Stochastic Gradient Descent (SGD) is a popular optimization algorithm widely used in machine learning and deep learning for training models. As an effective technique that offers ease and straightforwardness, its use spans various styles of models across a broad range of applications.

• Extension to Large Datasets: SGD has a significant benefit in dealing with large scale datasets where employing the conventional Gradient Descent algorithm could be computationally exhaustive and time-consuming.
• Online Learning: SGD shines in the domain of online learning as it continually updates the model with the randomly chosen sample.
• Superior Efficiency: When dealing with extensive, intricate, and highly redundant datasets, SGD proves to be a faster and more efficient algorithm.
• Randomness: SGD relies on randomness, thus beneficial in escaping shallow local minimum and discovering the global minimum.
• Widespread Usage: Integral part of deep learning algorithms, SGD optimizes highly non-convex loss functions used in training deep neural networks.

Industries employing machine learning models, data-driven analytics, artificial intelligence, and deep learning technology widely employ SGD for model optimization.

Implementing SGD requires careful planning, understanding of the dataset at hand, and selection of appropriate hyperparameters. Additionally, the constant evaluation and monitoring of the model's performance are key to a successful SGD optimization implementation. Moreover, experiments in small steps with a varying learning rate and number of iterations can be instrumental in tuning the model for optimal performance.

The consideration of using SGD in a complex machine learning scenario thus requires a comprehensive understanding of these factors, to ensure its advantages can be fully harnessed, while mitigating any potential challenges.

Stochastic Gradient Descent is a powerful tool in the toolbox of every data scientist, machine learning, and deep learning practitioner owing to its efficiency, scalability, simplicity, and widespread application. By accounting for its potential drawbacks and understanding its workings, one can optimally apply SGD, thereby achieving outstanding results in their machine learning and deep learning endeavors.

SGD carries several inherent advantages that make it a favorable choice for model optimization:

• Scalability: SGD is highly adept at dealing with large datasets, often outperforming the conventional Gradient Descent algorithm in terms of efficiency and speed.
• Regular Updates: The algorithm continually updates during the training process, offering more frequent updates and fine-tuning of the model than the batch gradient descent.
• Overfitting Avoidance: By updating parameters regularly using a small number of samples, SGD can introduce inherent noise to the learning procedure, effectively helping to avoid overfitting.
• Evasion of Local Minima: The randomness in SGD's procedure can assist in overcoming local minima and finding the global minimums, something highly beneficial for non-convex loss functions.

However, several challenges and drawbacks are intrinsic to SGD, which organizations need to account for:

• Hyperparameter Tuning: SGD relies heavily on the correct selection of learning rate and other hyperparameters. This tuning often requires expert knowledge and extensive experimentation.
• Non-Steady Convergence: The inherent randomness of SGD makes the convergence non-steady with fluctuating errors, making it challenging to determine the optimal stopping point for the algorithm.
• Dynamic Learning Rate: Choosing an effective learning rate is particularly tricky with SGD. A small learning rate leads to slow convergence, while a larger rate risks overshooting the minimum.
• Dependency on Initial Values: The algorithms' functioning largely depends on the initial parameter values and has the potential to get stuck in non-optimal solutions, especially in non-convex problems.