MLOps

Ensuring A Reliable, Efficient AI Model Development Lifecycle
EX Squared relies on its robust MLOps framework and process to ensure applications run as efficiently as possible throughout the AI lifecycle.

ML Development

  • Model Development
Our collaborative MLOps practice streamlines models from concept development through production launch. Using machine learning and AI development tools like MLflow and Weights & Biases, we track experiments and package code into reproducible runs. This helps to quickly identify and deploy the best-performing models with confidence.
  • Model Training
Evaluating and optimizing ML models is critical to ensure the highest levels of performance & accuracy. Automated pipelines and leading industry tools like KubeFlow help us conduct extensive experiments and track results in the most effective and scalable ways.

ML Deployment

  • CI/CD for Machine Learning
Continuous Integration & Delivery practices are essential at EX Squared. For MLOps, this includes data validation, model training and performance evaluation. Argo Workflows, Kubernetes, Jenkins, and other tools help automate code/model deployments and test runs. Our ML pipelines move new/changed models rapidly & reliably from Dev to Prod environments with more consistency and fewer errors.
  • Data Management
With two decades of experience, EX Squared uses time-tested versioning and QA processes to ensure datasets are always reliable and up-to-date.
  • Monitoring & Management
For greater accuracy, we track models’ performance to detect concept or data drift, and set notification alerts if anomalies or other deviations from expected behavior occur. KServe helps to serve up AI models predictably, and at scale.
  • Compliance & Governance
We implement tracking & versioning processes to ensure AI models adhere to regulatory, ethical and privacy requirements specific to your business. For high-transparency needs, we can also facilitate audit trails and model explainability.

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