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The project is a concoction of research (audio signal processing, keyword spotting, ASR), development (audio data processing, deep neural network training, evaluation) and deployment (building model artifacts, web app development, docker, cloud PaaS) by integrating CI/CD pipelines with automated tests and releases.

Dockerfile 1.53% Python 39.36% PureBasic 55.88% CSS 1.44% HTML 1.79%

asr ci-cd cnn-lstm-models deep-neural-networks docker dockercontainers flask github-actions kws mlflow

deploying-an-end-to-end-keyword-spotting-model-into-cloud-server-by-integrating-ci-cd-pipeline's Introduction

Hi 👋, I'm Jithin Sasikumar from Germany

Master's student in Autonomous Systems [ML/DL] & developer. Experienced in deep learning, speech technologies, NLP and MLOps.

🔭 I am really passionate about programming, Machine Learning, Deep Learning, ASR, NLP and software development.
🔭 I’m currently working on MLOps, Deep learning, Automatic Speech Recognition and Language modeling.
🔭 I'm always keen on amalgamating research, development, and deployment (obsessed with deploying models into production).
👨‍💻 All of my projects are available @ https://github.com/Jithsaavvy?tab=repositories
📫 Reach me @ [email protected]
📄 Know about my experiences @ https://www.linkedin.com/in/jithin-sasikumar/

Interests:

ASR | MLOps | NLP | Automated CI/CD for end-to-end ML Pipelines | Conversational AI | Deep Neural Networks (RNN, LSTM, CNN, End to End models, Attention models, Acoustic models) | Federated Learning

Skills:

Languages: Python | Groovy | Java (Intermediate) | HTML | CSS | YAML | C++ | SQL
ML/DL Frameworks: Tensorflow | Tensorflow Serving | Tensorflow Federated | Keras | Scikit-learn | Pytorch (Intermediate)
Cloud Technologies: AWS (Amazon S3, Amazon SageMaker, Amazon ECR, Amazon EC2) | Heroku
Container Orchestration: Kubernetes
Data Warehouse: Snowflake
Tools: Docker | MLflow | Poetry | Flask | Hydra | JFrog | Pytest | Jupyter Notebooks
CI/CD Tools & Version Control: Git | GitHub | GitHub Actions | GitLab | GitLab CI/CD
Workflow Orchestration: Apache Airflow
Build Automation: Gradle
Operating Systems: Linux | Windows

Connect with me:

deploying-an-end-to-end-keyword-spotting-model-into-cloud-server-by-integrating-ci-cd-pipeline's People

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ayon06112002 hamzajakouk chaitanyarai-deployaccount khushbookk

deploying-an-end-to-end-keyword-spotting-model-into-cloud-server-by-integrating-ci-cd-pipeline's Issues

Future steps

Implement data management pipeline for data extraction, validation, data version control etc.
Use cloud storage services like Amazon S3 bucket to store data, artifacts, predictions.
Even though exception handling is implemented in the code, it is equally important to write separate test cases for different scenarios.
Orchestrate the entire workflow as automated pipeline by means of orchestration tools like Airflow, KubeFlow. As this is a small personal project with a static dataset, the pipeline can be created using normal function calls. But, it is crucial and predominant to replace them with orchestration tools for large, scalable and real-time workflows.
Implement Continuous Training (CT) pipeline along with CI/CD.

Poetry - Install dependencies from requirements.txt

When using poetry over pip, it is possible to add and install dependencies using poetry add. But, what if there are multiple dependencies specified in requirements.txt (conventional way) ?

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