Exploring how Brainly leverages MLOps to create a cutting-edge visual search pipeline, optimising the user experience and driving innovation in the EdTech space
Machine Learning Operations (MLOps) is the discipline that brings together machine learning, DevOps, and data engineering to streamline the deployment, monitoring, and maintenance of machine learning models in production. MLOps have become increasingly crucial for businesses to remain competitive in today’s AI-driven landscape.
A recent blog post from neptune.ai, a leading experiment-tracking and model registry platform for production ML teams, explored how Brainly, a leading edtech platform, utilises MLOps to develop a state-of-the-art visual search pipeline that enhances user experience and fosters innovation.
Image source: neptune.ai
Brainly’s Visual Search: A Game-Changer in Education Technology
Brainly, a Poland-based, New York-headquartered online learning community with over 350 million monthly users, offers a platform for students to ask questions, collaborate, and solve problems together. Designed to enhance students’ abilities across various subjects like English, maths, science, and social studies, the platform relies on a peer-to-peer approach where learners can both ask and respond to questions. Top-notch answers enable students toearn higher ranks. Contributors are encouraged to give detailed explanations and cite their sources. Questions are sorted based on subject matter, country, and educational level. Upon signing up, each user receives a set number of points to ask questions, and they can accumulate more by responding to others’ inquiries. A leader-board highlights users who have either resolved the most queries or amassed the most points.
To improve the user experience and cater to the growing demand for visual content, Brainly developed a visual search feature. This cutting-edge functionality allows users to upload images of their questions, and the system then retrieves relevant answers from Brainly’s extensive database.
To build this robust visual search pipeline, Brainly employed MLOps best practices, including continuous integration and deployment (CI/CD), automated testing, and monitoring. These practices enabled Brainly to iterate quickly, ensuring that the visual search feature remained accurate, scalable, and efficient.
The MLOps Pipeline: Crafting a Visual Search Solution
Image source: neptune.ai
The development of Brainly’s visual search pipeline involved several steps, each of which was facilitated by MLOps principles:
- Data Collection and Annotation: Brainly collected a diverse set of images, including photos, screenshots, and scans, to train their machine learning models. They then used an annotation tool to label the images, ensuring that the models could accurately identify and categorise the visual content.
- Model Training and Validation: Brainly utilised a deep learning approach to train their models, employing convolutional neural networks (CNNs) to detect and extract features from the images. To validate the performance of these models, they employed several evaluation metrics, such as accuracy, precision, and recall.
- Model Deployment: Brainly utilised a CI/CD pipeline to automate the deployment process, enabling them to rapidly test and iterate on their models. This ensured that the visual search feature was continuously improved and updated with the latest advancements in machine learning.
- Model Monitoring and Maintenance: To maintain the performance and reliability of their visual search pipeline, Brainly established a monitoring system that tracked various metrics, such as latency, throughput, and error rates. By closely monitoring these metrics, they could quickly identify and address any issues that arose in production.
The Impact of MLOps on Brainly’s Visual Search
As illustrated by Brainly’s success in developing a visual search pipeline, MLOps is a vital component of modern education technology. By streamlining the deployment and maintenance of machine learning models, MLOps enables edtech companies to create innovative solutions that significantly enhance the learning experience for students.
Additionally, the implementation of MLOps best practices has enabled Brainly to stay ahead of the curve, as they can rapidly iterate on their models and incorporate the latest advancements in machine learning. This agility has allowed Brainly to maintain a competitive edge in the fast-paced edtech industry.
Looking forward, we can expect to see an increasing number of edtech companies embracing MLOps to drive innovation and deliver cutting-edge learning tools. From personalised learning experiences to advanced recommendation systems, the potential applications of MLOps in education are vast and transformative.
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