MLOps Platform

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Out of the box spins up a full-fledged ML development environment with all the tools you need at your fingertips. If you don’t see something you want in the table, you will probably find it here: Awesome Production Machine Learning. Let us know, and we’ll add it to your deployment.

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MLOps Tools

ML project lifecycle component
  • 1. Data labeling
  • 2. Data management
  • 3. Development environment
  • 4. Remote debugging
  • 5. Code management
  • 6. Experiment tracking
  • 7. Hyperparameter tuning
  • 8. Distributed training
  • 9. Metadata management
  • Label Studio
  • DVC
  • VSCode and Jupyter
  • VSCode remote debugger
  • Git
  • MLflow
  • NNI
  • MLflow
ML project lifecycle component
  • 10. Model management
  • 11. Deployment
  • 12. Testing
  • 13. Monitoring
  • 14. Interpretation
  • 15. Pipelines orchestration
  • 16. Resource orchestration
  • 17. Access control orchestration
  • MLflow
  • Seldon Core
  • Locust
  • Prometheus + Grafana
  • Seldon Alibi
  • can save a team of five data scientists over $100,000 per year:


All tools are open-source
All tools are open-source
All tools are installed in the same cluster
All tools are installed in the same cluster
CV and NLP projects on Python
CV and NLP projects on Python
AWS, GCP, or Azure
AWS, GCP, or Azure
mlops folder

Streamline Data Management

Data preparation — collection, consolidation, cleansing, and labeling — can eat up to 80% of your productive time. With, you do the experimentation, while we handle the mundane.
Connect all the necessary data sources (cloud or on-premises) and have data pipelines for automatic extraction or batch fetching in a suitable format set up for you. When configuring your ML environment, we make sure that all incoming data gets automatically validated against the set parameters, and then transferred to a centralized repository.
Benefit from access ready-to-use feature sets for model training, re-training, and validation.

How MLOps works?

Optimize Infrastructure Management

Can’t find the right balance between underpowering and overpowering your machine learning projects? We can help figure out when, where, and how to deploy ML applications at the minimal cost for the maximum gains.
Your custom-built platform provides complete visibility into models’ GPU/CPU usage across and nodes and clusters. So that you can continuously optimize job scheduling and resource allocation.
We also keep the data-hungry models at bay, while ensuring that other ML pipelines get access to the right amount of storage they need at the optimal speed your networks can muster.

Productizing ML and DL is a collective-action problem that MLOps platforms attempt to solve

ML and DL models burn in production because some of us are better at training algorithms to interact well with one another than getting along with other team members.
Communicating the breadth and depth of model deployment requirements over and over again can be daunting at times. An MLOps platform introduces CI/CD best practices and promotes knowledge sharing between the data scientists and Ops specialists. With collaborative workspace, version control, and model registration, developers can better pass critical knowledge to the operations teams.
Model management and monitoring tools, in turn, help the Ops people stay abreast of the models’ performance, accuracy, and resource usage and report any deviations rapidly back to the development team to drive continuous improvements.

Deploy ML models with confidence and scale your AI capabilities without any constrains

Unlike other proprietary MLOps platforms places no constraints on how you can deploy your models. Rely on containers or serve your models as API services using the framework you prefer — Flask, Spring, or TensorFlow.js.
Got another approach? We accommodate that too.
At any rate, we’d help you set up semi-automated pipelines for risk-averse model deployments and make sure that your models can be easily integrated with or within other apps.  In the same vein, we help you stay flexible with your choice of supporting libraries, tools, notebooks, or cloud computing resources.
We can add, upgrade, or replace any component of your MLOps platform as per your latest needs to ensure that your team has access to the exact resources they need for the upcoming project.