#4 Deep Learning End-to-End Life-cycle

Deep learning (DL) is a subset of machine learning (ML). A deep learning model is capable of learning by creating its own computing method. While it is easy to confuse machine learning with deep learning - there is a difference between the two. Basic ML models become better as you continue feeding them data, they would still require human guidance as the learning algorithms are not that deep. However, with deep learning, the model determines the accuracy or inaccuracy of its prediction based on its own neural network, optimizers, and loss functions. Also, the models are deep with huge datasets in the DL which increases the training time drastically.

The Deep Learning Life cycle

The DL life cycle is the cyclic process that is initiated to build an efficient machine learning project. The main purpose of a DL project is to find a solution to a problem using the available data. 

There are multiple steps in the DL end-to-end life cycle that follow a specific order

• DL Problem Statement

The first step begins by addressing that a problem exists and finding potential solutions that will tangibly improve operations, reduce the cost, time, and increase customer satisfaction. For example, in the construction industry, we now have PPE Kit detection, people safety monitoring, site development time and maintenance, etc.

• DL Data Procurement

The second step is to collect the required data and prepare it to be used in deep learning. This will imply consulting professionals in the construction industry to determine what data would be relevant in detecting the PPE kit and where the cameras will be placed to effectively cover the wide-area to accurately detect and predict the unusual mishaps happening. This stage covers the data gathering for training or performing Transfer Learning and then putting it into a file format that can be easily organized and understood by the model. 

• DL Data Pre-Processing

Data preprocessing is a Data Mining technique that entails converting raw data into a format that can be understood by the DL models. Real-world data is often incomplete, unreliable, and/or deficient in specific behaviors or patterns, as well as containing numerous errors and is also biased for a particular problem statement. Preprocessing data is a tried and true way of addressing such problems. Data pre-processing methods such as data labeling and annotation in case of video and image data and data tagging in case of speech and text data is a very important step for the DL life-cycle.

• DL Model Development

In order to gain insights into the data that you have collected and pre-processed, it is crucial that you set your target variable- which is the main component that you want to gain deeper insights from your data. For example, you want to detect how many vehicles are parked in the parking garage at a construction site? We will have to train a DL model to detect the vehicles in the parking from the CCTV camera. Sounds simple, isn’t it! Now, think if we want to detect how many vehicles are cars, trucks, SUVs, etc? Moving ahead what is the brand and make of the vehicle and what is the color of the vehicle? To perform all these analyses we need to perform iterative learning of the model and need a model ensemble layer to give us the desired results. The Deep in the DL stands for how deep the models are. 

• DL Model Optimization

After model training is completed the model needs to be optimized more for the specific case. There are generally 3 types of model optimization:

1. Accuracy optimization
2. Memory optimization
3. Latency and throughput optimization

• DL Model Inference

After the model has been successfully trained & optimized, the task at hand is to now explain these outcomes to people with very little knowledge in the field of data science. This can be particularly difficult. In the earlier years, it was difficult to convey these insights to important stakeholders in the business, but with time it has gotten easier. It is important to note that the more interpretable your model is the easier it becomes to communicate its value and importance to key stakeholders. 

• DL Model Governance

The last step in this process is to implement, document, version DL models, and ultimately maintain the data science project so that it can be harnessed while simultaneously improving its models. This step is also generally referred to as the model CI/CD pipeline and it is the longest in terms of duration in the entire deep learning cycle. 

The DL life cycle gives us a better perspective on how a data science project should be structured in order to add more value to a business or industry as a whole. Failing to execute any of these steps could result in unnecessary biases and misleading results.