Adaptive ML models

Machine learning service automatically tuned to your data.

Why you need
adaptive ML models

Toloka's collection of pre-trained models is available via API
and can automatically adapt to your data streams

Explore our models
  • Drastically cut time to production for your ML solutions —
    without investing in infrastructure
  • Easily deploy intelligent services with automated model
    monitoring and regular retraining
  • Automate a typical ML development cycle in just 4 steps
  • Get better accuracy from the first iteration by starting with
    pretrained models

How adaptive models work

Skip the repetitive steps in the ML lifecycle — let our automated service handle model tuning, evaluation, deployment, and monitoring.
Our adaptive ML models are backed by Toloka expertise in crowd science and machine learning for high quality and throughput.

  • Step 1. Choose a model
    Select a pre-trained model from our catalog that fits your task and type of data. Or contact us to get help selecting the right model to start with
  • Step 2. Create a project
    Our service will set up the model that you liked for your private use as a remote endpoint on a reliable cloud infrastructure
  • Step 3. Start using the model
    Integrate the model into your projects via API and start receiving real-time model-generated labels for your data streams
  • Step 4. Let Toloka monitor and update your model
    Our monitoring service checks model quality and performance. Re-training is automatic when needed to prevent model drift and keep the model in sync with your data streams

Adaptive ML models to get your project going

Use our pre-trained models out of the box or adapt them to your data streams automatically.

Explore other models available to you on our model podium at Toloka ML Platform
Explore more models

Benefits for the entire ML lifecycle

Toloka's adaptive machine learning models give you everything you need to move your projects to production.

  • Reliable accuracy

    Benefit from background human-in-the-loop processes that keep model accuracy stable over time

  • Continuous optimization and retraining

    Model evaluation and maintenance use HITL processes for model retraining and updates

  • No infrastructure needed

    Easily deploy intelligent services without investing in infrastructure and tedious ML experimentation processes. Available via API with low latency for model predictions

  • Ground truth datasets within easy reach

    Integration with the Toloka data labeling platform lets you build ground truth datasets to use for model tuning and measuring model performance during production

Need a custom solution tailored to your needs?
Talk to us

Discover how adaptive models
can make an impact

Learn how solutions using adaptive machine learning models impact
social media monitoring (SMM) for a large IT corporation.

Want to see results like this? We can help you find the right solution
Let us show you how

Next-level automation: MLOps with the Toloka ML platform

Build your own ML pipeline on the Toloka ML platform with support for the full ML lifecycle:

  • experiment tracking
  • dataset versioning including EDA tools
  • model management with versioning and tagging
  • visualizations, reports and diff tools
  • Python API for access from any environment

Take advantage of native integrations of tracking metrics, Toloka data labeling, and pre-trained models.

Explore the Toloka ML platform

FAQ about
ML models