Incorporate 0ML
to build scalable ML
solutions quickly

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0ML platform is not a tool. It is the data scientist. Use this self-serve platform to create practical ML models capable of delivering promising results.
0ml

Businesses are unable to harness the actual power of machine learning technologies because of lack of knowledge workers – data science & cloud experts. Even if they manage to find the right resource, the process of developing and deploying models is lengthy. The data scientist has to rewire the codes, examine the output and restructure the algorithms continuously.

We present 0ML technology, a machine learning platform with one-click model training & deployment. Teams having zero AI knowledge and no cloud expertise, can save a ton of your time, money and effort.

Use Cases

Image Classification

0ml

Build models that interpret images and classify them into separate categories using computer vision algorithm

Object Detection

0ml
The tool helps you build an object detector that can pinpoint custom objects in images as well as videos.

Chatbot

0ml
No matter what your use-case, you can have real-world conversations going with chatbots.

Text Classification

0ml
Create models with text classifier. Machine can segregate text and extract meaning out of the dialogues using natural Language processing techniques.

Face Recognition

0ml
Create a solid face recognition model powered by computer vision technology. Be it detecting employees or identifying breaches, every use-case can be solved.

Structure Data

0ml
With structured data model classifier, you can filter information to target customers, spot unwanted behaviors and so on

How does it work?

Determining Use-case

The user has to simply identify the right use case and upload their annotated data to train the model. OML has domain specific pre-determined models

Dynamic Training

With the input data provided, the platform automatically creates the right environment for training the ML models.

Right Fitment

Different models are evaluated and the right one is selected automatically. The model is fine-tuned with statistical techniques

Model Deployment

The model is deployed on cloud and API details are provided for integration and use with existing systems.

Try Predictions

The user can test the model for predictions and classifications by using different inputs.

Retrain the model

The users can modulate and retune the model using predictors and more data sets to enhance its performance.

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