. They will also help address challenges in areas of healthcare, technology & other areas. At the same time, these technologies will transform the nature of work and the workplace itself. In this code pattern, we will focus on building state-of-the-art systems for churning out predictions that can be used in different scenarios. We will try to predict fraudulent transactions which we know can reduce monetary loss and risk mitigation. The same approach can be used for predicting customer churn, demand and supply forecast, and others. Building predictive models require time, effort, and good knowledge of algorithms to create effective systems which can predict the outcome accurately. With that being said, IBM has introduced Auto AI which will automate all the tasks involved in building predictive models for different requirements. We will get to see how Auto AI can churn out great models quickly which will save time and effort and aid in a faster decision-making process.

It was conducted by Sbusiso Mkhombe - Developer Advocate, IBM & Khalil Faraj - Developer Advocate, IBM

In the next session we had one of our amazing guest speakers who talked about Speech Synthesis by using Advanced Machine Learning Techniques for Easy Readability of Dyslexic Children

Now a days, around 60% to 70% of children are facing problem of dyslexia while reading at the age of 7-10 years. There are advanced methods of Generative Adversarial Network which can be applied for speech recognition and support dyslexic children.

It was conducted by Geeta Atkar - Assistant Professor in G H Raisoni College of Engineering and Management

After which we commenced to our 2nd workshop which was about Building a recurrent neural network using TensorFlow Keras

In this workshop, we will learn how to perform language modeling on the Penn Treebank data set by creating an RNN using the long short-term memory (LSTM) unit. The notebook runs on IBM Cloud Pak® for Data as a Service on IBM Cloud®. The IBM Cloud Pak for Data platform provides additional support, such as integration with multiple data sources, built-in analytics, Jupyter Notebooks, and machine learning. It also offers scalability by distributing processes across multiple computing resources.

It was conducted by Mridul Bhandari - Developer Advocate, IBM,
Anam Mahmood - Developer Advocate, IBM, and by one of our guest speakers from OSCA (Open Source Community Africa) and also an IBM Champion
Emeka Boris Ama - Lead Data Scientist, Law Pavilion

Our next session was about climate change and was also a submission for Call for Code which is a yearly challenge based on climate change. The session covered Early forest fire detection via Machine Learning

Forest fires have caused a lot of damage in terms of wildlife as well as climate change, this talk will present the ongoing progress in deployment of image classification algorithms based on neural networks, using the IBM Cloud, for applications in early forest fire detection.

The session was covered by our guest speaker Graciana Puentes - Independent Senior Researcher at National Research Council / Group Leader at University of Buenos Aires

After a short 5 minute break, we had a very industry based use case on AI, the session was about Branch Specific AI-Based Target Management

Setting goals across multiple branches in a financial services organization can be a challenge. Almost everyone is familiar with the scenario in which annual targets are driven primarily by a percentage increase over the prior year’s performance. Consider an alternative, in which goals are uniformly or proportionally allocated across all branches within an organization. For managers that operate a busy branch and dynamic market, that tends to make life easier; whereas branches in highly stable markets are challenged to keep meeting their targets.

We designed a data-driven predictive analytics approach that incorporates historical performance data, branch characteristics, detailed demographic data, information about competitor locations, and more.

The session was conducted by our guest speakers Yılmaz Meral - Business Analytics Team Leader
Serdar Öztürk - Business Analytics Team Leader from AIMS

Networking Sessions

Our final session was a networking session where we covered various topics such as:

Your Career in Data Science which was covered by Qamar un Nisa - Lead Developer Advocate, IBM
Industry Focused Practical uses of AI/ML which was covered by Fawaz Siddiqi - Developer Advocate, IBM
Ethics in AI : Build trusted and explainable AI models which was covered by Naiyarah Hussain - Lead Developer Advocate, IBM
Deep Learning which was covered by Anam Mahmood - Developer Advocate, IBM

Interested in earning a badge on the skills which you have learned? Check out the on-demand sessions and course at the Digital Developer Conference: Data & AI - https://ibm.biz/devcon-ai where you can do a short course and gain a badge :D

Check out the on-demand sessions for Digital Developer Conference: Data & AI: https://ibm.biz/devcon-ai

In case you missed the regional event check out all the resources here: https://github.com/IBMDeveloperMEA/MEA-Regional-Event-Data-AI

You can watch the replay of the session here: https://www.crowdcast.io/e/ddc-mea2021

To get started, login/sign up for IBM Cloud: https://ibm.biz/MEA-DataAI

Interested in using Tech for Good? Putting your ideas to action to fight climate change? Join Call for Code, which is a yearly challenge by IBM, where you can join a large community of developers developing various solutions!

Read more about Call for Code here: https://developer.ibm.com/callforcode/

Know your Water Quality using Node Red and API's

Fawaz Siddiqi — Thu, 06 May 2021 12:16:35 +0000

Water is an essential resource for all of us, and it is considered to be the most threatened by climate change. Moreover when it comes to various other countries, clean water and its sanitation is a major issue.

Watch this video by charity: water - The Spring - The charity: water story which really explains the issue in-depth and how many people are affected by this.

According to WHO (World Health Organization) 2.2B people have no access to safely managed drinking water, 4.2B people do not have safely managed sanitation, and 3B people lack basic hand washing facilities! Which is a big number considering the fact that we are in a pandemic and with such issues, there is a chance of it increasing or even mutating to other variants at the same time, which really increases the problem rather than resolving it and the efforts to resolve it would be diminished as well.

There are some amazing organizations which are looking at the problem of clear water and sanitation, as we mentioned charity: water is one of them, but there's also UN Water which is the United Nations subsidiary actively looking into the water crisis.

Recently, IBM launched Call for Code, it is a global challenge which IBM conducts every year with various themes, this years theme is focusing on three major problem areas and clean water and sanitation is one of them.

Recently, Asna Javed (Lead Developer Advocate, IBM Pakistan) and I (Developer Advocate, IBM UAE) conducted a workshop on Building a Water Quality Dashboard with GeoJSON and Node-RED, where we used GeoJSON data provided by USGS NLDI API returns a GeoJSON set of latitude / longitude coordinates and features that can be plotted on a map The NLDI API is provided by the Open Water Data Initiative and is being developed as an open source project.

We also got water toxicity reports and values using the Aquagenuity Developer API. The Aquagenuity GetWaterScore API provides water quality reports for many USA Zip Codes. Sign up for a free Aquagenuity Developer API key. Select the free plan and create an account. You will receive an email welcoming you to the Aquagenuity Developer Access Program. The email will contain your username and password credentials.

All both of the API's were pushing information to the Node-Red instance which was running on IBM Cloud and you can also try to Install Node-RED on your system or in IBM Cloud. You can create and deploy these flows and dashboards to IBM Cloud by creating a Node-RED Starter application.

To get started, you can login/sign up for IBM Cloud here

Also, you can find the flow which we imported into the Node-Red instance here https://github.com/IBMDeveloperMEA/CFC-Water-Ridgewood.

As a final output, you will see a dashboard by going to

https://<your node-red url>/ui

and if the payload is sent properly you should see something like this

In case you missed the workshop and need the resources and replays, please find them below

Workshop Resources

Login/Sign Up for IBM Cloud: https://ibm.biz/NodeRedCleanWater
Hands-On Guide: https://ibm.biz/NRCleanWater-Guide
Slides: https://fawazsiddiqi.github.io/CFC-Water-Ridgewood
Workshop Replay: https://www.crowdcast.io/e/water-quality/

If you have any suggestions or queries, feel free to reach out to me, cheers!

Stay safe and keep others safe!

Deploy an Easy Apple CareKit iOS App for Healthcare with a HIPAA-ready back end

Fawaz Siddiqi — Sun, 02 May 2021 12:17:57 +0000

Recently, Elton de Souza (Chief Architect, IBM) and I (Developer Advocate, IBM) conducted a webinar on Deploying an Easy Apple CareKit iOS App for Healthcare with a HIPAA-ready back end.

We covered a lot of points in this presentation, which includes the topic of confidential computing, what is overall security and how do policies play a major role when it comes to the topic of security where we majorly focused on Health Insurance Portability and Accountability Act (HIPAA) and its implications on healthcare.

The application Elton demonstrated was an iOS application which has been created on top of Apple CareKit and to further extend the functionality and connectivity there is an Hyper Protect SDK for CareKit which allows us to connect to Hyper Protect Services on IBM Cloud

Wondering what is Hyper Protect?
It is basically IBM's confidential computing services which are provided and are focused mainly on security and efficiency.

There is a variety of Hyper Protect services which are available on IBM Cloud which you can sign up and login by clicking here

It was a very interesting webinar and I being new to the topic of confidential computing really enjoyed presenting the tech-talk with Elton.

In case you are wondering where you can get started:

Find the recording of the webinar here: https://www.crowdcast.io/e/healthcare-app

Slides: https://fawazsiddiqi.github.io/carekit-hyperprotect-lab

Sign up for IBM Cloud at: https://ibm.biz/HyperProtectHIPAA

Looking to use #TechforGood? Join a community of problem solvers to fight back some of the world’s most pressing challenges and win up to $200K!

Answer the call on → http://callforcode.org