A Comprehensive Guide to Machine Learning Development

Machine Learning (ML) and Artificial Intelligence (AI) technologies are everywhere around us right now. You will find AI and ML in everything from talking to a chatbot online to your streaming platform recommending movies similar to the ones you like to watch. You will find it enhancing healthcare in resource-poor settings as well.

Businesses gain all kinds of competitive advantages by developing and deploying a versatile and effective machine learning model into their workflows. However, the benefits come after a successful integration, and machine learning development can be a tricky and challenging task for many. That’s where Space-O’s machine learning development services come in.

Space-O has over 14 years of experience creating AI solutions that help businesses automate processes, streamline workflows, forecast risks, and maximize cost efficiency. This blog will guide you through everything you need to know about machine learning development and how machine learning consulting services can help you get started with the best strategies.

What is Machine Learning in AI?

Machine learning is a subset of artificial intelligence that enables AI systems to learn new things without any explicit programming. Machine learning models allow computer systems to mimic humans in a way that they improve their functions through pattern recognition.

As the main goal of artificial intelligence is to mimic human beings in certain tasks and actions, machine learning algorithms help AI models understand things such as human nature and the outcomes of their actions without allocating a dedicated developer or programmer. ML  allows AI to grow innately and become more powerful and useful with time and experience.

Now that you know what machine learning is, let’s examine how it works. And don’t worry—this is not a university class—we will try to make it as easy to understand as possible.

How Does Machine Learning Work?

To better understand how the machine learning development process works, we will break down the whole process into seven steps for easier comprehension.

Step 1: Data collection

Data is the primary ingredient that machine learning models run on. A machine learning model trains on the data we input, and the quality and quantity of the training data determine how effective and accurate the model will be once it’s ready.

As such, it is crucial to select or compose optimal training data for an ML model to learn. Here, optimal training data will be the data that is of the right format, includes all the necessary labels and features, and aligns best with the primary goal. This will also depend on your project budget, scale, and primary objective. 

Step 2: Data preparation

Data preparation or data preprocessing is an essential task to optimize the training process of the machine learning system. This step involves tidying up and grooming the data, including removing any mistakes and redundant data, adding missing data, and molding the data in a uniform format for the ML model to comprehend it best.

Step 3: Choosing the right model

Choosing the right ML model is paramount for it to function according to the data and the end goal. Plenty of machine learning models are available, such as neural networks, linear regression, and decision trees.

To give you an idea, here is what these models are best used for:

The size and type of your data, the complexity of the problem, and the availability of computational resources, all come into play when choosing the right model for your business.

Step 4: Training the model

Once you choose the right model, your next step will be training it by feeding in the prepared data. Here, you allow the model to adjust its internal parameters to predict the output better and learn faster.

However, you have to make sure to avoid overfitting or underfitting the data during training. Overfitting data will render your ML model performing well on the training data but poorly on new data, whereas underfitting will render it perform badly with both.

Step 5: Evaluating the model

Once the model is done with training, it is important to evaluate its performance before deployment, just like a beta test for software. It is wise only to deploy a machine learning system to go live once it has shown promising performance during evaluation.

For example, for a face recognition algorithm, you can use evaluation datasets during this step. An evaluation dataset is a set of images on which you can test an algorithm’s performance. Accuracy, precision, and total error rate are some metrics through which we can evaluate a machine learning algorithm.

Step 6: Tuning and optimization

After you evaluate the model, you may need to tweak some of its parameters to improve the performance, especially if the evaluation performance is not up to the mark. You can also call this process ‘hyperparameter optimization’.

Trying out different combinations of parameters (grid search), or dividing your data into subsets and using them individually to train your model (cross-validation) are popular techniques for hyperparameter optimization.

Step 7: Predictions and deployment

This process is also called MLOps, where once the model is ready after tuning and optimization, you deploy it in production. However, it doesn’t end here;to keep your machine learning model effective in the long run, you have to analyze its outcomes and keep the algorithm up to date with upgrades and optimizations.

What are the Different Methods of Machine Learning Development?

You can classify machine learning development based on four main methods. Each of these methods serves a unique purpose and is popular among developers even today.

1. Supervised machine learning

Supervised machine learning is the most common type of method for machine learning development. In supervised learning, we train the model with labeled data. In other words, the data comes with a label that the model is trying to predict. As a result, the model will instantly know if the outcome was right or not, and grow from its mistakes before making another one.

In supervised learning, the model learns how to connect features of the data (input) with labels such as categories or values (output). Linear regression and decision trees are examples of the supervised learning method.

2. Unsupervised machine learning

The unsupervised machine learning method, as you can guess, feeds unlabelled data to the model. Here, the model has to figure out the new data and how to label it on its own. Despite that, it’s a popular learning method because it’s useful for machine learning models that are used for clustering and dimensionality reduction.

A popular example of this would be to segment a company’s customer base. Here, by examining patterns, demographic data, and other information, the algorithm can divide customers with similar behaviors into segments without any accompanying labels.

3. Semi-supervised machine learning

The labeled approach costs more resources and effort whereas the unsupervised machine learning method is more affordable. On the other hand, it also offers more accuracy, where the unsupervised learning method falls short.

Just like its name, a semi-supervised machine learning model blends both techniques into a hybrid method and offers a balanced approach.  It offers good accuracy akin to the supervised method without the expenses reaching as high.

4. Reinforcement learning

Here, the model learns by interacting with the input data in a reward-based system. If the model predicts or labels the data accurately, it gets a positive reaction, and if it makes a mistake, it gets a negative response.

As a result, with enough training, the model can learn what to predict for a particular data, accurately and swiftly, through experience.

What are the Benefits of Developing Machine Learning Models?

Machine learning algorithms offer numerous benefits, especially during data analysis and decision-making. Here are four such advantages:

1. Effective Data Analysis
   Data acquisition and pattern recognition are two of the main functions of machine learning algorithms. Hence, collecting a vast amount of data and combing through it for useful insights is one of the essential benefits of developing an ML system for your business.
2. Tailored Solutions
   Developing a custom machine learning algorithm for your use case will be the most optimal solution for your business than conventional universal solutions that try to cater to all businesses simultaneously.
3. Enhanced Customer Journey
   One of the prominent features of machine learning algorithms is their ability to learn and adapt to data in real time. If businesses use it effectively, they can leverage ML’s capabilities to offer a personalized and enhanced customer experience to each individual. This can increase customer satisfaction and, by extension, customer retention.
4. Process Automation
   A versatile ML system can reduce manual work by automating certain processes. Other than resource savings, doing so also increases the efficiency and accuracy of said processes while eliminating human error.

What are the Applications of Machine Learning for Businesses?

There are many scenarios where developing and integrating a machine learning algorithm can act as a viable solution. Here are six such examples:

1. Natural Language Processing (NLP)
   Machine learning systems with Natural Language Processing can facilitate seamless communication with a diverse set of customers speaking different languages. This can help businesses grow their area of operations across countries – all with an AI model using one of the NLP techniques.
2. Human Resource Information Systems (HRIS)
   For larger businesses and enterprises, hiring and managing employees can be a very complex task. In such a case, businesses can develop machine learning models to streamline tasks such as talent scouting and performance assessments to get a holistic view of their employees.
3. Customer Relationship Management (CRM)
   We have already covered how machine learning models can help businesses enhance customer journeys and increase satisfaction. Businesses looking to predict customer behavior and tweak their services can do so by utilizing ML’s prediction capabilities.
4. Business Analytics
   Data analysis and insights drive the decision-making processes of a business. What better way to enhance a process than using an ML model developed for that specific reason? By spotting patterns and emerging trends, an ML system can help businesses strategize actions, enhance their operational prowess, and stay ahead of the competition.
5. Fraud Detection and Mitigation
   AI and ML models can help with real-time fraud detection by recognizing any anomalies in a customer’s purchase pattern, or by adding an extra layer of security through facial or biometric verification. Businesses can reduce overall loss through fraud by using ML.
6. Supply Chain Management
   ML models help businesses keep the optimal amount of stock, preventing overstocking or understocking of their products. ML models do so by predicting demand and forecasting restocking needs for businesses to help them make the right decisions during the time of restocking.

Developing Machine Learning Models vs Hiring a Service Provider

If you want to leverage machine learning for your business, there are two ways you can go about it. You can either develop the algorithm in-house, or you can outsource the process to a professional solution provider.

There are plenty of factors that go into deciding which option is best for you, and it is natural to be in a dilemma between the two options. However, we have managed to break it down into a simple comparison. Hopefully, this table will help you make the best and most suitable decision for your business.

Leverage the Power of Versatile Machine Learning Development with Space-O

Augment your existing development team with Space-O or allow us to take care of your machine learning development project with our team of 80+ certified ML engineers. Our machine learning engineers are highly proficient in ML libraries and frameworks such as TensorFlow, PyTorch, and Keras. Moreover, our developers have experience and expertise in various industries and can help improve the accuracy and efficiency of daily processes and machine learning applications for businesses.

With 14+ years of experience in delivering ML and AI solutions for businesses across various industries, including healthcare, eCommerce, finance, and more, Space-O is the ideal ML development firm to partner with.