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ANAI’s AutoML provides you with complete support for your data science workloads with a no code approach from the data ingestion and pre-processing to model deployment.
There is an increase in demand for quality data scientists everywhere, but a shortage has been seen and it will continue to rise, as per some studies. AutoML helps in building a team of Citizen Data Scientists who empower your organization through Machine Learning with the knowledge just for a particular use case.
AutoML platforms such as ANAI, handle your entire data science requirements without the need for a huge data science team.
ANAI’s over 150 plus data ingestion methods allow for integrating data sets from any data source and conducting pre-processing based on the type of data automatically. ANAI also helps understand the quality of the data by giving it a health score so that you can detect any issues within the data right at the start of a data science project.
ANAI assists you in building quick visualizations based on the type of data and the types of features it has. It gives a quick summary and insights from the data within a few clicks and performs exploratory data analysis with ease.
ANAI can produce a wide range of charts to get quick and insightful visualizations based on your particular use case.
Choosing the best ML model for your data science use case can be a challenging task and a decision that makes your project a success or a failure. ANAI’s over 450+ unique ML models give you the choice to select the right model based on your use case.
ANAI can also automatically train the right set of ML models on your data and give you the best-performing model to deploy in production.
ANAI automatically selects the best hyperparameters for an ML model and gives out a trained model that is robust and accurate for the real world. It runs through hundreds of hyperparameters to conduct hyperparameter tuning that improves the performance of the model and validates it so that you receive a model that can be trusted and deployed with ease into the real world.
ANAI also allows you to get valid explanations of the model outcome using various XAI (explainable AI) techniques such as K-LIME and SHAP. These explanations will help you understand a model more deeply and build more trust in the model. This will provide transparency into your ML models and can help build user trust in your AI-based systems. ANAI’s XAI-based solutions can aid you in building AI systems that are more reliable, responsible, and fair.