Advanced machine learning techniques streamline data table processing as part of an end-to-end product. Neural networks identify data types, detect anomalies, and classify columns for a smooth automated migration. Real-time. Accurate. Fast.
About the client
A rapidly growing tech startup offering data analytics needs only advanced solutions, seeking NN technologies to streamline and automate the client data table transformations.
About the
Product:
The service of NN-powered data table column classification with anomaly detection formed a part of a major data migration product. This product was designed to handle complex datasets as part of the automated migration process.
As a result, real-time data recognition and classification have enhanced analytics and supported decision-making, meanwhile anomaly detection has ensured data integrity across diverse data types.
Introduction:
The product is intended to smooth any data migration process, reducing errors in joining tables and analyzing unprepared datasets. Product key features include:
The solution processes vast amounts of structured and unstructured data, allowing businesses to make informed, data-driven decisions while reducing errors and manual oversight.
One machine learning expert
Challenges:
In building an effective solution, we faced several key challenges:
Python, Keras, Pandas library, Scikit-learn, NLTK (Natural Language Toolkit)
Solution:
A deep learning model based on Keras, utilizing a multi-layer perceptron architecture with three hidden layers, was trained on 100,000 pre-labeled columns to classify data types. The preprocessing pipeline leveraged Pandas for data normalization, encoding categorical variables, and standardizing date formats to ensure consistent model inputs.
Furthermore, to eliminate any manual labelling, we implemented an Isolation Forest model for anomaly detection. This unsupervised learning algorithm automatically establishes normal data patterns from the existing dataset, enabling it to identify outliers without requiring pre-labeled training data.
Eventually, an automated pipeline for data processing streamlined the entire process. However, we had to manually refine the logic, increasing the system’s accuracy and reliability.
Results:
The implementation of our neural network-based column classification and anomaly detection solution delivered both operational efficiency and enhanced data accuracy to the client’s database migration solution.
Sum Up:
Our solution focused on automating the classification of data columns and detecting anomalies in real-time, ensuring a streamlined and efficient data management process according to the client’s demands. By incorporating advanced neural networks and AI-driven processes, the product has ensured that data sets are both accurate and actionable, driving efficiency and operational excellence.
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