MLOps Services
Ship code and models with the same confidence.
OVERVIEW
Code and models are converging.
Most pipelines aren't.
Software delivery and model delivery are converging, but most teams are still running them on different muscle memory fast, automated pipelines for code, and manual, ad-hoc processes for models. That gap shows up as slow releases, configuration drift, environments that don’t match production, and models that perform well in a notebook and poorly in the wild. We close that gap by treating both as one delivery problem: version-controlled, automated, observable, and secure by default. The result is faster releases, fewer environment-related incidents, and models that ship as reliably as software.
Our Decade long experience, validated in numbers
AI Agent Live in Production
PO processing Time Reduction
Added Head Count as Scale
Agents Scaled from First Diployment
A general reference
One way to picture both pipelines.
An abstract reference architecture, not a diagram of any specific deployment the code path and the model path are usually separate until they converge at observability and release gating.
Trusted by Leading Enterprises
Our approach
How we think about building this.
01.
Architecture-first
Senior architects design the pipeline, platform, and operating model before a single tool is chosen.
02.
One delivery model, two payloads
Code and models move through the same discipline version control, testing, staged rollout, rollback.
03.
Secure by design
Zero-trust access, data isolation, audit trails, and compliance built into the foundation, everywhere.
04.
Start small, prove fast, scale
We land a high-value pipeline or use case, demonstrate impact quickly, then expand across the estate.
Platforms & tooling
Built on what your team already trusts.
Where these capabilities apply
Edge-to-Cloud AI across our platforms and services.
Faq
MLOps Services FAQs
What's the difference between MLOps and DevOps?
DevOps automates the path from code commit to production for software. MLOps applies that same discipline — version control, automated testing, staged rollout, rollback — to machine learning models, which also carry a data and training pipeline that traditional DevOps tooling doesn't account for. In practice, most teams need both running on a shared platform rather than as separate disciplines.
What does an MLOps engagement with XFactr typically include?
We start by mapping your existing model and data pipeline, then design a delivery architecture covering CI/CD for models, a model registry, automated retraining triggers, observability and drift detection, and staged release gates tied to your existing infrastructure (AWS, Azure, GCP, or on-prem/edge). We typically land one high-value pipeline first, prove impact, then expand.
How long does it take to see results?
Initial pipeline automation for a single model or use case is usually delivered in weeks, not months, since we start narrow and prove value before scaling. Full platform rollout across multiple models and teams depends on your existing infrastructure maturity.
Do you support LLMOps as well as traditional MLOps?
Yes. LLM-specific concerns — prompt versioning, evaluation pipelines, retrieval pipeline observability, and cost/latency monitoring — are handled alongside traditional ML model pipelines using tools like LangChain, LangSmith, and MLflow.
What platforms and tools do you work with?
Cloud: AWS, Azure, GCP. Infra: Terraform, Kubernetes, Docker. ML platform: MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML, Databricks. CI/CD: GitHub Actions, GitLab CI, Argo CD. Observability: Prometheus, Grafana, OpenTelemetry.
Why XFactr
Senior architects. Outcomes, not hours.
Senior architects not juniors lead every engagement. We sell outcomes, not hours. AI isn’t an add-on to how we run delivery; it’s engineered into the pipeline, the platform, and every system we ship. Fewer clients, maximum focus, enterprise-grade from day one.
Connect With us
Want to build as part of its DevOps and MLOps services?
Tell us about the Product, the protocols involved, and what you’re hoping to learn from the data.
We’ll think through the architecture with you on the first call.