Hello! I am Anass, and I’m passionate about the Cloud with over 3+ years of experience in the field. I have created numerous labs on the cloud and have a strong enthusiasm for networking. For a closer look at my work and projects, please visit my website.
Services : Microsoft Azure, Caching, OpenAI, Shadcn, TailwindCSS
I designed and managed the deployment of a high-availability web application leveraging AWS infrastructure. This involved using VPC for network isolation, EC2 for scalable compute resources, and RDS for reliable database services. I utilized Route 53 for DNS management, ASG for automated scaling, and S3 for backup solutions. Lambda was...
I deployed an application using Docker and Kubernetes on Linode as the cloud provider. I began by containerizing the application with Docker, creating a Docker image that encapsulated the application's environment and dependencies. This image was then pushed to a container registry. With Linode as the cloud infrastructure, I set...
Successfully deployed a Nextcloud server on an Azure Virtual Machine, demonstrating proficiency in Azure's core architecture. This project involved creating and configuring a Virtual Network with subnets, implementing Network Security Groups to enforce security policies, and using Azure Bastion for secure SSH access. Additionally, i installed and configured the Nextcloud...
Designing a serverless web backend on AWS Designing a serverless data analytics solution on AWS Designing a hybrid solution for container based workloads on AWS Designing a solution following account governance and management best practices
During the Defi IA 2022 with MeteoNet and INSA Toulouse, I utilized MeteoNet data and my expertise in Python to predict daily precipitation. I preprocessed and cleaned data to make it usable, and created a recurrent neural network (RNN) to forecast future precipitation. I adapted the model to the data format for accurate future predictions, leveraging my skills in artificial intelligence (AI), statistical data analysis, and predictive modeling.
During my internship at the Institut de Mathématiques de Toulouse, I analyzed Covid-19 data and collected and preprocessed data from various countries. I developed a regression model and a statistical SIRD (Suspected, Infected, Recovered, Death) model using the implicit Euler method to predict health states. I also used a neural network to predict mortality and recovery rates, leveraging my skills in Keras, Python, recurrent neural networks, crisis management, TensorFlow, mortality analysis, and epidemiology.
I forecasted the values of 14 major cryptocurrencies by developing predictive models from scratch. I utilized Python for data retrieval and preprocessing and created recurrent neural networks (RNNs) to predict cryptocurrency values. Leveraging TensorFlow and Keras, I developed innovative prediction methods and tested them to minimize errors. I validated these methods on real-time data and evaluated results over time, applying skills in Keras, Python, predictive modeling, artificial intelligence (AI), big data, TensorFlow, deep learning, and Apache Spark.
I have tackled intricate mathematical challenges, explored functional analysis and spectral theory, investigated number theory and geometric algebra, conducted descriptive and complex analysis, and studied probability theory and distribution patterns.
I possess expertise in a wide range of applied mathematics, from scientific computing to statistics, with the ability to solve complex engineering and industrial problems using mathematical models and methods. I am proficient in data analysis and big data tools, skilled in creating and optimizing neural networks, and have strong capabilities in probabilities, statistics, and sampling. Additionally, I excel in solving optimization and operational research problems.
