Introduction
The past decade has seen a remarkable upsurge in the application of machine learning techniques. These techniques are increasingly being adopted across a vast array of domains, such as research, education, environment, social science, businesses, and more. Incorporating machine learning into existing systems is not just an IT – related update; it’s a comprehensive business initiative. It can open up new horizons, identify problem areas, optimize workflows, explore new markets, and enhance customer services. However, for those without a specific computer science background, leveraging machine learning in their fields can be challenging. This article presents a case study on using a no – code platform to design a machine – learning solution.
Learning Outcomes
Understand the growth and impact of machine learning applications in various domains. Identify the challenges in traditional machine learning implementations and the role of no – code platforms. Learn about the key features and benefits of no – code machine learning platforms. Gain insights into the practical applications of no – code platforms through a detailed use case. Explore the steps to implement machine learning solutions using Python and no – code platforms.
Challenges with Conventional Implementation
Machine learning systems are inherently complex. Designing and coding a machine learning application through traditional methods is a labor – intensive and costly process. In – house development of customized data analysis products faces challenges like hiring qualified professionals, setting up hardware, and licensing software, along with a time – consuming development lifecycle. Citizen developers and programmers are moving away from this coding – heavy approach, seeking tools with simple user interfaces, drag – and – drop functionality, forms, or wizard – like features. Finding the right team of experts is also a significant hurdle. Traditional ML implementations rely on experts like data scientists or analysts who must use programming languages to code, deploy, and generate results. There is a shortage of ML experts with good coding skills in the market, leading businesses to seek alternatives.
An ML expert should have a good understanding of data analysis, machine learning algorithms, and coding. However, they may be experts in their domain but not in business solutions, creating a gap between expectations and results. A typical machine learning workflow involves data cleaning, preparation, model selection, training, testing, hyper – parameter tuning, and report generation or prediction. Implementing this requires a solid understanding of computer programming, mathematics, and statistics.
Potential Solution: No – Code Platform
No – code platforms have emerged to address these challenges and empower non – CS professionals. These are automatic machine – learning tools that can deliver quick results, especially for time – sensitive projects with limited resources. Unlike traditional programming, which demands extensive language skills, no – code platforms allow individuals with limited programming knowledge to design applications tailored to their needs. For example, Shopify enables business owners to launch online stores without building a website from the ground up, saving time and effort. Gartner predicts that by 2024, 80% of technology services and products will be built outside IT departments, making no – code platforms crucial for millions of businesses. User – friendly, automated ML platforms simplify the analytic and coding process, allowing anyone to develop customized products without conventional programming.
No – Code Platform Features
A no – code platform should have a user – friendly interface for creating a machine – learning system without coding. It should automate data ingestion and support multiple formats. It should also automate data preprocessing, including handling missing data, redundancy, or imbalance, with data visualization. The platform should offer a wide range of models and recipes for analysis, with automated training, testing, and validation. It can compare the performance of different models and rank them. The performance output is displayed on a dashboard using standard metrics. The models can be auto – scaled and are production – ready. It should also facilitate hyper – parameter auto – tuning and continuous model performance monitoring.
Use Case
Mammalian fully – grown oocytes are classified as Surrounded Nucleolus (SN) or Not Surrounded Nucleolus (NSN) based on their chromatin configuration after staining. We have a dataset of mouse oocyte images for this classification, which is a machine – learning classification problem. The dataset can be found at https://figshare.com/articles/dataset/Orange – Image – Analytics/9632276?file=17282204.
Here is a Python program to achieve this classification:
Step 1: Load Data Set and Pre – process. Load images for both SN and NSN from the given directory and convert them to arrays.
Step 2: Image embedding. Create and extract embeddings (vectors) of images using Google’s Inception V3.
Step 3: Calculate Distance. Calculate the pairwise distance between the vectors of images using the euclidean distance method.
Step 4: Apply Multidimensional Scaling. Convert the results into 2D using the dimension – reduction technique MDS to gain insights into the images.
Step 5: Visualization. Create a 2D scatter graph to show the classification of images with annotations.
Analysts without a Python background can also analyze the images using no – code platforms like Orange, an AutoML platform for data analysis and prediction.
Conclusion
No – code machine learning platforms are emerging as SaaS platforms, providing infrastructure and access to advanced functionalities. They offer advantages like easy upgradation, flexibility, and scalability. They democratize access to ML, streamline the development process, save time and costs, and support a wide range of applications across industries. However, they may have limitations in customization and performance for highly complex tasks.
Key Takeaways
No – code machine learning platforms make ML accessible to non – programmers. They simplify the ML development process compared to traditional methods. They offer user – friendly interfaces and automated features. They are applicable across diverse industries but may have limitations for complex tasks.
Frequently Asked Questions
Q1. What are no – code machine learning platforms? A. They allow users to build and deploy machine learning models without writing code. Q2. What are the main benefits of using no – code platforms? A. They simplify development, save time, reduce costs, and make ML accessible to non – programmers. Q3. Can no – code platforms handle complex ML models? A. Yes, they support various ML models and can automate processes like data preprocessing and model training. Q4. Are no – code platforms suitable for all types of businesses? A. Yes, they can be used across diverse domains, including healthcare, finance, and retail.
