AI/ML Pipeline Optimization And Its Value

The objective of machine learning is to create a model that works well and provides accurate predictions. Over time while ML models can become more accurate leveraging the data training, the models themselves can be updated or the ML pipeline can be optimized to achieve better AI capabilities and accuracy.

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Machine learning optimization is used to minimize the cost function by fine-tuning the hyperparameter using one of the machine optimization techniques.

Minimizing the cost function is an important factor because it represents the inconsistency between the true value of the estimated parameter and the value the model has predicted.

The difference between hyperparameters and parameters of the model is that hyperparameters need to be set before starting to train the model when the right learning rate is chosen.

Hyperparameters describe the structure of the model. On the other hand, parameters are obtained only during the training, not in advance. Examples of parameters are weights and biases for neural networks. This data resides internal to the model and the value changes based on their inputs.

We need hyperparameter optimization to tune the model.

By discovering the optimal combination of their values, we can reduce the errors and build the most accurate model.

Working of hyperparameter tuning in machine learning

As said earlier, hyperparameters are set before training. But we cannot know which learning rate is best in the given case in advance.

To improve the model’s performance, hyperparameters are optimized.

The output is compared with expected results after each iteration to assess the accuracy and to adjust the hyperparameters if needed.

This is an iterative process and can be done either manually or by using any of the handy optimization techniques when you work with larger data.

machine learning and optimisation
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Optimization techniques in machine learning

Exhaustive search

Exhaustive search or brute force search is the process of checking whether each option is a good match for the most optimal hyperparameters.

In machine learning, we try out all the possible options, here the number is usually larger. The exhaustive search method is very simple.

For example, if you are using the k-means algorithm, you will search for the right number of clusters manually. If there are thousands of options to consider, then it becomes unbearably slow. This makes brute force search inefficient in most real-life cases.

Gradient descent

Gradient descent is the most common optimization algorithm for minimizing errors, by iterating the training dataset while readjusting the model. The goal is to minimize the function cost with a minimum possible error to improve the accuracy of the model.

The gradient descent algorithm starts from a random point on the graphical representation and chooses the path arbitrarily. More errors were found in the wrong direction.  The optimization is over when you are not able to minimize the error anymore finding a local minimum.

Classical gradient descent does not work well with a couple of local minima. The algorithm finds only one local minimum after finding a local minimum it stops searching and does not find the global one. Gradient descent follows the same size of steps.

If you choose the large learning rate, the algorithm jumps over skipping the right answer. On the other hand, if you choose the small learning rate, it takes an exhaustive search which is an inefficient step.

The gradient search becomes computationally efficient and a quicker method to optimize the models when the right learning rate is chosen.

Algorithm
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Genetic algorithms

Genetic algorithms represent another optimization approach. This algorithm applies the theory of evolution to machine learning, only to the specimens that have the best adaptation mechanisms to reproduce and survive.

Among multiple models with some predefined hyperparameters, some are adjusted better than others. Then calculate the accuracy of each model and keep only those worked out best.

And now generate descendants for the best model hyperparameters to have the next generation of models. By iterating this process, only the best model will survive at the end of the process. Genetic algorithms do not depend on local minima or maxima. This algorithm is most commonly used in neural network models.

Deep learning model optimization

The deep learning model uses a good and high-tech algorithm instead of generic ones since training takes more computing power.

Stochastic gradient descent with momentum

The gradient descent method requires a lot of updates which is the biggest disadvantage, and the steps of gradient descent are noisy. Due to this, the gradient descent leads to the wrong direction which in turn becomes computationally expensive. This is the reason behind frequently used optimization algorithms.

RMSProp

RMSProp is used in gradient normalization because it helps to balance the size of the steps. It can work well with even the smallest batches.

Adam Optimizer

Adam optimizer can handle the noise problems better and can work efficiently with larger parameters and also with data sets.

Adopting updates in algorithms

In the field of computer vision, the YOLO family has seen some incredible research work in recent years, since it has shown to be a terrific resource for real-time object identification. Because of its quick and accurate identification, the YOLO algorithm has a lot of economic potential.

The recently launched YOLOv7 has amazing accuracy compared to its predecessors (YOLOv5, YOLO-X, YOLO-R, YOLOR-p6) and many products and solutions employing older version might want to upgrade to get a step ahead. However, YOLOv7 provides only 3-4 FPS making it unsuitable for Edge AI or Edge intelligence use-cases.

Eventually, those updates to the algorithms or newer variants which have the edge capability would emerge enabling broader applications across multiple industries like retail, robotics, automobile and more.

Also read: Deep Tech For Indian Ecosystem - Interior Design | Insights - Tooliqa

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FAQs

Quick queries for this insight

What is the difference between hyperparameters and parameters?

When the appropriate learning rate is selected, hyperparameters must be established before the model can begin to be trained, which is where they differ from the model's parameters. The model's structure is described by its hyperparameters. Parameters, on the other hand, are not acquired beforehand but only during training. Weights and biases for neural networks are two examples of parameters. The value of this data, which is internal to the model, varies depending on the inputs.

What is the difference between Gradient Descent and Stochastic Gradient Descent?

You iteratively update a set of parameters in both gradient descent (GD) and stochastic gradient descent (SGD) in order to minimize an error function. In SGD, however, you use ONLY ONE or SUBSET of training samples from your training set to make the update for a parameter in a particular iteration, whereas in GD you must run through ALL the samples in your training set to do a single update for a parameter in a particular iteration. Therefore, Stochastic Gradient Descent is preferred over Gradient Descent in case of a large number of training samples.

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