GETTING STARTED

The purpose of this guide is to illustrate the main features that ml-dsl provides. It assumes a very basic working knowledge of machine learning practices (data processing, fitting, predicting, etc.).

Please refer to our installation instructions for installing ml-dsl.

As a rule, the standard working process of a data scientist includes such steps as data processing, training, deployment and evaluation models. Sometimes the resources of a desktop/laptop are enough for execution. But in some cases the more resources are needed. This case data specialist has resources to cloud platforms. A lot of additional knowledge is necessary: preparation and deployment of code to cloud platforms, familiarity with some SDK cloud libraries or command-line tools.

The main idea of ml-dsl is to simplify this process for data specialists. Ml-dsl lets submit jobs and run your code on cloud platforms from a jupyter notebook.

Let’s see an example. We have a movie review dataset and want to build a model for classification the reviews as positive or negative. We are going to build a simple LSTM network. Text sample of movie review: