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ML Data Preparation
The process of converting raw data into dataset of features and labels for training, testing and implementations of algorithm.
Data Preparation for Machine Learning
Importance of ML Data preparation
Data Preparation is a supervised task in Machine Learning, as it requires inputs like feature maps, labels, input constraints, etc. The main work in Data Preparation is to construct and design a data type, which can be used in various algorithms like Data mining, Data classification, and Data cleansing. These data types are the critical building blocks of diverse Machine Learning Algorithms like principal component analysis, neural networks, supervised learning, artificial intelligence, decision trees, etc. Data can also be used in different stages of an algorithm, like training, pre-training, post-trained, and benchmarks.
Foiwe manages all critical steps to provide high quality dataset for machine learning models to perform efficiently. We work on both dataset and data model to complement each other and to ensure the model performs to expectations.
Applications and Capabilities
Data Tagging also helps in machine learning, and in particular, it is helpful for large machine learning tasks, which in turn helps in providing better quality solutions to problems faced by users.
Applications
- Processing Big Data
- AI Systems
- Ecommerce platforms
Capabilities
- Cleaner and structured data stream
- Capable to handle large data volumes
- Multilingual team
- Experienced Staffs for greater output
Benefits of Data Preparation
Technology is evolving everyday and technocrats must stay up to date with changing trends in Big Data and AI. Foiwe’s Data Preparation solutions helps in gaining edge at a fraction of the cost
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