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SOLID - Training models in TensorFlow The SOLID principles can be applied to the software architecture surrounding the machine learning models. Here's how the principles can be relevant: Single Responsibility Principle (SRP) Each class or module in your TensorFlow project should have a clear and single responsibility. For example, you can have separate modules for data preprocessing, model training, model evaluation, and model deployment. This promotes modularity and makes it easier to understand, test, and maintain each component. Open-Closed Principle (OCP) By designing your TensorFlow project with the OCP in mind, you can make it easier to extend the functionality without modifying existing code. For example, you can define abstract base classes or interfaces that define the common behavior expected from different models, allowing you to add new models by implementing these interfaces without modifying the existing code that consumes them. Liskov Substitution Principle (LS

Hiring Process Interviews First Round Hiring Process IT team structure Second Round IT team structure Job description Have to talk to management? Have to gather requirements? Code only? Nature of the system? Web Large System Distributed Code what? Infrastrucure? System Architect? Design Patterns Creational Patterns Builder class Pizza: def init(self): def str(self): class PizzaBuilder def init(self): def set_size(self, size): def add_cheese(self): def add_pepperoni(self): def add_bacon(self): def build(self): pizza = builder.set_size("Large").add_cheese().add_pepperoni().build() Factory class DataProcessor def process(self) class DatabaseExtractor(DataProcessor) def process(self): class APIExtractor(DataProcessor) def process(self) class FileExtractor(DataProcessor) def process(self) class DataProcessorFactory: def create_data_processor(source) Structural Patterns Adapter class MediaPlayer: def play(self