"MLOps, or DevOps for Machine Learning, streamlines the end-to-end machine learning lifecycle, blending the principles of DevOps, continuous integration, and continuous delivery with machine learning. It aims to automate the end-to-end process of deploying machine learning solutions into production, emphasizing reproducibility, automation, and quality."

Amol Malpani (CTO, Cloudaeon)

Key Use Cases
  • Model Scalability: Scaling models to handle large-scale predictions.
  • Quick Iterations: Quickly iterating through different versions of models.
  • Production Monitoring: Monitoring model performance in real-world scenarios.
  • Model Retraining: Keeping models up-to-date with new data.
  • Compliance and Auditability: Meeting regulatory requirements in industries like finance and healthcare.
Key Benefits
  • Model Decay: Addressing the natural deterioration of model performance over time.
  • Complex Deployments: Streamlining the otherwise complex deployment of ML models into production.
  • Inconsistencies: Eliminating discrepancies between development and production environments.
  • Manual Monitoring: Automating performance monitoring and error detection.
  • Reproducibility: Ensuring experiments and models can be recreated and validated.
What We Offer
  • Version Control: Storing versions of datasets, model parameters, and code.
  • Model Building and Training: Tools and platforms for model development.
  • Continuous Integration & Continuous Delivery (CI/CD): Automating testing and deployment pipelines for ML models.
  • Model Serving and Deployment: Serving models in a way they can be utilized by other services or applications.
  • Model Monitoring: Watching the model's performance and health in real-time.
  • Model Retraining and Fine-tuning: Periodically retraining the model with new data.
  • Model Governance and Auditability: Ensuring models are compliant and can be audited for decisions.
How We Work
  • Define the Objective: Understand the problem you're trying to solve with machine learning.
  • Data Collection & Versioning: Gather the required data and version it to keep track of changes.
  • Model Development: Experiment with different algorithms, architectures, and hyperparameters.
  • Continuous Integration: Test new changes and models automatically for robustness and performance.
  • Model Deployment (Continuous Delivery): Push the trained model into production, making it available for making predictions.
  • Monitoring: Constantly monitor the model's performance in the production environment.
  • Feedback Loop: Gather feedback on model predictions to refine and improve.
  • Retraining: Use new data and feedback to periodically retrain the model.
  • Documentation & Compliance: Maintain detailed documentation for transparency and auditability.

Managed Services

  • MLOps Platform Management: Setting up and managing MLOps platforms/tools like MLflow, Kubeflow, or TFX.
  • Continuous Integration & Delivery Setup: Implementing CI/CD pipelines tailored for ML workflows.
  • Model Monitoring Services: Setting up real-time monitoring tools to track model performance.
  • Model Deployment and Scaling Solutions: Assisting in deploying models in diverse environments, from edge devices to cloud infrastructures.
  • Training and Consultation: Guiding teams on best practices in MLOps, model management, and deployment strategies.
  • Security & Compliance: Ensuring the end-to-end ML workflow meets industry standards and regulations.
  • Versioning and Data Management: Implementing tools and practices to version datasets, models, and code.
  • Retraining Workflows: Setting up automated retraining pipelines to keep models updated.

Readiness Check

In 10 minutes, get a score to assess your Readiness & Maturity. You'll get a clear score to help your identify areas of improvement.

Getting Started

If you are ready to engage with us and would like do dive deeper into the subject, go ahead and book in a Discovery Workshop with our Practice Leads.