How to Develop Machine Learning Applications for Business
Today, most businesses rely on machine learning applications to realize new revenue streams, predict market trends, analyze customer behavior and price fluctuation, and make accurate business decisions. Machine learning is a subset of artificial intelligence technology and helps make sense out of historical data and helps in decision making. It is a technique set to find patterns in data and build mathematical models around those findings. Once we make and train a machine-learning algorithm to form a mathematical representation of these data, we can use that model to predict future data.
Such framework is executed across all sectors, and thus, developing these machine learning applications requires following a structured approach with diligent planning. Problem framing, data cleaning, feature engineering, model training, and improving model accuracy are some steps that are majorly undertaken while developing such applications.
Types of machine learning algorithms
A machine learning algorithm can is divided into three categories:
- Supervised machine learning – Used for performing tasks like categorical classification (binary and multiclass), activity monitoring, predicting a numerical value.
- Unsupervised machine learning – This is used for grouping or clustering, dimensionality reduction, and anomaly detection.
- Reinforcement machine learning – Has limited business applications like path optimization for the transit industry. This is because the potential of RL is not harvested yet. It is going through extensive research and will slowly take over supervised and unsupervised learning.
Developing Machine Learning Applications
As mentioned above, Machine learning application development follows a highly structured approach. Thus, there are several involved in developing machine learning applications. The steps of paramount importance are mentioned below:
Problem framing is the first step and involves framing a machine learning problem regarding what we want to predict and the observation data required to make those predictions. Predictions are generally a label or a target answer; they may be a yes/no label (binary classification) or a category (multiclass classification), or a real number (regression).
Collecting and cleaning the data
After framing the problem and identifying what kind of historical data the business has for prediction modeling, the next step is collecting the data by Data Analytics Services. This can be from a historical database or open datasets, or any other data sources. This step is crucial as the quality and quantity of data gathered will directly determine how good the predictive model will be. The data collected is then tabulated and called Training Data.
Not all the collected data is valid for a machine learning application. Thus, the next step is to clean the irrelevant data, which may affect the accuracy of prediction or take additional computation without aiding in the result.
The data is loaded into a suitable place and then prepared for use in machine learning training. Here, the information is put all together, and then the order is randomized as the order of data should not affect what is learned.
This is also a good enough time to do any visualizations of the data, as that will help you see if there are any relevant relationships between the different variables, how you can take their advantage, and show you if there are any data imbalances present. Also, the data now has to be split into two parts. The first part used in training our model will be the majority of the dataset, and the second will be used to evaluate the trained model’s performance. The other forms of adjusting and manipulation like normalization, error correction, and more occur at this step.
Sometimes a raw data may not reveal all the facts about the targeted label. Feature engineering is a technique to create additional features combining two or more existing elements with an arithmetic operation that is more relevant and sensible.
For example: In a compute engine, it is common for RAM and CPU usage to reach 95%, but something is messy when RAM usage is at 5%, and CPU is at 93%. In this case, a ratio of RAM to CPU usage can be used as a new feature, which may provide a better prediction. If we are using deep learning, it will automatically build features itself; we do not need explicit feature engineering.
Training a model
The data is first to split into training and evaluation sets to monitor how well a model generalizes to unseen data. This lets the algorithm read and learn the pattern and helps it map between the feature and the label. The learning can be linear or non-linear, depending upon the activation function and algorithm. A few hyperparameters affect the teaching and training time, such as learning rate, regularization, batch size, number of passes (epoch), optimization algorithm, and more.
Evaluating and improving model accuracy
Accuracy is a measure of how good or bad a model is doing on an unseen validation set. Depending on the application, the model can use different accuracy metrics. E.g., for classification, we may use precision and recall or F1 Score; for object detection, we may use IoU (interaction over union).
If a model is not doing well, we may classify the problem in either of the classes:
- a) Over-fitting – When a model is doing well on the training data but not on the validation data. Somehow the model is not generalizing well. The problem includes regularizing the algorithm, decreasing input features, eliminating the redundant element, and using resampling techniques like k-fold cross-validation.
- b) Under-fitting – In the under-fitting scenario, a model does poorly on both training and validation datasets. The solution to this may include training with more data, evaluating different algorithms or architectures, using more passes, experimenting with learning rates, or optimization algorithm.
Once the evaluation is over, any further improvement in your training can be possible by tuning the parameters. There were a few parameters that were implicitly assumed when the training was done. Another parameter included is the learning rate that defines how far the line is shifted during each step, based on the information from the previous training step. These values all play a role in the accuracy of the training model and how long the training will take.
For more complex models, initial conditions play a significant role in determining the outcome of training. Differences can be seen depending on whether a model starts off training with values initialized to zeroes versus some distribution of values, leading to the question of which distribution is to be used. Since there are many considerations at this training phase, you must define what makes a model suitable. These parameters are referred to as Hyperparameters. The adjustment or tuning of these parameters depends on the dataset, model, and training process. Once you are done with these parameters and are satisfied, you can move on to the last step.
After an iterative training, the algorithm will learn a model to represent those labels from input data, and this model can be used to predict the unseen data. The model is then deployed for the prediction on real-world data to derive the results.
It is also essential to understand when to use ML. Machine learning is a powerful tool, but it should not be used frequently for it is computationally extensive and needs training and updating of models regularly. Experts suggest using machine learning in certain exceptional cases and scenarios: an inability to code the rules (difficulty identifying and implementing regulations, overlapping laws, etc.) and when the data scale is enormous.
Machine learning is the enabler of technology, but if we do not follow a proper plan and execution to train and learn models on algorithms, we may fail. Hence, it is always an excellent idea for businesses to build complex machine learning systems to hire AI and Machine learning service providers and focus on their core competencies.