You will get CI/CD for Ai/ML Model Pipelines with a single click
Project details
A fast, reliable “push‑to‑prod” system that lets your data‑science team ship new models with a single git command—no late‑night SSH sessions, no surprise outages using Dockerized notebook, single-node Kubernetes/Fargate service, health checks, request logging.
Machine Learning Tools
Amazon SageMaker, Apache Spark, Azure Machine Learning, ChatGPT, Databricks Platform, Databricks MLflow, deeplearn.js, GitHub Copilot, Kubeflow, MATLAB, Python, PyTorch, SQL, TensorFlow, Vertex AI, XGBoostWhat's included $500
These options are included with the project scope.
$500
- Delivery Time 5 days
- Number of Revisions 2
- Number of Model Variations 1
- Number of Scenarios 1
- Number of Graphs/Charts 1
- Model Validation/Testing
- Model Documentation
- Data Source Connectivity
- Source Code
Optional add-ons
You can add these on the next page.
Fast 2 Days Delivery
+$200
Additional Revision
+$50About Bimal
Certified DevOps - Automation, CI/CD, AWS, Kubernetes, Docker
Ahmedabad, India - 4:43 am local time
* Amazon Web Services (AWS) | Experience with EC2, S3, VPC, IAM, RDS, Lambda, CloudFormation, CloudWatch, and more.
* Microsoft Azure | Knowledge of Azure App Services, Virtual Machines, Azure Kubernetes Service (AKS), Azure DevOps, and ARM templates.
* Google Cloud Platform (GCP) | Proficiency in Compute Engine, Cloud Functions, Kubernetes Engine (GKE), Cloud Build, and Cloud Deployment Manager.
* Multi-cloud Environments | Experience with managing applications and services across multiple cloud platforms (AWS + Azure + GCP)
Infrastructure as Code (IaC)
* CloudFormation (AWS) | Proficiency in defining and deploying infrastructure using AWS CloudFormation templates.
* Azure Resource Manager (ARM) | Expertise in using ARM templates to manage and deploy resources in Azure.
Containerization & Orchestration
* Docker | Building, testing, and deploying applications using Docker containers.
* Kubernetes | Expertise in deploying, managing, and scaling containerized applications using Kubernetes (including services like AWS EKS, GKE, and Azure AKS).
* Docker Swarm | For smaller-scale container orchestration solutions.
CI/CD (Continuous Integration and Continuous Deployment)
* Jenkins | Setup and management of Jenkins pipelines for automated deployments and testing.
* GitLab/GitHub CI Automating build, test, and deployment pipelines using GitLab/GitHub CI/CD.
* AWS CodePipeline / CodeBuild | Expertise in leveraging AWS-native CI/CD services for seamless integration and deployment.
Monitoring & Logging
* Prometheus & Grafana | Monitoring and alerting systems, with Grafana for visualizing cloud metrics.
* ELK Stack (Elasticsearch, Logstash, Kibana) | Setup and management of the ELK stack for log aggregation and real-time analytics.
* AWS CloudWatch | Monitoring AWS infrastructure and setting up alarms, metrics, and logs.
* Datadog / New Relic | Advanced cloud application monitoring, tracing, and performance metrics.
* Azure Monitor | Monitoring Azure cloud infrastructure and application performance.
Serverless Computing
* AWS Lambda | Expertise in designing and deploying serverless applications using AWS Lambda.
* Azure Functions | Serverless compute for event-driven applications in Azure.
* Google Cloud Functions | Managing serverless workloads with GCP's event-driven compute.
Cloud Cost Optimization
* Cost Management in AWS, Azure, GCP | Proficiency in cloud cost estimation, tracking, and optimization tools.
* Right-Sizing Infrastructure | Ensuring resources are efficiently sized to meet performance requirements while minimizing cost.
* Spot Instances & Reserved Instances | Utilizing cloud cost-saving strategies like AWS EC2 Spot Instances or Azure Reserved VM Instances.
Cloud Migration & Hybrid Cloud Solutions
* Cloud Migration Strategy | Expertise in migrating on-premise systems to the cloud, including lift-and-shift, re-platforming, and cloud-native re-architecting.
* Hybrid Cloud Architecture | Designing hybrid cloud solutions to integrate on-premise infrastructure with public cloud environments.
Security & Compliance (DevSecOps)
* IAM (Identity and Access Management) | Designing and managing secure access policies for AWS, Azure, and GCP resources.
* Security Automation | Integrating security tools like AWS Inspector, Azure Security Center, and automated vulnerability scanning into CI/CD pipelines.
* Cloud Security Best Practices | Knowledge of securing cloud-native environments (e.g., network security, firewalls, encryption at rest/in transit).
Cloud Networking & Load Balancing
* AWS VPC & Subnet Design | Designing and managing cloud networks using VPCs, subnets, security groups, and routing tables.
* Azure Virtual Network (VNet) | Configuring Azure VNets, subnets, and network security groups for secure cloud environments.
* GCP Virtual Private Cloud (VPC) | Managing networking and security rules for cloud applications on GCP.
* Cloud Load Balancers | Configuring load balancing services like AWS Elastic Load Balancing (ELB), Azure Load Balancer, or Google Cloud Load Balancing to distribute traffic across instances.
Disaster Recovery & High Availability
* Cloud-Based Disaster Recovery | Design and implement automated disaster recovery (DR) strategies for cloud workloads (e.g., using AWS Backup, Azure Site Recovery).
* High Availability Architecture | Architecting highly available cloud solutions using auto-scaling, load balancing, and replication strategies.
Steps for completing your project
After purchasing the project, send requirements so Bimal can start the project.
Delivery time starts when Bimal receives requirements from you.
Bimal works on your project following the steps below.
Revisions may occur after the delivery date.
Ready‑to‑run CI/CD pipeline in your repo
Dockerfile and Kubernetes manifest file or Helm chart (or Cloud Run YAML)