Machine learning app development is growing rapidly, and it’s easy to see why. ML algorithms can significantly enhance your app’s functionality by personalizing content and aiding in decision-making. They can also handle repetitive tasks like answering common questions, thereby improving customer service for both businesses and users.
Overall, creating a machine learning app increases the likelihood of meeting consumer needs. So, how to build a machine-learning app?
This guide offers comprehensive insights into machine learning development for web and mobile apps. You’ll learn about:
- What it takes to create ML-powered features;
- How to implement machine learning on mobile devices;
- Common challenges in machine learning app development;
- Examples of top ML apps in various industries.
What is machine learning?
Machine learning, in its broadest sense, is a type of artificial intelligence that allows computers to learn from data and make decisions or predictions without being explicitly programmed for specific tasks.
Unlike traditional software, which relies on rule-based logic, machine learning software requires large datasets for training and validation, data preprocessing, and management. It also demands more computational resources and specialized expertise in data science and machine learning.
In mobile machine learning app development, developers must additionally account for the resource constraints of mobile devices, such as limited CPU power, memory, and battery life.
They also need to address the dependence on internet connectivity and specific user interactions, such as gesture-based navigation and the use of sensors like GPS, accelerometers, and cameras.
How to create a machine-learning app from scratch?
The machine learning app development process generally resembles that of custom software development, with some unique considerations due to the needs of machine learning algorithms.
These include the requirement for large datasets for training and a specific tech stack.
Here’s a general flow reflecting those specifics:
1. Identify the problem
While it might seem that creating a machine learning app is a perfect solution for any issue, that’s not always the case. Consider this example:
Suppose you need to implement an input validation feature for a registration form to check required fields, correct email format, password strength, and matching password confirmation.
Given the current hype around machine learning app development, it might seem that these technologies would enhance the accuracy of input validation. However, simple rule-based validation can efficiently meet these requirements.
In fact, using ML/AI in this scenario would complicate development without adding significant value.
Therefore, before planning machine learning app development, ask yourself the following questions:
- Can I solve this problem without ML/AI?
- What benefit do I expect from creating an ML app?
- What benefit will my customers gain from this ML/AI feature?
Additionally, keep in mind:
- Do you have enough data to train a sustainable ML/AI model?
- How can you track the success of the ML/AI feature?
- What happens if the ML algorithm fails?
- How will your ML model evolve over time?
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Contact us2. Collect and prepare data
Data is the lifeblood of any machine learning algorithm, as it relies on data to learn and identify relevant patterns to perform the intended task.
Relevant data can be sourced from internal databases, public datasets, or collected through surveys. Once gathered, this data must be cleaned and preprocessed to ensure it is homogeneous and free of errors that could mislead the ML model during training.
For more detailed guidance, check out our article on data collection and preparation for AI/ML development.
3. Select the machine learning algorithm
The choice of ML algorithm depends on your specific goals and the nature of your problem.
Different algorithms are suited for different types of problems and require various data types for training. Consequently, your choice of algorithm will affect the complexity of your ML model and the resources needed to create a machine-learning app.
Here is a table summarizing common ML algorithms, their use cases, and characteristics:
| ML algorithm | Use case | Description |
|---|---|---|
| Linear regression | House price prediction | Predicts continuous outcomes |
| Logistic regression | Spam detection | Categories data into one of two distinct classes |
| Decision tree | Customer segmentation | Models decisions and their possible consequences as a tree-like structure |
| Support Vector Machines (SVM) | Image recognition | Finds the optimal decision that best separates the data into different classes |
| Random forest | Fraud detection | Combines the results of many decision trees and either classifies or averages them |
| Neural networks | Speech recognition | Recognizes complex patterns |
| Clustering | Market segmentation | Groups data into clusters |
| Principal component analysis | Image compression | Simplifies data and reduces noise |
| Association | Market basket analysis | Discovers relationships between variables |
4. Choose the tech stack for the ML app
Choosing the optimal tech stack depends on your project’s specific needs and your team’s expertise. Python is the most commonly used language for machine learning app development, but you might also consider an R—or JavaScript-based tech stack if your team is more comfortable with those languages.
Consider the following when choosing your tech stack:
- Python is widely used for building ML apps due to its extensive libraries and frameworks like TensorFlow, PyTorch, and scikit-learn. Suitable for most ML tasks;
- R is stronger for regression and classification tasks. It’s preferred if your project involves a lot of statistical analysis;
- JavaScript is preferable for smaller projects and when developing ML applications directly in the browser using frameworks like TensorFlow.js.
Additionally, consider the abilities of your chosen tech stack concerning your project goals. For instance, larger datasets require more powerful frameworks to manage and process the data effectively.
For a step-by-step approach to choosing a tech stack, check out our previous post on the topic.
Top 5 technologies used in ML app development
| Category | Technologies |
|---|---|
| Programming languages | Python, R, Java/Scala, C++, Go, SQL |
| Frameworks | TensorFlow, Caffe2, SciKit, PyTorch, MXNet, Keras |
| Data warehousing | Hadoop, BigQuery, Snowflake, Spark, Ray, Redshift |
| Libraries | Pandas, Keras, SciPy, NumPy, DaskML, NLTK |
| Cloud services | AWS SageMaker, IBM Watson, AWS Glue, Google Cloud AI Platform, Azure ML Studio, Azure DataBricks |
| DevOps | CometML, HopsWorks, Apache Maven, Kuberflow, MLFlow, Docker |
| Tools | Jupiter Notebooks, Google Data Lab, SQL Alchemy, Google Colab, Airflow, Hevo |
5. Build the machine learning model
You have two main options for building your ML model: using a pre-trained model or custom-training one.
Pre-trained ML models
Pre-trained models, or generic models, come already trained on large datasets and are ready to handle a wide range of tasks, from image recognition to text analysis. Your ML development team will only need to fine-tune these models to better fit your application’s specific needs.
- Pros: Quick to implement, cost-effective, and suitable for general tasks.
- Cons: Limited in performance and may struggle with specific industry jargon or standards.
Custom ML models
Custom models are trained for specific use cases within a particular domain using specialized datasets. Typically, you need to provide this dataset to your data science team to develop a custom model.
- Pros: More accurate and reliable results, tailored to specific needs, and can be retrained and updated with new data.
- Cons: Requires more time and resources to create.
Approaches to training an ML model
- Supervised learning uses manually labeled datasets to teach algorithms to classify or understand relationships between data points accurately.
- Example: Image classification where images are labeled as “cat” or “dog.”
- Unsupervised learning: algorithms search for patterns in datasets without prior labeling, though human intervention is needed to validate output variables.
- Example: Clustering customer data to identify different customer segments.
- Reinforcement learning is where the model trains in operation, taking actions and receiving rewards or punishments based on those actions.
- Example: A self-driving car learning to navigate streets by receiving rewards for safe driving and punishments for errors.
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Machine learning models are usually developed offline, so they need to be integrated into a real-time application and prepared to process real-time data.
The specific process will vary depending on the chosen tech stack, the environment, the type of model used, and the DevOps services employed by your development team.
Basically, there are two deployment options for ML models: cloud-based deployment and on-premises deployment.
Cloud-based deployment
Deploying the ML model on the cloud offers scalability, flexibility, and ease of management. To do this, choose a suitable cloud provider based on your requirements and budget, and upload your model using services like AWS Lambda or Azure Functions.
- Pros: Scalable, flexible, and easy to manage.
- Cons: Requires internet connectivity and can involve ongoing costs.
On-premises deployment
This method involves deploying the ML model on your organization’s local servers, requiring the necessary hardware and software to handle the model’s computational requirements. Developers will also need to create APIs to enable data exchange between the model and the application.
- Pros: Enhanced data security, suitable for sensitive data.
- Cons: Less scalable, requires a technical team for infrastructure support.
ML deployment for a mobile application
Mobile devices have limited computational capacity compared to desktop and cloud environments. Therefore, ML model deployment often occurs in the cloud, with access provided to the mobile application through an API.
However, for relatively simple ML models (like predictive keyboards or mobile-based computer vision for Face ID), you can deploy the model directly onto the device using on-device inference.
- Pros: Enables the ML model to work without internet access and increases speed.
- Cons: Requires creating separate app versions for different OS. Also, updating the ML model requires updating the entire application.
7. Monitor your ML application
Once the ML model has been deployed, it starts processing real-world data, which may have imperfections compared to the training data.
Therefore, it’s crucial to monitor the model’s performance, fairness, and accuracy to detect any model drift.
If you notice the model’s performance degrading, there are two main approaches to address this:
Retraining on demand
This approach involves retraining the model only when specific criteria or triggers are met, such as performance degradation, significant data changes, or at scheduled intervals.
- Pros: Allows for planned updates, minimizes disruptions, and uses resources efficiently by retraining only when necessary.
- Cons: May not quickly respond to changes in data, thus being less than ideal for applications requiring real-time updates.
Continuous training
This method involves regularly and automatically retraining the ML model as new data becomes available.
- Pros: Enables the model to learn and adapt to new patterns and trends in real-time or near real-time.
- Cons: Can be cost-prohibitive due to the constant need for computational resources.
Which industries benefit from machine learning apps the most?
E-commerce
- Personalization and recommendations. ML applications analyze user behavior, preferences, and past interactions to generate personalized product recommendations. This technology is highly effective in boosting sales, with companies reporting up to 40% of sales coming directly from personalized recommendations.
- Customer service. Evidence suggests that 60-80% of customer inquiries are repetitive and can be managed with a standard script or sequence of actions, making them ideal for chatbots. ML-powered chatbots enhance this process by using natural language processing (NLP) to understand and respond to inquiries in real-time, thereby improving customer support efficiency.
- Inventory management. Predictive analytics models forecast product demand, optimizing stock levels and reducing overstock or stockouts. Walmart is a notable advocate of ML and AI-powered inventory management systems.
- Dynamic pricing. ML applications adjust prices in real-time based on factors like demand, competitor pricing, and market trends. Uber uses dynamic pricing to balance demand and supply and improve customer satisfaction. Note that dynamic pricing can also be implemented using rule-based methods, as seen in our project with BookYaBike.
Healthcare
- Medical image analysis. ML apps analyze medical images such as X-rays, MRIs, and CT scans, improving disease detection accuracy. For example, Google’s DeepMind developed a model that identifies patients at high risk of vision loss.
- Personalized medicine. ML analyzes genetic data to create personalized treatment plans. This is particularly useful in oncology, where treatments are tailored to the genetic profile of a patient’s tumor.
- Drug discovery and development. ML accelerates drug discovery by predicting interactions between compounds and targets, identifying potential drug candidates more quickly.
- Electronic Health Records (EHR). ML algorithms manage and analyze EHRs, extracting insights from unstructured data to improve clinical decision-making and patient care. Epic is one of the companies creating ML and AI applications to manage EHR notes effectively.
- Remote health monitoring. ML algorithms analyze data from wearable devices to monitor patients’ health, providing instant alerts for abnormal patterns. For instance, the Apple Watch uses ML to detect irregular heart rhythms, notifying users of potential atrial fibrillation.
- Robotic surgery. ML provides surgeons with real-time assistance and decision support, enhancing precision and outcomes in minimally invasive procedures. For example, the da Vinci Surgical System uses ML to assist surgeons with complex procedures, offering greater control and accuracy.
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- Fraud detection and prevention. ML algorithms analyze transaction patterns to identify anomalies and flag suspicious transactions. For instance, PayPal uses ML to analyze vast amounts of transaction data to detect and prevent fraud efficiently.
- Risk management. ML models analyze a wide range of data, including market trends, economic indicators, and individual transaction histories, to make informed recommendations. In fact, banks all over the world rely on ML to evaluate the creditworthiness of loan applicants by analyzing their financial behavior and history.
Education
- Enhanced search and booking. ML improves search algorithms, making it easier for users to find relevant educational options quickly. For example, Evrlearn uses ML to provide users with the most relevant courses based on their interests and skill levels.
- Intelligent tutor apps. ML-powered tutors offer additional support to students outside the classroom by providing explanations, answering questions, and giving feedback on assignments. Thus, Carnegie Learning’s MATHia uses ML to provide personalized math tutoring, adjusting to each student’s needs.
- Enhanced LMS. ML personalizes course recommendations, tracks student progress, and provides insights into learning behaviors. Platforms like Moodle and Canvas integrate ML to offer personalized course suggestions and analytics that help educators understand student engagement and progress.
Travel
- Sentiment analysis. ML analyzes customer reviews and feedback to gauge sentiment, helping companies improve their services based on customer satisfaction levels. TripAdvisor uses sentiment analysis to aggregate user reviews and provide ratings and feedback summaries for hotels and attractions.
Trends in machine learning app development
Generative AI and LLMs continue to have a transformative impact across high-demand sectors, especially in information services, professional, scientific, and technical services, finance, and insurance. Due to the advancements and applications of ML, these fields are particularly lucrative for investment.
Notable examples of general-purpose apps using Generative AI and LLMs include Claude and ChatGPT. These solutions enhance data management and processing, enabling businesses to analyze vast amounts of data efficiently.
Generative AI and LLMs also find use in industry-specific applications. Thus, professional, scientific, and technical services rely on these technologies to automate complex tasks such as legal document review, scientific research analysis, and technical writing. These applications save time and reduce human error in these labor-intensive tasks.
The finance and insurance sectors are also experiencing a significant increase in the adoption of ML solutions. Applications include:
- Fraud detection: Analyzing transaction patterns to identify and prevent fraudulent activities.
- Risk assessment: Evaluating a wide range of data to assess financial risks and make informed decisions.
- Personalized financial advice: Providing tailored financial recommendations based on individual customer data.
- Automated customer support: Using chatbots and virtual assistants to handle customer inquiries and improve service efficiency.
Common challenges in machine learning app development
Data quality and quantity. To create a machine learning application, data science teams typically use over 100,000 data samples for training, and obtaining such large, relevant datasets can be costly for many proprietary companies. Even with sufficient data volume, ensuring it is directly relevant to the problem, complete, consistent, and free of noise demands significant time and resources.
Lack of computational resources. Training and running complex ML models require hardware that is 10 to 20 times more powerful than that used for traditional software development. This issue is especially critical for mobile devices due to their limited memory and storage capabilities. To address this problem, developers perform careful optimization, using special techniques to reduce the size of ML models and data.
Integrating the ML app with existing systems. Many organizations still rely on legacy systems not designed for modern ML workflows. Moreover, different software often returns data in various formats, necessitating a workflow to unify the data before it can be fed into an ML system. All these reworks are costly and require specialized skills.
Talent and expertise shortage. The demand for ML specialists has surged, yet the number of professionals has not kept pace. ML requires deep knowledge of mathematics, statistics, computer science, and domain-specific areas, making training new specialists a lengthy process. Additionally, the rapidly evolving field of machine learning app development challenges professionals to continuously update their skills. One popular solution to this problem is outsourcing ML engineers from a reliable digital transformation company.
The cost of machine learning app development
Depending on the specifics of your ML development project, costs can range from $150K to over a billion dollars. Key factors to consider when estimating the ML app development cost include:
- The specific ML model or algorithms used;
- The approach used to train the ML model;
- Availability and preparation of training data;
- Complexity and length of the exploratory stage;
- Infrastructure costs;
- Team salaries.
Let’s estimate the cost of an example project involving natural language processing. Suppose the development team consists of six members: two data scientists, two ML engineers, one NLP specialist, and one project manager. With datasets already available and prepared, and an estimated project duration of six months, we can calculate the total cost.
| Item | Cost for 1 month | Cost for 6 months |
|---|---|---|
| 2 Data Scientists | $6,000–$20,000 | |
| 2 ML Engineers | $7,000–$21,000 | |
| 1 NLP Specialist | $4,000–$12,000 | |
| 1 Project Manager | $2,500–$10,000 | |
| Total personnel cost | $117,000–$378,000 | |
| Cloud computing for training models (e.g., AWS, GCP, Azure) | $1,200–$5,000 | |
| Storage costs for datasets | $300–$500 | |
| Miscellaneous infrastructure costs (e.g., backups, networking) | $200–$500 | |
| Total infrastructure cost | $15,000–$36,000 | |
| ML frameworks and tools (e.g., TensorFlow, PyTorch, spaCy) | $0–$2,000 one-time | $0–$2,000 |
| Contingency fund | 10% of total costs for unexpected expenses | $13,200–$41,600 |
| Grand Total | $145,200–$457,600 |
How Syndicode can help you build a machine learning app
Syndicode is an end-to-end digital transformation partner and machine learning development company with over a decade of experience in custom development. We handle projects of varying complexity from scratch and provide outsource and outstaff services to companies looking to quickly scale their core teams with in-demand talent.
This way, we address one of the top critical challenges in machine learning app development: finding relevant talent.
As a turnkey ML app development services provider, we ensure our engineers have the necessary equipment and resources to deliver high-quality work. As an outsourcing provider, we help integrate our engineers into the client’s team to maximize productivity swiftly. We also foster a learning environment where our employees continually update their knowledge and skills.
By doing all that, we ensure that when you hire ML developers from Syndicode, you get professionals with proven expertise who are familiar with the latest practices and can choose the most efficient solutions for your problem.
Feel free to contact us with any questions. To stay updated on AI and ML development trends, follow the AI digest from our CEO on LinkedIn.
Frequently asked questions
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Which language is best for machine learning app development?
Python is widely regarded as the best language for machine learning app development due to its simplicity, readability, and extensive ecosystem of libraries, such as TensorFlow, PyTorch, and Scikit-learn. Java is another popular language for its performance and scalability in large-scale applications. C++ is also often chosen for performance-critical applications requiring high efficiency. Finally, R excels in statistical analysis and visualization.
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How to choose an ML algorithm for machine learning app development?
Choosing the right algorithm for machine learning app development depends on the problem type (classification, regression, clustering), the nature and volume of data, and computational constraints. For instance, Logistic Regression or SVM is often used for binary classification, and Random Forest or Gradient Boosting is often used for regression tasks. When choosing a suitable ML algorithm, consider model interpretability, required accuracy, and training time. It’s a good idea to experiment with multiple algorithms and validate performance using cross-validation to identify the best fit.
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What are the benefits of using machine learning for mobile apps?
Using ML in apps enables smarter, more responsive, and user-centric mobile applications, contributing to their success and user satisfaction. One example of ML in apps is personalized recommendations, such as suggesting products or content based on individual preferences. Creating mobile applications with ML can also improve efficiency by automating tasks like voice recognition and image classification, enabling real-time interactions. Then, ML enhances security with features like biometric authentication and fraud detection. Additionally, it provides predictive analytics, helping users make informed decisions. Integrating ML can differentiate apps in a competitive market, driving user engagement and retention.
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Why should you work with a software development company like Syndicode?
Working with a software development company like Syndicode offers numerous benefits, including access to a team of experienced professionals who bring a wealth of expertise and innovative solutions. Their comprehensive services cover the entire development lifecycle, ensuring a seamless process from concept to deployment. Syndicode’s focus on high-quality, scalable, and secure software solutions helps businesses achieve their goals efficiently. Moreover, by leveraging the latest technologies and methodologies, they deliver customized solutions that meet specific client needs. Additionally, their collaborative approach fosters strong partnerships, enabling clients to stay involved and informed throughout the project.
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How can I make my machine-learning app scalable?
Creating scalable machine learning applications starts at the design stage. First, aim to use modular architectures to separate different components. This way, they can be scaled independently. Second, opt for cloud services like AWS, Google Cloud, or Azure, which offer scalable infrastructure and managed services. Implement load balancing to distribute traffic efficiently and auto-scaling to adjust resources based on demand. Use distributed databases and data pipelines to optimize your data storage and processing. Remember to employ efficient algorithms and techniques like batch processing and caching. Finally, regularly monitor performance and implement CI/CD pipelines for continuous integration and deployment to ensure your app adapts smoothly to growing user needs.