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Add MLflow to llm-tools.md #34

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- [skypilot](https://github.com/skypilot-org/skypilot) Run LLMs, AI, and Batch jobs on any cloud. Get maximum savings, highest GPU availability, and managed execution
- [ONNX Runtime](https://github.com/microsoft/onnxruntime) cross-platform inference and training machine-learning accelerator compatible with PyTorch, TensorFlow/Keras, scikit-learn, LightGBM, XGBoost, etc. and runs with different hardware, drivers, and operating systems
- [vllm](https://github.com/vllm-project/vllm) high-throughput and memory-efficient inference and serving engine for LLMs, [paper](https://arxiv.org/pdf/2309.06180v1.pdf)
- [MLflow](https://github.com/mlflow/mlflow) An open-source framework for the end-to-end machine learning lifecycle, helping developers track experiments, evaluate models/prompts, deploy models, and add observability with tracing.
- [openllmetry](https://github.com/traceloop/openllmetry) observability for your LLM application, based on OpenTelemetry
- [DeepSpeed-FastGen](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen) High-throughput Text Generation for LLMs at 2x vLLM speeds
- [DeepSparse](https://github.com/neuralmagic/deepsparse) Sparsity-aware deep learning inference runtime for CPUs
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