Aakash Kunchwar

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Hello !

I am Aakash Kunchwar.

Bridging Technical Expertise and Exemplary Client Service for Business Growth.

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About

IT Professional Looking for A New Opportunity: Application Support, Problem-Solving, Python, Cloud Services, and More.

It all started in the year 2015 when I was leaving the pharmacy field to enter the tech world. I knew that the foundation of science, problem-solving, and attention to detail from a pharmacy background would be highly valued. Upon entering the tech world, I chose to explore financial services and logistics industries from years 2018 to year 2020, offering IT Solutions using expertise gained from diverse backgrounds in science, technology, finance, and logistics. I have faith in teamwork, stepwise actions, passion, and humility as fundamental values. Every choice I make is influenced by these values and guarantees exemplary service for our clients.

  • Web Apps
  • Database Apps
  • Desktop Apps
  • Mobile Apps
  • Cloud Apps
  • No Code Apps
  • Low Code Apps
  • AI, MI, Blockchain, IoT, AR, and VR Apps

Portfolio

I enjoy my work and would like you to see some of my most recent projects, case studies, and labs.

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Securing and Optimizing Cloud Environments with IBM Cloud Object Storage

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  • Problem: Lack of clear IAM and ineffective monitoring complicates the ability to monitor unauthorized access to object storage, resulting in potential cost or security problems.
  • My Role: As a Cloud Engineer, I was responsible for deploying an application to Cloud using a serverless architecture.
  • Understanding Serverless Needs: Analyzing and using serverless cloud functions for scalability and reducing operational costs.
  • Creating Systems that are Scalable: Making sure that the application can manage both anticipated and unforeseen increases in workload.
  • Integrating Cloud Resources: Facilitating smooth data transfer, improving efficiency, and allowing applications to utilize all cloud functionalities using self-service portal and APIs.
  • Deployment Automation: Coordinating the seamless, consistent deployment of code modifications and infrastructure upgrades in a serverless setting.
  • Best Practices for Security: Implementing IAM best practices, encryption where needed, and vulnerability scanning to maintain cloud security, compliance, and customization.
  • Results: The serverless deployment on IBM Cloud not only achieved the project's original objectives but also surpassed expectations in scalability, cost reduction, and security. The organization is now well positioned for continuous growth and innovation with the resilient, high-performing application in operation.
  • Technologies Used: IBM Cloud Object Storage, Restful APIs, Identity and Access Management Encryption, Firewall, Kubernetes, Terraform, Cloudformation, and IaaS/PaaS/SaaS.
  • Skills: Cloud Computing, Hybrid Multicloud, Devops, Iaas PaaS Saas, and Cloud Native.

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Data Analysis GUI Application to Visualize and Quantify with Java and Python

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  • Problem: The lack of seamless Java-Python integration for data analysis hinders efficient GUI visualization and delays valuable insights, impeding informed decision-making.
  • My Role: In my project as a Data Analyst, I was responsible for creating a Java GUI that allows users to retrieve data from a specified file and generate graphs using a Python application.
  • Central Hub for Data Interaction: This graphical user interface makes it simple for users to select data files and initiate analysis.
  • Seamless Integration: I developed a strong communication link between the Java GUI and Python backend. This enabled the smooth transfer of data and the running of Python scripts for tasks such as data processing, statistical analysis, and visualization.
  • Interactive Visualization: I utilized Python's robust data visualization libraries like Matplotlib and Seaborn to create detailed and customizable charts directly within the Java GUI. This removed the necessity for users to change tools, speeding up the analysis procedure.
  • Results: The GUI application successfully improved user experience, made analysis more efficient, and ultimately led to better decision-making by connecting the two programming languages.
  • Technologies Used: Java JFrame Class, Java Button Event Handlers, Python IDLE, and Apache Netbeans.
  • Skills: Java GUI Development, Data Science, Python Programming, Java, and Swing (Java).

Cloud-Based Docker Management with Amazon Web Services (AWS) Linux Server Command Line Interface for Containerization and Deployment of Applications

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  • Problem: Conventional deployment techniques can be slow and prone to mistakes compared to Docker's efficient deployment and management features, enabling you to package code and all its dependencies in a standardized unit container and deploy applications with greater efficiency.
  • My Role: As a DevOps Engineer, I streamlined and enhanced the processes of software development and deployment by launching an AWS EC2 Linux server, installing Docker in the server, and managing docker images and container in the cloud remotely.
  • Consistent Dependencies Needs: Dependencies are contained within the container, preventing conflicts, and ensuring consistent performance of your application.
  • Building Images using Dockerfile for Reproducibility and Automation: Simplifying configuration is made easier by reproducing consistently across different environments, managing application environment variables and settings in a single file, eliminating the “it works on my machine” problem.
  • Portability, Collaboration and Standardization: Dockerfile is portable can be used to build and run the application on different platforms, such as development machines, testing environments, and production servers.
  • Results: Implementing Docker on AWS EC2 Linux server changed how we deployed software, leading to quicker, more dependable, and more economical deployments. We managed to release software updates more often and with more assurance, ultimately enhancing the end-user experience.
  • Technologies Used: AWS EC2 Linux Server, Docker, Dockerfile, Linux Command Line Interface Commands, and SSH.
  • Skills: Docker Images, Docker Containers, AWS Integration, Command Line Tools, Docker Networking, Docker Storage, and Docker Security.

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Prediction of Baby Weight with TensorFlow Machine Learning on Artificial Intelligence Platform

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  • Problem: Accurate prediction of baby weight is crucial for early detection of pregnancy complications. Machine learning, particularly with TensorFlow on AI platform, offers a promising approach to analyze data and improve predictions, enabling timely interventions and personalized care.
  • My Role: I was responsible for creating and implementing a machine learning model using TensorFlow on Google Cloud AI Platform to accurately predict baby weight, which assists healthcare providers, scientists, and parents in making informed choices regarding pregnancy and newborn care.
  • Cloud Storage Management: Google Cloud Storage (GCS) bucket was created to securely store the dataset used for training and evaluation, as well as the trained machine learning model. It was better managed ensuring proper access controls and data organization within the GCS bucket for deployment of the machine learning model.
  • Vertex AI Workbench Setup: Vertex AI Workbench instance provides a managed JupyterLab environment for model development and experimentation. This environment offers a convenient and collaborative platform for writing code, exploring data, and building machine learning models.
  • Code Repository Management, Training, Evaluation, Deployment & Prediction: Code repository was obtained containing the TensorFlow model and scripts to understand code structure and workflow. Model was further trained using the provided dataset, evaluated for its performance, deployed on AI for making accurate predictions on new data, monitored, and retrained as needed.
  • Results: I successfully implemented a machine learning model using TensorFlow on Google Cloud AI Platform to accurately predict baby weight. This has the potential to contribute to early detection of pregnancy complications and enable personalized care for newborns.
  • Technologies Used: TensorFlow, Google Cloud AI Platform, Google Cloud Storage (GCS), Vertex AI Workbench, JupyterLab Environment, and Python.
  • Skills: Machine Learning, Google Cloud Platform, and AI Platform.

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Implementation of DevOps at Acme Company

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  • Client: Acme Company
  • Summary of Case Study: Acme's goal was to implement DevOps methodologies in order to optimize the process of software development and distribution.
  • Problem: Collaboration was impeded and processes were delayed due to a disconnected strategy involving distinct Dev and Ops groups, as well as a dependency on a ticketing system.
  • Solution: 1. Cultural Transformation - Moving from separate teams to a cooperative DevOps environment. 2. Communication - Enable direct communication between development and operations teams members Charles, Miguel, Nancy and Jim, skipping the ticketing system for immediate requests using Slack or Teams. 3. Infrastructure as Code (IaC) - Allows developers to use automated scripts to provision resources on their own. 4. Shared Ownership - Encouraging both Dev and Ops teams to take ownership of the entire software lifecycle.
  • Results: Acme can speed up delivery, enhance responsiveness, and prevent unauthorized resource like Jim's personal cloud account use by dismantling silos, fostering collaboration, and adopting IaC.
  • Skills: Cultural Transformation, Communication, Infrastructure as Code (IaC), and Shared Ownership.

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Implementation of DevOps at E-Commerce Website Company

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  • Client: E-Commerce Company
  • Summary of Case Study: The backend developer Roopa faced obstacles when trying to introduce a new feature by picking up a story from the Kanban board on the e-commerce site because of teams working in isolation, manual procedures, and inadequate testing, resulting in conflicts during merging, unsuccessful deployment to production, and substantial delays.
  • Problem: The ongoing development process using frontend team's GitHub and database team's schema change ticket was disorganized, resulting in delays and heightened risk. There was a lack of effective communication among the frontend, backend, and database teams, leading to mistakes in manual processes. Insufficient testing resulted in problems being found only during the production release. Rollback of release identified the reason for the failure which was schema update never being applied for Roopa's new feature.
  • Solution: We implemented DevOps methodologies. 1. Continuous Integration (CI) - Used to automate the integration of code and detect conflicts early. 2. Continuous Deployment (CD) - Automating the release process with CD helps reduce human error. 3. Cross-functional Collaboration - Collaboration across different functions to enhance communication and coordination across teams. 4. Automated Testing - Used to guarantee the quality of code and detect errors prior to deployment.
  • Results: The organization saw a decrease in development cycle lengths, enhanced deployment dependability, and lowered the chances of expensive mistakes by adopting DevOps. This more efficient development workflow allowed for quicker feature releases.
  • Skills: Automation, Collaboration, Testing, and Infrastructure Management.

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Implementation of DevOps at Beta Company

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  • Client: Beta Company
  • Summary of Case Study: Beta's account team lead Jeff aimed to introduce a new product recommendation feature but encountered setbacks because of the product team's backlog as per Susan.
  • Problem: The isolation of team structures and absence of code-sharing impede collaboration and result in redundant work from product team member Kiet.
  • Solution: 1. Implement a Platform Team Model - Create a platform team in charge of developing and upkeeping shared services (such as a product recommendation engine) that can be used by various teams. 2. Encourage Inner Sourcing - Motivate teams to share code and knowledge, enabling others to contribute and speed up development. 3. Promote Collaborative Culture - Offer rewards for working together across teams and sharing knowledge.
  • Results: The platform team structure allows for quicker development and eliminates duplicated tasks. Inner sourcing utilizes the knowledge of various teams, leading to code that is stronger and easier to maintain. Breaking barriers leads to a more cooperative and effective work setting.
  • Skills: Collaboration, Communication, Problem-Solving, Teamwork, and Adaptability.

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Capstone Computer Networks and Cisco Devices Lab

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  • Summary of Lab: By simulating network topologies, Cisco Packet Tracer allows to identify and connect different network components, such as end devices, connection media, and intermediary devices. It helps us understand the process of accessing and managing Cisco devices remotely, using telnet, SSH, and console. Additionally, implementing line passwords and password encryption services ensures the confidentiality and integrity of network devices protecting against unauthorized access. It is a crucial skill for network management, administration, configuration, and security.
  • Problem: In the modern interconnected society, organizations rely on computer networks to communicate, exchange resources, and carry out business activities. Nevertheless, the complexity and challenges of managing and securing these networks can be daunting.
  • Network Components: Identify and connect routers, switches, PCs, and various cable types.
  • Device Management: Learn how to configure Cisco devices through Telnet, SSH, and console connections.
  • Device Security: Implement line passwords and encryption to secure network access.
  • Configuration Essentials: Set hostnames, IP addresses, enable interfaces, and save configurations.
  • Technologies Used: Cisco Packet Tracer, Cisco Router, Cisco Switch, Telnet, SSH, Console Connection, and Password Encryption Service
  • Results: Gained a deep understanding of computer networks, the ability to manage Cisco devices, and the knowledge to implement basic security measures.
  • Skills: Cisco Devices, Computer Networks, Packet Tracer, and CCNA.

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