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Fiverr Pro
English
$
USD
I will mlops pipeline setup for ai and ml teams on any cloud
RAM G
About this gig
Your models work in notebooks. Getting them into production is a different problem and right now it's probably nobody's job.
I build the MLOps infrastructure that bridges that gap. From experiment tracking to automated retraining to production serving your team gets a repeatable, auditable ML pipeline that runs without manual intervention.
10+ years of DevOps and cloud infrastructure experience applied to ML workflows on AWS, GCP, and Azure. Here's what I set up:
- Pipeline orchestration Kubeflow / Airflow
- Experiment tracking MLflow / W&B
- Model registry & version control
- Automated retraining triggers
- Feature store integration
- Model serving Seldon / BentoML / TorchServe
- CI/CD for ML train, register, deploy
- Data versioning DVC / LakeFS
- Model monitoring & drift detection
- SageMaker / Vertex AI / Azure ML
Tell me your ML stack and where models are getting stuck I'll reply with a clear plan same day.
Get to know RAM G
RAM G
Secure, Scalable and Cloud Automated DevOps Solutions
- FromIndia
- Member sinceFeb 2026
- Avg. response time1 hour
- Last delivery1 month
Languages
English
I am a DevOps Engineer with 10 years of hands-on experience in a service-based IT company.
I have worked on enterprise-level applications and production environments.
My expertise includes:
✔ CI/CD pipelines (Jenkins, Git)
✔ Python, Linux & Shell scripting automation
✔ Deployment & release management
✔ Server setup & monitoring
✔ Cloud & DevOps best practices
✔ IAC (Ansible and Terraform)
I focus on reliable, secure, and well-documented solutions.
Client satisfaction and clear communication are my top priorities.
My Portfolio
FAQ
What's your MLOps experience specifically?
10+ years of DevOps and cloud infrastructure applied to ML workflows. I've implemented MLflow, Kubeflow, DVC, Seldon, and BentoML in production on AWS, GCP, and Azure — not tutorials, real systems.
Q2: My team has data scientists but no MLOps engineer — is this right?
Yes — that's exactly who I work with. I build the infrastructure so your data scientists can train, track, and deploy without needing to understand Kubernetes or CI/CD pipelines.
Q3: Which cloud providers do you support?
AWS (SageMaker, S3, ECR), GCP (Vertex AI, GCS, Artifact Registry), and Azure (Azure ML, AKS, Blob). Also self-hosted Kubernetes for teams that want full control.
Q4: Do you work with my existing training code?
Yes — no rewrites needed. I wrap your existing scripts in a pipeline framework, add tracking, and connect to a model registry. Changes to your code are minimal.
Q5: Can you set up model monitoring and drift detection?
Yes — included in Premium. I implement drift detection using Evidently AI or Alibi Detect, with alerts and optional auto-retraining triggers when model performance degrades.
Q6: Will we be able to maintain this after delivery?
Yes. I document everything, run a live walkthrough, and structure the system so your data scientists can add models and trigger retraining without needing an MLOps engineer on call.
