What is Octai?

No Fuss, No Muss, No Code.

One platform to easily solve all your data problems. No code is required.

Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on developing algorithms and statistical models that enable computers to learn and improve their performance on tasks without being explicitly programmed. In other words, machine learning allows computer systems to identify patterns, make decisions, and generate predictions based on historical data.

The process of machine learning typically involves the following steps:

  1. Data collection: Gather relevant data that can be used to train and evaluate the machine learning models. This data can come from various sources like databases, sensors, or user interactions.
  2. Data preprocessing: Clean, normalize, and transform the data to ensure it is in the appropriate format for analysis. This may include handling missing values, scaling numerical features, or encoding categorical variables.
  3. Feature engineering: Identify and create the most relevant features (attributes or variables) that can be used to represent the data and improve model performance.
  4. Model selection: Choose the most suitable machine learning algorithm for the specific problem being addressed. There are various algorithms available, such as linear regression, decision trees, and neural networks, each with its own strengths and weaknesses.
  5. Model training: Train the selected algorithm on the preprocessed data. During this process, the model "learns" from the data by adjusting its internal parameters to minimize the error in its predictions.
  6. Model evaluation: Test the performance of the trained model on a separate dataset not used during training. This helps assess the model's ability to generalize to unseen data and helps prevent overfitting.
  7. Model deployment: Integrate the trained model into applications, services, or processes to make predictions or decisions based on new, unseen data.

Machine learning has a wide range of applications across various industries, including finance, healthcare, marketing, and transportation. Some common examples of machine learning applications are fraud detection, image recognition, recommendation systems, and natural language processing.

87% of ML projects never make it into production.

What is Octai?

Octai can help you decrease that business risk, which enables new data scientists to onboard quickly and allows even no-coders and domain experts to solve data problems effectively.

Octai enables you to:

  • Import your data from databases, cloud resources, local files, or data marketplaces
  • Transform, join, enrich, and clean your data; use feature engineering methods, and create model-ready datasets without coding
  • Experiment, analyze, and train ML models in one click.
  • Deploy and monitor models into a production environment to create real-world, data-driven solutions.
ML Studio Workflow

Octai Workflow

The completed work and the know-how always stay with the team. There is no need to begin the projects from scratch when the situation is changed. Just build your structure and let Octai manage the whole process automatically.

How does Octai work?

At Octai, we understand that not everyone has a background in coding or programming. That's why we've created a user-friendly drag & drop canvas for both technical and non-technical people, enabling you to perform complex data operations with ease. You can select from a range of pre-built blocks to perform tasks such as data cleaning, and feature engineering, or create your own custom blocks to suit your unique needs. With Octai's drag & drop canvas, you can take control of your data without needing to learn complex programming languages or syntax.

Here's a general overview of how drag-and-drop tools work in Octai:

  1. Access the platform: Start by accessing the Octai.
ML Studio

ML Studio

  1. Import data: Drag the necessary data import component onto the canvas. This component allows you to load your dataset, which can be in various formats (e.g., CSV, Excel, or JSON), or connect to a database.


  1. Data preprocessing: Drag and drop the components needed for data cleaning, transformation, and feature engineering onto the canvas. Connect these components to the data import component to apply the operations to your dataset. Examples of these components include missing value imputation.


  1. Model selection: Choose the machine learning algorithm you want to use and drag the corresponding component onto the canvas. Examples of algorithms include linear regression, decision trees, etc. Connect the preprocessed data to the input of the chosen algorithm component.


  1. Model training: Connect the algorithm component to the model training component. This step configures the model and trains it using the preprocessed data.
  2. Model evaluation: Drag and drop components to evaluate the model's performance, such as accuracy, precision, recall, or F1 score. Connect the trained model and the test dataset to these evaluation components.
  3. Deployment: Octai offers components for model deployment, enabling you to create APIs, integrate the model into a web service, or export the model for use in other applications.
  4. Execute the workflow: Once you've set up the entire workflow, execute it by running the process. The platform will process the data, train the model, and evaluate its performance based on the components and connections you've set up.
  5. Bonus: Templates With Octai's templates, you can kick-start your projects by using our off-the-shelf solutions. Our templates guide you through the process of building a data pipeline and creating a predictive model, making it easy for anyone to get started. Whether you're in the energy industry, finance, healthcare, or retail, we have a template for you.


Drag-and-drop tools in Octai make it easier for users to build and deploy machine learning models without needing in-depth knowledge of programming languages or libraries. However, it's important to note that having a foundational understanding of machine learning concepts will still be beneficial in building effective models and interpreting the results.