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Last Updated: March, 21 2025
Machine Learning (ML) and Artificial Intelligence (AI) technologies are everywhere 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. AI and ML also enhance healthcare in resource-poor settings.
Businesses gain 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 tricky and challenging for many. That’s where Space-O’s machine learning development services come in.
Space-O has over 15 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 about machine learning development and how machine learning consulting services can help you get started with the best strategies. Let’s start with the introduction to machine learning and how it works.
Machine learning is a subset of AI and computer science that enables AI systems to learn new things without explicit programming. Machine learning models allow computer systems to mimic humans in a way that they improve their functions through pattern recognition.
The main goal of AI is to mimic human beings in specific tasks and actions. Different types of machine learning algorithms help AI models understand 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 about machine learning, 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.
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To better understand how the machine learning development process works, we will break down the whole process into seven steps for easier comprehension.
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, selecting or composing optimal data for an ML model to learn is crucial. The optimal data will be in the right format, include all the necessary labels and features, and best align with the primary goal. This also depends on your project budget, scale, and primary objective.
Data preparation or preprocessing is an essential task to optimize the machine learning system’s training process. This step involves tidying up and grooming the data, including removing mistakes and redundant data, adding missing data, and molding the data in a uniform format so that the ML model can comprehend it best.
Choosing the right ML model is paramount to function according to the data and the end goal. Many machine learning models, such as neural networks, linear regression, and decision trees, are available.
To give you an idea, here is what these models are best used for:
| ML Model | Primary Feature | Use Cases |
| Neural Networks | Learning intricate relationships with nonlinear input data | ⇒Medical diagnosis ⇒Speech recognition ⇒Chatbots & virtual assistants |
| Linear Regression | Learning linear data for simpler operations on a lower budget | ⇒Data science ⇒Market analysis ⇒Business decisions |
| Decision Trees | Best for when the target variables are discrete or categorical, and with branches | ⇒Engineering ⇒Civil Planning ⇒Troubleshooting |
The size and type of your data, the complexity of the problem, and the availability of computational resources all play a role in choosing the right model for your business.
Once you choose the right model, your next step is to train it by feeding it the prepared data. Here, you allow the model to adjust its internal parameters to better predict the output and learn from data faster.
However, you must avoid overfitting or underfitting the data during training. Overfitting data will make your ML model perform well on the data used for training but poorly on new unseen data, whereas underfitting will make it perform badly with both.
Once the training is complete, evaluating the model’s performance before deployment is essential, just like a beta test for software. It is wise to deploy the model once it has shown promising performance during evaluation.
For example, you can use evaluation datasets for a face recognition algorithm during this step. An evaluation dataset is a set of images to test an algorithm’s performance. Accuracy, precision, and total error rate are metrics through which we can evaluate a machine learning algorithm.
After you evaluate the model, you may need to tweak some of its parameters to improve its performance, especially if the evaluation performance is not up to par. This process is also called ‘hyperparameter optimization’.
Popular techniques for hyperparameter optimization include trying out different combinations of model parameters (grid search) or dividing your data into subsets and using them individually to train your model (cross-validation).
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 must analyze its outcomes and keep the algorithm up to date with upgrades and optimizations.
Four main methods can be used to classify machine learning development. Each serves a unique purpose and is popular among developers even today.
Supervised machine learning is the most common method for machine learning development. In supervised learning, we train the model with labeled data, which has a label that the model is trying to predict.
As a result, the model will instantly know if the outcome was right and will learn 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 supervised learning methods.
As you can guess, the unsupervised machine learning method feeds unlabeled data to the model. The model then 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 used for clustering and dimensionality reduction.
A popular example of this would be segmenting a company’s customer base. By examining patterns, demographic data, and other information, the algorithm can divide customers with similar behaviors into segments without any accompanying labels.
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 provides good accuracy akin to the supervised method without the expenses reaching as high.
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; 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.
Machine learning algorithms offer numerous benefits, especially during data analysis and decision-making. Here are four such advantages:
There are many scenarios where developing and integrating a machine learning algorithm can act as a viable solution. Here are six such examples:
If you want to leverage machine learning for your business, you can go about it in two ways. You can develop the algorithm in-house or outsource the process to a professional solution provider.
Plenty of factors 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 simplified the comparison. This table will help you make the most suitable decision for your business.
| In-house ML Development | Outsourcing ML Development |
|---|---|
| Businesses may need to hire and allocate dedicated machine learning engineers to develop a robust algorithm for the best results. | Businesses can partner with a service provider for the duration of machine learning projects without hiring additional staff. |
| The ML project’s quality and return on investment depend on the skill and expertise of the developers you hire and allocate. | Service providers can provide developers with the highest-standard expertise and skill sets for your project at a reasonable price. |
| Hiring new skilled staff may yield positive returns in the long run, as they can help continuously maintain and upgrade the ML system. | You get a value-for-money project corresponding to the price and quality of your chosen solution provider. |
| The project timeline remains consistent after deployment, with the same team usually handling upgrades and maintenance work. | In-house developers may take time to adjust to the project when taking over from service providers. |
| Works best for businesses looking to develop multiple projects and have a long-term plan for their ML services. | Works for various use cases for machine learning projects with a specific requirement. |
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 like TensorFlow, PyTorch, and Keras.
With 14+ years of experience delivering AI and ML solutions for all industries, Space-O is the ideal ML development firm to partner with.
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It usually takes 3-5 weeks to develop a prototype, 2-4 months for a simple ML solution such as a basic image classification model, and more than 1 year for complex models to go from ideation to deployment. This includes everything from data preparation to model training, QA testing, and integration.
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