Building Machine Learning-Powered Applications: A Comprehensive Guide

Machine learning (ML) is transforming industries by leveraging data and automating decision-making. MLOps (Machine Learning Operations) is a set of practices that streamline the building and running of machine learning models, creating an assembly line for their development and deployment. Any business that collects and uses data can benefit from machine learning to address diverse use cases, from proactively monitoring financial transactions for fraud in banking to advancing diagnostic tools in healthcare.

The Need for Machine Learning

Machine learning helps make sense of historical data and assists in decision-making. It's a technique to find patterns in data and build mathematical models around those findings. Once a machine learning algorithm is built and trained to form a mathematical representation of data, that model can be used to predict future data. For example, in retail, historical purchase data can be used to predict whether a user will buy a particular product.

Types of Machine Learning Algorithms

Machine learning algorithms can be divided into three categories:

Supervised Learning: This approach relies on labeled datasets to train models. The training dataset includes input and labeled output data for tasks. It is widely used in predictive analytics and decision-making.
Unsupervised Learning: This approach learns from data without labeled outputs, requiring only input variables. It is designed to uncover hidden insights, patterns, trends, or groupings without direct supervision.
Reinforcement Learning: This approach uses a trial-and-error feedback loop to optimize actions in dynamic environments. It does this through reward signals that are released whenever a successful action is performed, and the system learns through trial and error. Reinforcement learning is used a lot for applications that include the likes of any AI development.

The Machine Learning Application Development Process

Building a machine learning application is an iterative process that follows a set of sequences.

1. Problem Framing

The first step is to frame the machine learning problem in terms of what you want to predict and what kind of observation data you have to make those predictions. Predictions are generally a label or a target answer; it may be a yes/no label (binary classification) or a category (multiclass classification) or a real number (regression).

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2. Data Collection and Cleaning

Once the problem is framed and the kind of historical data needed for prediction modeling is identified, the next step is to collect the data from a historical database, open datasets, or other data sources. Not all collected data is useful for a machine learning application. It may be necessary to clean the irrelevant data, which may affect the accuracy of prediction or may take additional computation without aiding in the result. According to Forrester, over 25% of global data and analytics employees report annual losses exceeding $5 million due to poor data quality, with 7% estimating losses of $25 million or more.

3. Data Preparation

Once the data is ready for the machine learning algorithm, it needs to be transformed into a form that the ML system can understand. Machines cannot understand an image or text. It needs to be converted into numbers. It also requires building data pipelines, depending on the machine learning application needs.

4. Feature Engineering

Sometimes, raw data may not reveal all the facts about the targeted label. Feature engineering is a technique to create additional features by combining two or more existing features 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%. A ratio of RAM to CPU usage can be used as a new feature, which may provide a better prediction. If deep learning is used, it will automatically build features itself; explicit feature engineering is not needed.

5. Model Training

Before training the model, the data needs to be split into training and evaluation sets, as monitoring how well a model generalizes to unseen data is important. The algorithm will then learn the pattern and mapping between the feature and the label. The learning can be linear or non-linear, depending upon the activation function and algorithm. There are a few hyperparameters that affect the learning as well as training time, such as learning rate, regularization, batch size, number of passes (epoch), optimization algorithm, and more.

6. Model Evaluation and Improvement

Accuracy is a measure to know how good or bad a model is doing on an unseen validation set. Based on the current learnings, it is important to evaluate how a model is doing on a validation set. Depending on the application, different accuracy metrics can be used. For example, 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, the problem can be classified in either of two classes:

Over-fitting: When a model is doing well on the training data, but not on the validation data, it is the over-fitting scenario. Somehow model is not generalizing well. The solution for the problem includes regularizing algorithm, decreasing input features, eliminating the redundant feature, and using resampling techniques like k-fold cross-validation.
Under-fitting: In the under-fitting scenario, a model does poor on both training and validation dataset. The solution to this may include training with more data, evaluating different algorithms or architectures, using more number of passes, experimenting with learning rate or optimization algorithm.

After iterative training, the algorithm will learn a model to represent those labels from input data, and this model can be used to predict on the unseen data.

7. Model Deployment

After training, the model will do well on the unseen data and now it can be used for prediction. This is the most important thing for businesses. This is also one of the most difficult phases for business-oriented machine learning applications. In this phase, the model is deployed in production for the prediction on real-world data to derive the results.

8. Iterate and Innovate

The development of machine learning applications is an iterative process. Practice a culture of continuous improvement and innovation. User feedback, performance analysis, and flexibility to adapt to evolving business requirements and technological advancement are what make an advanced ML application development approach.

Ensuring Model Effectiveness

Machine learning models typically require large volumes of high-quality training data to ensure accuracy. As an example, supervised machine learning models are trained on labeled datasets, which include both input variables and their corresponding output labels. Preparing and labeling this data is usually the responsibility of a data scientist and can be a labor-intensive process. In both cases, the quality of the data plays a critical role in determining the model’s overall effectiveness. Poor-quality training data can lead to ineffective models once deployed.

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The real-world effectiveness of a machine learning model depends on its ability to generalize. Models are often at risk of overfitting, where the algorithm becomes too closely aligned to the training data. This can result in reduced accuracy or even a complete loss of functionality when handling new data in a live environment. To counter this, the dataset is typically split into training and testing subsets. A significant portion (e.g., 80%) is allocated for training, while the remaining portion serves as testing data. The model is trained using the training dataset and then evaluated on the testing dataset, which acts as new, unseen data. This process is also called cross validation in machine learning. These are methods that stimulate how a model will perform in real-world scenarios, ensuring the model doesn’t merely memorize the training data but can generalize effectively.

Exhaustive Techniques: These methods test all possible combinations and iterations of training and testing datasets. While exhaustive techniques provide the most detailed insights into the dataset and model performance, they are time-consuming and resource-intensive.
Non-exhaustive Techniques: These involve creating randomized partitions of training and testing subsets.

Model Optimization

Model optimization is an integral part of achieving accuracy in a live environment when building a machine learning model. The aim is to tweak model configuration to improve accuracy and efficiency. Models can also be optimized to fit specific goals, tasks, or use cases. The process of machine learning optimization involves the assessment and reconfiguration of model hyperparameters, which are model configurations set by the data scientist. Hyperparameters aren’t learned or developed by the model through machine learning. Instead, these are configurations chosen and set by the designer of the model. Examples of hyperparameters include the structure of the model, the learning rate, or the number of clusters a model should categorize data into. Historically, the process of hyperparameter optimization was performed through trial and error. This was extremely time consuming and resource intensive. Now, optimization algorithms are used to rapidly assess hyperparameter configuration to identify the most effective settings. Examples include bayesian optimisation, which takes a sequential approach to hyperparameter analysis.

Containerization

More and more organizations are leveraging containerization as a tool for machine learning deployment. Containers are a popular environment for deploying machine learning models as the approach makes updating or deploying different parts of the model more straightforward. As well as providing a consistent environment for a model to function, containers are also intrinsically scalable.

When to Use Machine Learning

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 on a regular basis. It is sometimes better to rely on conventional software than machine learning. For certain use cases, a robust solution can be built without machine learning, which can rely on rules, simple calculations, or pre-determined processes for results and decision-making. These things are easily programmable and do not need any exhaustive learning. Hence, experts suggest using machine learning in certain special cases and scenarios:

There are two scenarios where machine learning solutions can be used:

Inability to code the rules:
- Tasks which cannot be done by deploying a set of rules
- Difficulty identifying and implementing rules
- Multiple rules to go hand in hand, which are difficult to code
- Other factors making it difficult to code the rules based on those factors
- Overlapping rules rendering inaccurate codes
Data scale is high:
- When you can define rules from a few samples, but it is difficult to scan millions of data sets for a better prediction.

Machine learning model integration can be used for both above scenarios as it brings out a mathematical model containing rules and can solve large-scale problems.

Seldon: Streamlining Model Deployment

Seldon focuses on the deployment of models, with its real-time machine learning framework with enhanced observability. Using a tool like Seldon enables faster model deployment through advanced experimentation features and a flexible, agnostic approach that seamlessly integrates with your existing legacy stack and adapts to future requirements.

BaseTen: Simplifying the ML Application Building Process

BaseTen allows the deployment of scikit-learn, Tensorflow, and PyTorch models. To serve models, users can write Python code in BaseTen, and it will be wrapped in an API and served. All of the above is wrapped in a worklet, depicted in BaseTen as an inference graph made up of different types of nodes. Worklets allow users to think in terms of an application’s business logic and overall workflow. Using the drag-and-drop UI in BaseTen, it’s easy to add and arrange components to build a view, including text inputs, file pickers, forms, tables, PDF viewers, and data visualizations. BaseTen also includes utilities to easily collect and display data, react to user inputs, and call internal and external APIs. Views can link to one another to build multi-page applications. Unlike notebook-based app builders, user state is a first-class concept. This means BaseTen can power complex human-in-the-loop applications.

The Rise of Reinforcement Learning

While supervised and unsupervised learning have many practical implications for businesses, reinforcement learning (RL) has a very limited business application like path optimization for the transit industry. However, RL is going through extensive research and slowly taking over supervised and unsupervised learning. RL holds the future for businesses and is super powerful.

Reinforcement Learning (RL) is a type of machine learning where a system, called an agent, learns to make decisions by interacting with its surroundings or environment. The agent learns by trial and error, and it aims to achieve a goal by taking action. In simpler terms, it is like a computer system learning how to play a game.

The Power of Reinforcement Learning: A Case Study

DeepMind built AlphaGo algorithm based on reinforcement algorithms that learned by analyzing games and playing against a real player. In Oct 2015, it won against a professional player named Fan Hui by 5-0. In March 2016, AlphaGo was set to take on the Go champion named Lee Sedol and won the game by 4-1. The next version named AlphaGoZero learned the game just by playing against itself and feeding basic rules. In just three days of training, it surpassed the ability of AlphaGo, which won against the world champion Lee Sedol. Although this was achieved by reinforcement learning, inside it, they used deep convolutional neural networks (CNN) to process images. CNN is the type of deep learning algorithms that are widely used in business applications.

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