| Languages & Platforms |
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Python : Core language for AI/ML model development, including libraries like PyTorch and TensorFlow.
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C++/CUDA : For performance optimization, especially in GPU-accelerated environments.
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Bash/Shell Scripting : For automation and deployment scripts.
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| AI/ML Frameworks |
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PyTorch : For deep learning model development and training.
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TensorFlow/Keras : Alternative framework for neural network development.
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Transformers (Hugging Face) : Pretrained model usage and fine-tuning.
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ONNX : For model interoperability and deployment.
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| Model Training & Optimization |
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CUDA/CuDNN : GPU acceleration for training large models.
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Distributed Training : Using tools like PyTorch Distributed, Horovod.
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Mixed Precision Training : Using tools like Apex or native PyTorch support for faster training with reduced memory usage.
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| Model Deployment |
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ONNX Runtime :For optimized model inference.
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Triton Inference Server : Scalable inference server for deploying models in production.
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TensorRT : For high-performance inference, especially on NVIDIA GPUs.
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Docker & Kubernetes : For containerized deployment and orchestration of ML models.
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| Data Engineering |
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Apache Kafka :For streaming data pipelines.
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Apache Spark : For large-scale data processing.
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ETL Pipelines : Using tools like Airflow or Luigi for data preparation.
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| DevOps & MLOps |
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CI/CD Pipelines :Integrating with Jenkins, GitLab CI, or GitHub Actions for automated testing and deployment
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Model Versioning & Experiment Tracking : Tools like MLflow or Weights & Biases.
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Monitoring & Logging : Prometheus, Grafana, ELK Stack for monitoring model performance and system health.
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| APIs & Integration |
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FastAPI/Flask :For creating API endpoints to serve the models.
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gRPC : For efficient, scalable microservices communication.
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| Cloud Platforms |
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AWS Sagemaker :For training and deploying ML models
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Google Cloud AI Platform : For managed AI services.
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Azure ML : For end-to-end machine learning lifecycle management
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| Security & Compliance |
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Data Encryption :Ensuring data is encrypted at rest and in transit.Model Security: Techniques for securing models from dversarial attacks
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Compliance : GDPR, CCPA, and other regulatory requirements for handling sensitive data.
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| Advanced Topics |
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| Advanced Topics |
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