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The ANAI platform enables the accelerated delivery of your ML models to production in weeks versus years. Our solutions break down barriers for AI adoption, solve complex business problems, and accelerate the ideation to implementation cycle.
ANAI is built to significantly decrease the time it takes for building, testing and deploying ML models allowing businesses to focus more on business and customers, than complexities of model building.
ANAI engine’s data ingestion pipeline has 150+ data connectors, and if you can’t find one, let us know and we will work with you.
We help you focus on taking care of your business while we meet all your AI and ML needs, from data engineering and autoML to RAI and XAI.
From Data Engineering to model training & tuning, to deployment and monitoring in approx. 16 weeks*, helping us in Democratizing AI for everyone.
ANAI comes with 450+ unique AI and ML models, and more are constantly being added to offer speed, focus, and business value, as well as nimbleness and scalability.
Defining the problem andgenerating a hypothesis beforediving deeper with the machinelearning is an importantrequirement for success of all typesof ML project.
After knowing what you want, thenext step is sourcing the data thatwill be required for solving yourpredefined problem.
Selecting the right data and correctthe features will help building MLmodels that outperforms everythingelse. Building the right ML model canmake a difference between successand failure of the ML project as eachmodel has its own uniqueness.
After the model has been built andtrained, it needs to show prominenceon the data that it hasn’t seen before.
After selecting the best of the bestmachine learning model, it needs tobe deployed and managed. ANAI’sMLOps helps you in monitoring.
Along with building the best MLmodels which performs accurately inany situation, it should also be apriority or an obligation of the MLengineers to make the modelexplainable and responsible to itsusers.
Provides an end-to-end journey for your data to get cleaned, transformed, and standardized into usable formats for the ML pipeline.
Generate insights on your data, build/train ML models based on your use case, and get the most accurate models through hyper-tuning.
Continuously build and test your ML models even after deployment to keep the model accurate and functional towards its objective.
Generate trust in your AI systems by understanding the outcomes of your ML model and ensuring that the model is responsible, fair, and accountable.
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