TDD In Practice Video Tutorial Playlist

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Pickle Vs M, VS ONNX vs SavedModel vs TorchScript

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  Pickle Vs M, VS  ONNX vs SavedModel vs TorchScript Let’s break it down in terms of purpose, use cases, compatibility, and safety : 🥒 Pickle (.pkl) Pickle is a Python-specific serialization format for objects, including machine learning models. Feature Details Use Case Serializing Python objects (including scikit-learn, XGBoost models) Compatibility Python-only (tight coupling with specific versions) Frameworks scikit-learn, XGBoost, LightGBM, etc. Speed Fast to load/save Portability ❌ Low — not portable across languages or platforms Security ⚠️ Unsafe to unpickle untrusted data (can execute arbitrary code) Deployment Typically for offline inference or Python-based pipelines ✅ Best For : Local development, internal tools, reproducible experiments ❌ Not Ideal For : Cross-platform deployment, mobile/edge/cloud scaling 🧠 TensorFlow SavedModel (MD) SavedModel is TensorFlow’s official format for storing trained models for production use. ...
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Experiment Tracking and Versioning during Model Training

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 Experiment Tracking and Versioning during Model Training Experiment Tracking   Following is just a short list of things you might want to consider tracking for each experiment during its training process:  • The loss curve corresponding to the train split and each of the eval splits.  • The model performance metrics that you care about on all nontest splits, such as accuracy, F1, perplexity. • The log of corresponding sample, prediction, and ground truth label. This comes in handy for ad hoc analytics and sanity check • The speed of your model, evaluated by the number of steps per second or, if your data is text, the number of tokens processed per second.  • System performance metrics such as memory usage and CPU/GPU utilization. They’re important to identify bottlenecks and avoid wasting system resources.  • The values over time of any parameter and hyperparameter whose changes can affect your model’s performance, such as the learning rate if you use...
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Module 3: Fine-Tuning and Customizing Generative AI Models

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  Module 3: Fine-Tuning and Customizing Generative AI Models Overview: In this module, we will explore how to fine-tune and customize pre-trained generative AI models to meet specific needs and tasks. Fine-tuning allows developers to adapt powerful, pre-existing models (such as GPT-3, BERT, or GANs) to domain-specific applications and data, which is often more efficient than training a model from scratch. You'll learn how to fine-tune various types of generative models for use cases like code generation, text generation, image synthesis, and more. By the end of this module, you will have the skills to tailor pre-trained models to your specific software development needs. Lesson 3.1: Understanding the Fine-Tuning Process 3.1.1: What is Fine-Tuning? Fine-tuning is the process of taking a pre-trained model and adapting it to a specific task or dataset. Pre-trained models are typically trained on large, diverse datasets (e.g., GPT-3 trained on vast text data). Fine-tuning helps ...
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