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BigTree108

MLOps services and consulting

MLOps services and consulting: machine learning platforms, CI/CD for models, experiment tracking and model registries, model serving and autoscaling, GPU infrastructure, drift monitoring, LLMOps for language model applications and model governance, with dedicated MLOps engineers for your team or an MLOps platform delivered end to end.

MLOps work we take on

  • Machine learning platform set-up

    Azure Machine Learning, SageMaker AI, Gemini Enterprise Agent Platform or Databricks, or Kubeflow and MLflow on your own Kubernetes cluster, defined in Terraform with separate development, staging and production environments and access through your identity provider.

  • Experiment tracking and model registry

    Runs, parameters, metrics and artefacts logged in MLflow 3 or Weights & Biases, and every model registered with the code, data and parameters that produced it, then promoted to production through a recorded approval rather than a file copied by hand.

  • CI/CD for machine learning

    Training code linted and tested on every pull request, data and model checks run in GitHub Actions, Azure DevOps or GitLab CI, and a new model released only when it beats the current one on the agreed evaluation set.

  • Inference endpoints and autoscaling

    Real-time and batch inference on KServe, Ray Serve, BentoML, SageMaker AI endpoints or Azure Machine Learning online endpoints, scaled to zero when a model is idle, released through canary and shadow deployments, with latency and cost per thousand predictions measured.

  • Model monitoring and drift alerts

    Input drift, prediction drift and live accuracy against delayed ground truth tracked in Evidently, Arize or your Grafana stack, alerts routed to the team that owns the model, and retraining triggered by an agreed threshold instead of a calendar.

  • GPU infrastructure and cost

    GPU node pools on AKS, EKS or GKE with the NVIDIA GPU Operator, training jobs queued in Kueue or Ray, spot capacity for runs that resume from checkpoints, and inference costs cut with quantisation, batching and right-sized instances.

  • Feature store set-up

    Feast, Databricks feature tables or the feature store in SageMaker AI, so training and live predictions read features computed by the same code, with point-in-time joins for training sets and low-latency lookups for serving.

  • LLMOps

    Prompts and retrieval settings versioned like code, each release gated by an evaluation run, traces in MLflow or Langfuse, a gateway such as LiteLLM for routing, rate limits and cost per team, and open-weight models served with vLLM on KServe.

  • Model governance and audit trail

    Model cards, lineage from source data to deployment, approval records and access logs kept in the registry, in the form the documentation duties of ISO/IEC 42001 and the EU AI Act ask for, with personal data in training sets tracked so it can be removed on request.

Hire MLOps engineers

  • Dedicated MLOps engineers

    MLOps engineers who join your team full time, work in your tools and process, and report to your lead. You interview them; we carry the Ukrainian contract, payroll, invoicing and leave.

  • MLOps projects

    A defined piece of MLOps with a scope, a fixed plan and a named lead on our side who owns the result and reports progress in your channels.

  • Ongoing MLOps

    MLOps as a continuing service: the same people every month, a backlog you prioritise, and hours you can see in our portal and on the invoice.

The dedicated team page explains how specialists join your team, and the outsourcing page covers project delivery, take-overs and how we charge.

Who works on your MLOps

  • MLOps engineers

    Pipelines, registries, serving and monitoring

  • Machine learning engineers

    The models and evaluations the pipelines run

  • Cloud and DevOps engineers

    Kubernetes, GPUs, networking and Terraform

  • Data engineers

    Feature pipelines and training data

  • Python engineers

    APIs and services around the models

Models that reach production and stay there

Most MLOps work is engineering around the model: training that can be repeated from code and data versions, a registry with lineage, releases gated by evaluation, and monitoring that tells the owner when accuracy slips. An existing set-up is first reviewed read-only, and you get a written list of gaps in order of risk before anything changes.

The platform is code like any other: infrastructure in Terraform, pipelines in Git, CI that signs in to the cloud with federated credentials rather than stored keys, secrets in a vault and dependencies kept on current releases, so a model’s path to production is the same every time and can be audited afterwards.

Other data and AI services we provide

Data and AI overview

Questions about MLOps

Which industries do your MLOps engineers work in?

AI and data products, where many models are trained and released each week; fintech, for fraud and credit models that have to stand up to audit; e-commerce and retail, for recommendation, search and forecasting models; healthcare, for models that need traceable training data; advertising and marketing technology, for bidding and audience models retrained daily; and enterprise SaaS, for machine learning features shipped inside the product.

Do we need Kubeflow, or is a managed platform enough?

A managed platform such as Azure Machine Learning, SageMaker AI, Gemini Enterprise Agent Platform or Databricks covers most teams with a handful of models and no wish to run clusters. Kubeflow, KServe and MLflow on Kubernetes pay off when you run many models, need the same platform in several clouds, or want direct control over GPU cost.

Can you take models from our data scientists’ notebooks to production?

Yes. The notebook code becomes a tested training pipeline, the model is registered with its data and parameters, served behind an endpoint or as a batch job, and monitored, while your data scientists keep working the way they do today.

How quickly can MLOps engineers start?

When the right MLOps engineer is available, the start is gated only by your interview and the NDA and IP assignment. Otherwise we run a search, which typically produces candidate profiles within two to three weeks, and nobody starts until you have said yes.

How do we hire MLOps engineers through BigTree108?

Tell us the work, the seniority and the hours you need. We propose one or two people with their profiles, you interview them the way you would interview your own hire, and you sign one agreement with BIG TREE 108 LLC and receive one invoice a month.

Who owns the work they produce?

You do. Every specialist has a signed contract with BigTree108 that assigns all work product to the company, and our agreement with you assigns it onward. Code, designs and documents are delivered into your own repositories and tools, not kept where only we can change them.

Need your models to reach production reliably?

Tell us how many models you run, where they are trained and how they reach production today. You get an answer within one business day: a review plan, candidate profiles, or a first list of gaps to close.

Email hello@bigtree108.com How we respond