ML Observability & Monitoring Learning Path

This course introduces Machine Learning Operations tools to manage the complexities of AI projects. You will learn to use Weights & Biases to track experiments, version data, and collaborate. The course covers instrumenting a Jupyter notebook, managing hyperparameters, logging metrics, and tracing prompts and responses to LL Ms over time.

Image classification models find themselves in different places today, like farms, hospitals, industries, schools, and highways,... With the creation of much more efficient deep learning models from the early 2010s, we have seen a great improvement in the state of the art in the domain of image classification.In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step approach. We shall start by understanding how image classification algorithms work, and deploying them to the cloud while observing best practices. We are going to be using TensorFlow 2 (the world's most popular library for deep learning, built by Google) and Hugging Face You will learn:The Basics of TensorFlow (Tensors, Model building, training, and evaluation)Deep Learning algorithms like Convolutional neural networks and Vision Transformers Evaluation of Classification Models (Precision, Recall, Accuracy, F1-score, Confusion Matrix, ROC Curve)Mitigating overfitting with Data augmentation Advanced TensorFlow concepts like Custom Losses and Metrics, Eager and Graph Modes and Custom Training Loops, Tensorboard Machine Learning Operations (ML Ops) with Weights and Biases (Experiment Tracking, Hyperparameter Tuning, Dataset Versioning, Model Versioning)Binary Classification with Malaria detection Multi-class Classification with Human Emotions Detection Transfer learning with modern ConvNets (Vggnet, Resnet, Mobilenet, Efficientnet)Model Deployment (Onnx format, Quantization, Fastapi, Heroku Cloud)If you are