CodeAssist converts raw data to business impact in minutes.
CodeAssist provides a low-code/no-code approach to build workflows through a drag and drop interface.
Choose your source data, specify what you want the pipeline to do and design simple workflows in minutes - no coding required!
Save time by automating the deployment of your machine learning pipeline so that you can focus on more important tasks.
The pipeline builder provides a visual interface for data pre-processing, model building and training.
CodeAssist makes it easy to collaborate with your team on the same project in real time.
Visualize data sets through interactive charts and graphs or export them as PDFs for presentations to clients or stakeholders.
CodeAssist is the first platform for building, training and deploying ML models in the fewest clicks possible.
We have an extensive library of code snippets to automate common ML tasks like data pre-processing, model selection, hyperparameter tuning etc..
Design powerful Machine Learning workflows visually without any coding required
CodeAssist supports low-cost training by optimising resources and optimises models to run on CPU so you don't have to worry about your cloud bills.
Save money with low cost, highly scalable end to end pipeline automation from preprocessing data to deploying models at scale.
Automatically create your pipeline using a point & click interface. Start modeling faster than ever before!
AI Practitioners spend hours on end manually setting up machine learning pipelines for every new project, wasting valuable time on hyperparameter tuning and creating needless risk of human error. CodeAssist does the heavy lifting for you by enabling data scientists to focus on their core competencies.
We understand that not everyone has the time or patience to learn about machine learning. You don't need to be an expert programmer or data scientist - just use our intuitive visual builder with a simple drag and drop interface to create custom solutions for specific problems.
Automate the process of building ML pipelines from pre-processing data to building, training and deploying models at scale.
Define the steps in your pipeline with an intuitive graphical interface and click on any step to learn more about it's parameters.
Once you've defined all of the steps in your ML pipeline, train a predictive model by clicking "Train Model". You can also upload new data for retraining or rerunning previous models.
Finally, deploy the trained model as a web service so it can be integrated into any application with just one line of code!