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Achievement: StarstruckAchievement: Pull SharkAchievement: Arctic Code Vault Contributor

Achievements

Achievement: StarstruckAchievement: Pull SharkAchievement: Arctic Code Vault Contributor

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Jithsaavvy/README.md

Hi 👋, I'm Jithin Sasikumar from Germany

Specializing in MLOps, Kubernetes and Deep Learning | Converging HPC with Kubernetes for Scalable AI Platforms

jithsaavvy

Interests:

MLOps | GitOps | ASR | NLP | Automated CI/CD for end-to-end ML Pipelines | Deep Neural Networks | Scalable Infrastructure | Distributed ML/DL | Distributed systems | Containerization | Cloud Computing

Skills:

  • Languages: Python | Groovy | Bash | C# | C++
  • MLOps: Kubernetes with Helm | Docker | Kubeflow | MLflow | Apache Airflow | Ansible | Istio | Argo CD | Minio | Longhorn | Kaniko | YAML
  • ML/DL Frameworks: Tensorflow | Pytorch | Accelerate | Transformers | DeepSpeed | Keras | Scikit-learn Tensorflow Federated | Numpy
  • Cloud Technologies: AWS (EC2, IAM, SageMaker, S3, ECR) | Heroku
  • CI/CD Tools & Version Control: Git | GitHub | GitHub Actions | GitLab | GitLab CI/CD
  • 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Prometheus, Grafana and EFK (Elasticsearch, FluentBit, Kibana)
  • Additional Tools: Flask | FastAPI | Poetry | Slurm | Jupyter Notebooks | Gradle | Pytest | Hydra
  • Database & Warehouse: PostgreSQL | MySQL | Snowflake
  • Operating Systems: Linux | Windows

Connect with me:

jithin-sasikumar

Pinned Loading

  1. Sentiment-analysis-from-MLOps-paradigm Sentiment-analysis-from-MLOps-paradigm Public

    This project promulgates an automated end-to-end ML pipeline that trains a biLSTM network for sentiment analysis, experiment tracking, benchmarking by model testing and evaluation, model transition…

    Python 13 4

  2. Deploying-an-end-to-end-keyword-spotting-model-into-cloud-server-by-integrating-CI-CD-pipeline Deploying-an-end-to-end-keyword-spotting-model-into-cloud-server-by-integrating-CI-CD-pipeline Public

    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 mode…

    PureBasic 15 6

  3. Serving-federated-trained-models-using-tensorflow-serving-and-docker Serving-federated-trained-models-using-tensorflow-serving-and-docker Public

    This project is an amalgamation of research (federated training and comparison with normal training), development (data preprocessing, model training etc.) and deployment (model serving). It create…

    Python 3

  4. Explaining-deep-learning-models-for-detecting-anomalies-in-time-series-data-RnD-project Explaining-deep-learning-models-for-detecting-anomalies-in-time-series-data-RnD-project Public

    This research work focuses on comparing the existing approaches to explain the decisions of models trained using time-series data and proposing the best-fit method that generates explanations for a…

    Jupyter Notebook 28 6

  5. Expandable-image-classification-system-using-Places365-Convnet-and-One-vs-All-Classifier- Expandable-image-classification-system-using-Places365-Convnet-and-One-vs-All-Classifier- Public

    This research mini-project trains an expandable image classification system for place categorization which solves the closet-set limitation of convnets. The state-of-the-art Places365 convnet is tr…

    Python 1

  6. iBot---A-Conversational-and-Interactive-AI-Bot__Bachelor-Thesis__ iBot---A-Conversational-and-Interactive-AI-Bot__Bachelor-Thesis__ Public

    I developed "Intelligent Bot [iBot]" which is a programmed application that performs an automated task in a conversational format using supervised learning [ML Paradigm] and Natural Language Proces…

    C# 1 1