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ANAI’s Data Engineering pipelines offer an end-to-end journey for the data to get processed, transformed, and made available for the data science part. The data goes through multiple stages from Data Wrangling to Exploratory Data Analysis and necessary steps are taken to convert it into a suitable form for easier handling by the ML model.
With ANAI you can select more than 150 data connectors to connect your data to the platform. You can select from a wide range of data connectors including the most frequently used in the industry such as MySQL, MongoDB, Elastic Search, etc., and even simpler ones such as Comma Separated Values files. This allows you the flexibility to bring in or connect your data to ANAI from any other data source.
ANAI conducts a pre-analysis of the data to understand the type of the data and finding relevant methodologies to process it further. This helps in the profiling of the data and becomes useful for the next steps within the pipeline.
ANAI allows you to conduct machine learning-based Data wrangling to remove any discrepancies within your data such as missing values and helps cleanse your data to make it usable for the ML model. This helps you to get accurate and actionable data that can be easily analyzed and used further down the line.
ANAI assists you in generating beautiful visualizations using a set of charts and diagrams and helps you in getting deeper insights into your data. With a few clicks, you are set to analyze the data and find interesting patterns within the data. Based on the features inside the data set you can generate all sorts of charts and/or select a few that you find insightful.
Selecting the right features from a data set is a very important part of the data science workflow to build projects that create an impact. Also, building out new features out of the existing ones also boosts the model performance by a lot. This is known as Feature Engineering and ANAI enables you to get the right features so that you get the best training data for your model.
ANAI helps you in detecting the anomalies found within your data set such as an outlier or a value which do not conform to an expected pattern. This is useful in determining frauds and also for medical diagnosis, etc. Detecting such anomalies can help your model to be more robust and resilient to any outliers. While analyzing the data, you can investigate such anomalies to find the problems and take care of them with the necessary steps.
ANAI gives out recommendations regarding the quality of the data and suggests proper steps which should be taken to make the data more standardized and appropriate for the ML pipeline.
Easily prepare the data by using ANAI’s Data Preparation pipeline that can be accessed by the easy-to-use UI and which helps users from other team members to citizen data scientists have the data prepared without much of a hassle.
Perform explorations, transformations, and data cleaning rapidly and have the data ready for the ML pipeline and for training machine learning models in the least amount of time to help you with faster testing and deployment.
Wrangle your data with an assist from ML tools that modify your data accordingly and have data which error-free, standardized, and functionally appropriate for the ML models to handle.
Keep the data security at the forefront by implementing end-to-end data governance using ANAI’s Data Preparation and ensure data transparency throughout the various stages of the data pipeline.
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