Strategy
Recommend strategies for model governance, monitoring, and automation required for ongoing reliability and value.
Engineering
Provide guidance on developing frameworks to instill responsible and compliant processes.
Change Management
Formulate plans to evolve skills and culture needed to adopt practices organization-wide.
Data Ops
Assess data infrastructure and workflows required for high quality, well-governed training data pipelines.
Cloud Platform Expertise
Leverage deep experience with leading cloud platforms to architect optimized MLOps environments.
Our R&D team evolves our AI/MLOps capabilities by exploring advances in model deployment, monitoring, explainability, and automation.
Automated ML pipelines
Enable CI/CD for machine learning workflows to increase efficiency and consistency of model development.
Model performance monitoring
Continuously monitor model performance on new data, trigger alerts for degradation, and enable rapid retraining.
MLOps platform engineering
Architect MLOps technology stacks on cloud leveraging services for automation, reproducibility, collaboration.
Anomaly detection
Detects distribution data and model inputs that may indicate anomalies or data quality issues.
Risk mitigation
Quantify ecosystem dependencies and failure risks across operationalized ML systems.
Effective MLOps requires cross-disciplinary expertise encompassing data, ML engineering, infrastructure, and monitoring. Our team leverages proven tools and techniques to make models reliably operational.
Our long-standing partnerships with some of the best-known cloud service providers has enabled us to evolve a standardized, stable and tested approach to machine learning and AI model deployment, monitoring and performance analysis.
Our AIOps and MLOps teams have years of experience across multiple tech domains, including both embedded and distributed systems, providing
clean and compliant services and pipelines for both your data preparation and transformation and model training and deployment.
Automate claims processing
Operationalize ML models trained to accurately evaluate insurance claims and optimize outcomes.
Improve predictive maintenance
Monitor telemetry from embedded models in equipment to identify degradation over time.
Refresh fraud detection models
Retrain financial fraud classifiers on new data to maintain detection rates in a dynamic environment.
Govern clinical decision models
Ensure healthcare models meet evolving compliance needs for validated, explainable recommendations.
Detect abusive user accounts
Continuously tune models identifying fake and malicious account profiles based on new patterns.
Monitor predictive demand forecasting
Receive alerts for anomalies in retail demand forecasts that may indicate model or data issues.
Co-creating the world we want to live in
From today's trendsetting brands to the startups shaping tomorrow, we collaborate with organizations we respect and applaud. Over two decades, our commitment has been to jointly shape a more open, resilient, and optimistic world alongside our clients.
