Your ML models need more than training. We cover the full operations stack, from strategy through production monitoring, so your team ships models that stay reliable at scale.
Lufthansa needed to classify and route thousands of customer documents faster. We built an NLP pipeline using transformer models and NER that automated categorization and slashed onboarding time.
Customer onboarding dropped from 4-5 months to 1 month, while cloud infrastructure costs fell by 25%.
90% of ML failures come from poor productization, not model quality (McKinsey). Most teams build great models and then stall at deployment. We build the pipeline, monitoring, and retraining infrastructure that close that gap.
Getting ML models into production is an infrastructure and process problem, not just a modeling problem. You need a partner who builds the pipelines, runs the monitoring, and treats every deployment with the same rigor as your production software releases.
Your industry shapes your MLOps requirements. Fraud models in fintech retrain on different schedules than demand forecasting in retail. We build pipelines tuned to your data volume, compliance rules, and model risk profile.
Manufacturing and Industrial IoT
Every engagement follows the same six-phase process. You get a reproducible pipeline with version control, automated testing, and monitoring baked in from day one.
Whether you need a quick proof-of-concept or a full enterprise MLOps build, we have an engagement model that fits your stage. Start small and scale when you are ready.
These results come from published client engagements where BigOhTech built and deployed AI/ML systems in production. Every metric below is sourced from our portfolio.
Building MLOps in-house means hiring 3-5 specialized roles you may not retain. Freelance ML engineers can build a pipeline but rarely stick around to monitor it. We give you the full team, the process, and the long-term support in one engagement.
Your MLOps architecture depends on where your models run, how fast they need to respond, and what compliance rules you follow. We build for all of these scenarios.
Your MLOps stack needs to fit your cloud, your team's skill set, and your scale requirements. We work with these tools daily across training, deployment, monitoring, and data infrastructure.
We deliver MLOps on these platforms and ecosystems. Your pipeline runs on the tools your team already trusts, with our engineering depth behind the integration and operations.
We build MLOps pipelines that handle training, deployment, monitoring, and retraining at enterprise scale, on AWS, Azure, GCP, or your own infrastructure. What happens when ML teams skip the operations layer?
These are the engineering capabilities behind every MLOps engagement. Each one exists to solve a specific production ML problem, from reproducibility to real-time serving under load.
MLOps consulting covers the infrastructure, pipelines, and processes needed to get ML models into production and keep them running reliably. At BigOhTech, that includes strategy audits, CI/CD pipeline builds, model monitoring, drift management, and ongoing platform support.
DevOps automates code deployment. MLOps extends that to handle data versioning, experiment tracking, model validation, and drift monitoring, which standard CI/CD pipelines do not cover. We build the ML-specific layers on top of your existing DevOps tooling.
Our engineers join your tools (Jira, Slack, GitHub) and attend your standups. We work as an extension of your team during the build, then hand over full documentation and run enablement sessions so your internal team owns the pipeline going forward.
Yes. We run side-by-side migrations where your existing models keep serving production traffic while we build the new pipeline around them. Post-migration, we validate model outputs against your baseline metrics before cutting over.
Yes. We offer a 40-hour pilot where we scope a single pipeline or infra audit before any long-term commitment. You evaluate our work on a fixed scope with no contract obligation before deciding on a full build.
Every engagement runs under ISO 27001 certified security governance with NDA signed before we access any data. Access controls, encryption at rest and in transit, and audit logging are standard on all MLOps projects we deliver.
We configure statistical drift monitors that compare incoming data distributions against your training baseline. When drift crosses a threshold you set, the system triggers automated retraining, validates the retrained model, and promotes it to production if it passes.
A single pipeline (training through deployment) typically takes 4-8 weeks depending on your data infrastructure and cloud setup. Complex multi-model systems with monitoring and retraining take longer, but you will see your first pipeline deployed within the first engagement sprint.
We have delivered AI/ML and data infrastructure projects across fintech, healthcare, aviation, retail, telecom, and energy. Each industry has different compliance and data handling requirements, and our pipelines are configured accordingly.
We build on AWS SageMaker, Azure ML, GCP Vertex AI, Databricks, and self-managed Kubernetes. If you run a multi-cloud or hybrid setup, we design your MLOps pipelines to work across providers without locking you into one.
Share your project goals, timeline, and technical requirements. We'll review your requirements and send a tailored solution with an indicative estimate within 48 business hours.
