You will get Build & Automate MLOps Pipeline using AWS SageMaker
Project details
I create production-ready MLOps pipelines on AWS SageMaker with CI/CD, Docker, and automated monitoring — helping you deploy, scale, and track ML models reliably and efficiently.
Machine Learning Tools
Amazon SageMaker, Azure Machine Learning, BERT, MATLAB, PyTorch, R, RapidMiner, TensorFlowWhat's included
| Service Tiers |
Starter
$200
|
Standard
$350
|
Advanced
$600
|
|---|---|---|---|
| Delivery Time | 4 days | 6 days | 10 days |
Number of Revisions | 1 | 2 | 2 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 1 | 2 | 0 |
Model Validation/Testing | - | - | - |
Model Documentation | - | - | - |
Data Source Connectivity | - | - | - |
Source Code | - | - | - |
About Chidiebube
Senior MLOps & DevOps Engineer | AWS, Kubernetes, Jenkins, Terraform
Toronto, Canada - 9:04 am local time
tech expertise, I bring a wealth of hands-on experience optimizing
software development processes. Currently contributing my skills
and knowledge to Nokia, I've honed my craft within dynamic, highimpact
environments.
My career spans various industries, allowing me to fine-tune
my skills across software development, system operations, and
automation. Proficient in implementing cutting-edge DevOps
methodologies and tools, I've worked with renowned industry names,
leveraging my technical prowess to drive innovation and efficiency.
Key skills in my arsenal include proficiency in cloud technologies
(AWS, Azure), containerization (Docker, Kubernetes), continuous
integration/continuous deployment (CI/CD), infrastructure as
code (IaC), and orchestration tools. I thrive on architecting robust,
scalable solutions, integrating security, and streamlining deployment
pipelines.
Why clients hire me:
6+ years delivering real business value through automation and scalability
Clear documentation and transparent communication
Proven results across enterprise and startup environments
Let’s make your infrastructure fast, reliable, and production-ready.
Message me — I can start immediately or schedule a quick call to discuss your goals.
Steps for completing your project
After purchasing the project, send requirements so Chidiebube can start the project.
Delivery time starts when Chidiebube receives requirements from you.
Chidiebube works on your project following the steps below.
Revisions may occur after the delivery date.
Project Planning & Setup
I will Review your dataset/model, define the architecture, set up the AWS SageMaker environment, and prepare necessary IAM roles and permissions.
Model Deployment & CI/CD
I will thenDeploy the model on SageMaker, configure the CI/CD pipeline using GitHub Actions, and Dockerize the model for reproducibility.