About Me:

🔭 Enthusiastically immersed in the realms of Digital Transformation, Hybrid Cloud Computing, Red Hat, DevSecOps - SRE, Product Management, Artificial Intelligence, Data Science, Analytics, Deep Learning, Machine Learning, Computer Vision, and Natural Language Processing, with a keen eye on Cyber Security.

💭 My personal mission revolves around crafting AI-based solutions that ingeniously address high-impact global challenges, thereby simplifying the tapestry of daily life.

👯 Passionately inclined towards mentoring, coaching, and synergizing with fellow visionaries in the expansive domain of Digital Transformation, AI, and Product Management. If your idea resonates with my passion areas, don't hesitate to drop me an email!

👉🏼 Join me on LinkedIn to stay abreast of my latest AI and Digital Transformation ventures.

Bio 🧠:

Continuously channeling innovation to propel the frontiers of AI and Pattern Recognition, my focus encompasses predictive analysis, mathematical modeling, and paving pioneering paths.

⌛️ Presently, a proud contributor to IANCON.net's Digital Transformation through DevOps Strategies and AI Machine Intelligence Neural Design (AI-MIND) team. My expertise lies in architecting AI models spanning Natural Language Processing, Speech Recognition, and Computer Vision applications.

🌱 Currently, advancing my graduate studies on a part-time basis, specializing in Artificial Intelligence, as part of my Computer Science journey at the University.

🎓 A stellar alumnus of NED University, where I majored in Electrical and Computer Engineering with a Computers specialization, graduating with the prestigious Outstanding Graduate Student Award in 2007.

📚 A beacon of distinction within NED University, one of the globe's largest institutions of learning.

Personal Profile 🌎:

Watch this space for my forthcoming personal insights.

AI Portfolio 💼:

My GitHub repository is a treasure trove of meticulously curated course notes, learning resources, and tools dedicated to Artificial Intelligence and Deep Learning. Also, catch me hosting invigorating podcasts and contributing insightful blogs on https://www.engineerability.com | https://podcast.engineerability.com

Feel free to Connect with me via email: [email protected]

Led the implementation of a Red Hat OpenShift-based service orchestration platform for a leading telecom provider. Reduced service provisioning time by 40% through automated containerization and microservices architecture. Achieved 99.9% uptime by leveraging OpenShift's self-healing capabilities, ensuring uninterrupted customer connectivity. Scalability improvements resulted in handling a 300% increase in concurrent user requests during peak hours.

Spearheaded the development of a real-time data analytics solution on Red Hat OpenShift for optimizing drilling operations. Reduced drilling downtime by 25% through predictive maintenance models powered by containerized data pipelines. Achieved 50% cost savings by scaling up and down the infrastructure dynamically based on workload demand. Improved decision-making with instant insights, resulting in a 15% increase in drilling efficiency.

Managed the transition of a legacy monolithic banking application to a modern microservices architecture on Red Hat OpenShift. Reduced time-to-market for new features by 30% through continuous integration and deployment pipelines. Achieved 99.95% transaction success rate and improved system availability using OpenShift's built-in failover and load balancing. Scalability enhancements enabled handling a 200% increase in daily transactions during promotional campaigns.

Directed the containerization of a legacy logistics management system using Red Hat OpenShift for a global shipping company. Reduced deployment time by 60% through automated container orchestration and rolling updates. Achieved 98% order accuracy by optimizing routing algorithms through real-time data processing. Scalability improvements accommodated a 150% increase in shipping volume during holiday seasons.

Orchestrated the development of an integrated property management platform using Red Hat OpenShift for a prominent real estate developer. Reduced time-to-market for new property listings by 40% through streamlined application deployment and updates. Achieved 95% tenant satisfaction by enabling self-service portals for maintenance requests and lease renewals. Scalability enhancements facilitated managing a portfolio expansion of 30% without compromising performance.

Spearheaded the migration of a legacy learning management system to a cloud-native architecture using Red Hat OpenShift for a major university. Reduced server provisioning time from days to minutes through automated container scaling and orchestration. Improved student engagement by 25% with real-time collaboration features enabled by containerized microservices. Scalability improvements supported concurrent access by 10,000 users during peak exam periods.

(Dr. Andrew Ng is a globally recognized leader in AI (Artificial Intelligence). He is Founder of DeepLearning.AI, Founder & CEO of Landing AI, General Partner at AI Fund, Chairman and Co-Founder of Coursera and an Adjunct Professor at Stanford University’s Computer Science Department.)

Build Basic Generative Adversarial Networks (GANs) Build Better Generative Adversarial Networks (GANs) Apply Generative Adversarial Networks (GANs)

Understand GAN components, build basic GANs using PyTorch and advanced DCGANs using convolutional layers, control your GAN and build conditional GAN Compare generative models, use FID method to assess GAN fidelity and diversity, learn to detect bias in GAN, and implement StyleGAN techniques Use GANs for data augmentation and privacy preservation, survey GANs applications, and examine and build Pix2Pix and CycleGAN for image translation

Natural Language Processing with Classification and Vector Spaces Natural Language Processing with Probabilistic Models Natural Language Processing with Sequence Models Natural Language Processing with Attention Models

Use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies & translate words. Use dynamic programming, hidden Markov models, and word embeddings to implement autocorrect, autocomplete & identify part-of-speech tags for words. Use recurrent neural networks, LSTMs, GRUs & Siamese networks in Trax for sentiment analysis, text generation & named entity recognition. Use encoder-decoder, causal, & self-attention to machine translate complete sentences, summarize text, build chatbots & question-answering.

Structuring Machine Learning Projects Sequence Models Convolutional Neural Networks Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization Neural Networks and Deep Learning

Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering

AI for Medical Diagnosis AI for Medical Prognosis AI For Medical Treatment

Diagnose diseases from x-rays and 3D MRI brain images Predict patient survival rates more accurately using tree-based models Estimate treatment effects on patients using data from randomized trials Automate the task of labeling medical datasets using natural language processing

Introduction to Machine Learning in Production Machine Learning Data Lifecycle in Production Machine Learning Modeling Pipelines in Production Deploying Machine Learning Models in Production

Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements. Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application. Build data pipelines by gathering, cleaning, and validating datasets. Establish data lifecycle by using data lineage and provenance metadata tools. Apply best practices and progressive delivery techniques to maintain and monitor a continuously operating production system.

Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning Convolutional Neural Networks in TensorFlow Natural Language Processing in TensorFlow Sequences, Time Series and Prediction

Best practices for TensorFlow, a popular open-source machine learning framework to train a neural network for a computer vision applications. Handle real-world image data and explore strategies to prevent overfitting, including augmentation and dropout. Build natural language processing systems using TensorFlow. Apply RNNs, GRUs, and LSTMs as you train them using text repositories.

Browser-based Models with TensorFlow.js Device-based Models with TensorFlow Lite Data Pipelines with TensorFlow Data Services Advanced Deployment Scenarios with TensorFlow

Run models in your browser using TensorFlow.js Prepare and deploy models on mobile devices using TensorFlow Lite Access, organize, and process training data more easily using TensorFlow Data Services Explore four advanced deployment scenarios using TensorFlow Serving, TensorFlow Hub, and TensorBoard

Custom Models, Layers, and Loss Functions with TensorFlow Custom and Distributed Training with TensorFlow Advanced Computer Vision with TensorFlow Generative Deep Learning with TensorFlow

Understand the underlying basis of the Functional API and build exotic non-sequential model types, custom loss functions, and layers. Learn optimization and how to use GradientTape & Autograph, optimize training in different environments with multiple processors and chip types. Practice object detection, image segmentation, and visual interpretation of convolutions. Explore generative deep learning, and how AIs can create new content, from Style Transfer through Auto Encoding and VAEs to GANs.

Linear Algebra for Machine Learning and Data Science Calculus for Machine Learning and Data Science Probability & Statistics for Machine Learning & Data Science

A deep understanding of the math that makes machine learning algorithms work. Statistical techniques that empower you to get more out of your data analysis. Fundamental skills that employers desire, helping you ace machine learning interview questions and land your dream job.