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MLOps & AI operations

Keep AI systems reliable in production

We help you deploy, monitor and improve AI systems, from predictive models to generative AI applications, with controls for performance, cost and maintainability.

State of Data+AI Report, Databricks 2024

“MLOps makes companies 3x more efficient
at deploying models into production”

Tools & partners

MLOPS

Challenges we solve

Cloud Migration & Optimization

Move ML and AI workloads to platforms like GCP and Vertex AI and tune the infrastructure behind them, reducing complexity and keeping compute costs under control.

Streamlined Model Deployment

We automate data preparation, training, evaluation and deployment in a single workflow, so new model versions reach production in a repeatable way.

Scalable & Reliable Solutions

We set up monitoring and observability for models and generative AI applications, with alerts, retraining and serving practices that keep performance and latency in check.

MLOps playbook for leaders: our roadmap to perational excellence in AI

Our MLOps &
LLMOps services

We operate the full lifecycle of ML models and generative AI applications, from deployment and monitoring to evaluation and governance.

Model Migration & Modernization

Migrate existing ML models and AI systems to scalable cloud platforms. We assess legacy systems, optimize models and re-architect the pipelines around them.

AI Operations Optimization

We assess how your AI systems are deployed and run today, select and integrate the right tools, and execute PoCs with your team to close operational gaps.

LLMOps Specialization

Run large language model applications in production, with evaluation of output quality, prompt and model versioning, and tracking of cost and latency per use case.

Monitoring & Governance

Monitoring and observability for model performance, drift, bias and resource usage. Governance processes that define ownership, approvals and controls for responsible AI in production.

MLOps Center of Excellence

Define shared MLOps standards, tooling and operational responsibilities so teams can deploy and maintain models consistently across projects.

Streamlined ML Project Lifecycle

Automate the ML lifecycle with CI/CD for training, retraining, evaluation and deployment, so updates ship through a consistent, tested process.

LLMOPS

Specialized expertise for LLMs

Large language model (LLM) applications bring their own operational challenges: evaluating outputs, managing prompts and models, and controlling cost and latency. Our LLMOps services cover deployment, evaluation and monitoring for generative AI in production.

Scale your AI initiatives
with MLOps

Case study

Airline MLOps
migration to Vertex AI

A major airline faced scalability and cost challenges with their MLOps platform.

We migrated their operations to Vertex AI, creating a scalable deployment pipeline. This included organizing model registries and implementing advanced CI/CD practices.

In partnership with

Case study

Retail efficiency with a custom MLOps platform

A luxury marketplace aimed to enhance its analytics but faced inefficiencies in model deployment and management.

We built a tailored MLOps platform, integrating pipelines for preprocessing, training, deployment, and evaluation.

Case study

Transforming retail Ops with Databricks & GCP

A luxury retailer serving 160 million customers struggled with outdated processes and isolated workflows.

We leveraged MLOps to transform their operations, implementing robust version control and optimizing infrastructure with Databricks on GCP, enhancing adaptability and efficiency.