Hello 👋 Introduce the Data-Centric MLOps based data science platform, the DS2.ai solution.

DS2 is a data science platform that automates machine learning pipelines and prescriptive analytics.

• Manual & Auto Annotation Tools (Tableur, Text, Image, Recommeder system)

• ML & DL Training (Pytorch, Tensorflow, XGboost, etc)

• AI Aalytics (Prescriptive analysis and Data analytics with Metabase)

• AI Model Deployment and monitoring

• Use Active learning process

• API & Python SDK Support

Extended documentation for DS2

Prescriptive analysis is a statistical method that focuses on finding the necessary action or ideal method for a specific scenario based on data. In the case of data visualization, you have to make your own decisions after seeing the graph, but prescriptive analysis has a great advantage in the insight of actionable sentences that can follow the decision as it is.

Prescriptive analytics not only predicts what the future will look like, but also uses those predictions to take the best course of action for the future. The key to prescriptive analysis is to minimize human errors that can occur when making decisions and to increase the accuracy of decision-making in companies and projects.

A deep learning model for executing prescriptive analysis can increase its accuracy as it advances over time, and prescriptive analysis made with such increased accuracy will have higher accuracy. DS2 automatically installs a machine learning pipeline through the Active Learning process to [Learn - Analyze - Deploy - Collect] - [Relearn - Analyze - Deploy - Collect] - … Let us help you do it without much effort.

After labeling is complete, artificial intelligence can be developed using the learning data. Click the “Start AI Development” button on the dataset or labeling project screen to enter the setting screen for AI development. On the setting screen, three types of development environments are supported.

• Manual setting: Select the desired deep learning & machine learning library (Pytorch, Tensorflow, XGboost, etc.)
• Fast learning speed (AutoML): A function that creates a model by speeding up the learning rate among AutoML learning techniques
• High Accuracy (AutoML): A function that creates a model with high accuracy among AutoML learning techniques

After selecting the desired learning method, click the Start button on the right to start learning. If you click the "Model" tab at the bottom after starting, you can check the progress of the model being developed. It provides the function of "distribute" and the function of "analyzing" through the data set of the prediction result created by the model.

• Setting Training Options

• Confusion Matrix

• Feature Importance

Once the optimal model has been selected, prescriptive analysis can be run to gain insights from sentences built around explainable AI (XAI).

You can use the Deploy Model function by completing training through DS2.ai or uploading a model you have already created to DS2.ai. (The ability to upload models directly supports Pytorch and Tensorflow2 models.)

You can upload by clicking the "Deploy" menu button at the top or distribute the developed model through the "Deploy" function in the "Learning" menu. The deployed model can be managed through a separate endpoint, and the number of API calls can be monitored.

After the model is deployed, the input and output values used for prediction are automatically stored in the dataset, helping to quickly and easily create AI with higher accuracy through active learning.

Supports training data labeling tools needed to create artificial intelligence models. After clicking Labeling on the top menu, upload the dataset, and you can start labeling by selecting the desired function between manual labeling and auto-labeling tool.

One of the powerful features of DS2.ai is the manual setting function that can easily set up learning under various conditions to derive an optimal artificial intelligence model.

pip install ds2ai

After completing the installation of the ds2ai Python library, you can start learning using the example below.

import ds2ai
ds2 = ds2ai.DS2("your-app-code")

project = ds2.train(
    "BankMarketing.csv",
    option="custom",
    training_method="normal",
    value_for_predict="is_charge",
    algorithm="keras_ann",
    hyper_params={
      "layer_width": [20,3,5],
      "layer_deep": [3],
      "epochs": [10],
      "loss_function": ["mean_squared_error"],
      "optimizer": [
        {
          "clipvalue": 0.5,
          "learning_rate": 0.001,
          "beta_1": 0.9,
          "beta_2": 0.9999,
          "epsilon": None,
          "decay": 0,
          "amsgrad": False,
          "function_name": "Adam"
        }
      ],
      "activation": ["relu"],
      "batch_size": [32],
      "output_activation": ["relu"]
    }
)

You can check the app code by clicking the user name in the upper right corner. You can start learning with the code above after putting this app code as shown below.

ds2 = ds2ai.DS2("Your App code")

After the code is executed, the ability to predict or deploy the job situation and the trained model is available in ds2.ai as-is. For more information on how to use, refer to "SDK | PYTHON" in the left menu.

Each file included in this repository is licensed under the Apache License 2.0 BY License.

This project exists thanks to all the people who contribute.

Please read the contribution guidelines before submitting a pull request.