What is ML manager service?
Previously, you have identified a business use case that requires you to create a machine learning model, and you have transformed your raw data into a consumable format for your machine learning algorithms. Because your goal is to predict breakdown probability, you will need to train a binary classification model. To do this you will need to perform the following tasks:
All these tasks can be performed using ML manager service that will allow you to build predictive models without coding. With this service, you can train and tune machine learning models, evaluate model performance, and deploy trained models into production. ML manager provides you the functionality to create multiple projects and perform multiple experiments using multiple algorithms.
How can ML manager service simplify your work?
ML manager simplifies your work by automating the model development processes under the hood. It takes care of your model validation, hyperparameter tuning, model selection, and model deployment without coding. This automation will shorten your development time, minimize errors, and speed-up your business time-to-value. In ML manager users can also collaborate in a project to improve productivity and minimize work redundancy.
ML Manager Service Capabilities in ZX AnalytiX
Fig 1. ML Experiments Features in ZX AnalytiX
How do you perform ML experiments?
To start your experiments, first you need to create a project workspace. This workspace will group all of your experiments and test runs in one place. On each experiment, you can select multiple machine learning algorithms to fit to your dataset. The detail experiments flow is as follows:
Create a new experiment inside the project workspace Fill-in some basic information of your experiment, i.e. name, description, dataset, and model types (regression, binary or multiclass classification).
Set your experiment configuration and parameters
Below is the visualization of the end-to-end structure we have created by far. From data integration to data pipeline, then ML pipeline (experimentation), and finally pipeline deployment.
Fig 2. ML Experiments Result in ZX AnalytiX