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Services
MLOps
Productionizing machine learning is difficult. it requires stringent operational rigor to keep all
these processes synchronous and working in tandem. MLOps encompasses the
experimentation,
iteration, and continuous improvement of the machine learning lifecycle.
Manage all aspects of the model lifecycle centrally with data interactivity and visibility
Expedite the Data-to-Revenue cycle with the right mix of automation, re-usability and adept monitoring of projects
Enable improved focus for Data Scientists on model design and training with automation of repetitive tasks involved in preparing data
Derive the best from both tech and non-tech teams. Benefit with the flexibility to code or click-through to visually profiling data
Promote transparent, well -governed environments to enable all aspects of collaboration under a single roof.
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Iteratively explore, share, and prep data for the machine learning lifecycle by creating reproducible, editable, and shareable datasets, tables, and visualizations.
Use popular open source libraries such as scikit-learn and hyperopt to train and improve model performance. As a simpler alternative, use automated machine learning tools such as AutoML to automatically perform trial runs and create reviewable and deployable code.
Manage the frequency of model refresh, inference request times and similar production -specifics in testing and QA. Use CI/CD tools such as repos and orchestrators (borrowing devops principles) to automate the pre -production pipeline.
Iteratively transform, aggregate, and de-duplicate data to create refined features. Most importantly, make the features visible and shareable across data teams, leveraging a feature store.
Track model lineage, model versions, and manage model artifacts and transitions through their lifecycle. Discover, share, and collaborate across ML models with the help of an open source MLOps platform such as MLflow.
Automate permissions and cluster creation to productionize registered models. Enable REST API model endpoints. Create alerts and automation to take corrective action In case of model drift due to differences in training and inference data.
Cleansing, missing value imputations
Generating features with hot encoding on categorical variables
Saving different model runs, accuracies, predictions of each run
General parameters, booster parameters, learning task parameters
Achieving 80-90% accuracy with model governance, retraining
Pipeline configured for data,model and deployment
There are plethora of open source and paid ML tools available in market to implement MLOps in your p...
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In this article we will look at how we can setup and integrate Feast Feature Store with MLflow. We w...
Case Study
There’s no doubt that data-driven companies will lead the world. But to lead in our current times ...
