DevOps & MLOps Services.

Benefit from Continuous IT and Software Delivery Without Breaking the Bank on In-House Expertise.

Streamline the software development life cycle through continuous delivery, agile development, and operational best practices with CapeStart DevOps and MLOps services. Stay lean and razor-focused on your core business while automating infrastructure and application delivery,  increasing efficiency, and lowering costs.

Increase Innovation. Lower Time to Market.

Accelerate your business with CapeStart’s integrated, in-house team of DevOps/MLOps architects and site reliability, security, automation, and cloud engineers. Your unique, dedicated CapeStart DevOps/MLOps team can design and manage cloud services, configure deployment pipelines, monitor system and application performance, and orchestrate patch and configuration management.

 

CapeStart’s DevOps support team is available 24/7, 365 days a year, so we’ve always got your back when you need help the most.

What’s the CapeStart Difference?

Customized DevOps/MLOps Services

Customized DevOps/MLOps Services

CapeStart’s experienced team works with all sizes of organizations. We’ll assign a dedicated DevOps team to your account who will analyze requirements and develop an effective, on-budget execution plan.

24-7 DevOps/MLOps Support

24-7 DevOps/MLOps Support

Our 24-7, 365 remote technical support team is available via collaboration apps such as Slack, Skype, MS Teams, and WhatsApp, email, or phone.

Up-to-the-Minute Reporting

Up-to-the-Minute Reporting

We provide full reporting services on a variety of metrics including change success rate, ticket volume trend, infrastructure stability, first call resolution rate, cost per ticket, and infrastructure utilization rate.

Cost Effective, Remote Solutions

Cost Effective, Remote Solutions

CapeStart’s integrated, in-house team uses field-tested methodologies and processes proven to optimize costs while delivering services remotely and securely.

What We Deliver.

Cloud Architecture and Migrations.

  • Secure, HIPAA and SOC 2 Type II-compliant environments with scalable, highly available, fault-tolerant databases in private subnets 
  • Design and management of cloud services such as Azure, AWS, Google Cloud, and Digital Ocean; migration of databases and applications to cloud
  • Configuration of deployment pipelines using Jenkins, CircleCI, Github Actions, Azure DevOps, and AWS CodePipeline
  • Creation and configuration of resources for staging, user acceptance testing (UAT), and production 
  • Infrastructure-as-code delivery through Terraform and other tools
  • Pipeline versioning, workflow orchestration, and model serving using Kubeflow, Airflow and other tools 

Cloud Monitoring, Maintenance, and Cost Optimization.

  • Configuration and management of application performance monitoring tools including Nagios, Kibana APM, PagerDuty, Grafana, Instrumental, Scalyr, and New Relic
  • Server OS and software updates, auto-deployment management, service creation, and management
  • Patch and configuration management; Dockerization of applications; Kubernetes clustering

Data Security, Infrastructure Hardening, and Compliance.

  • Configuration of AWS Web Application Firewall (WAF) and other security tools, configuration of security alerts, vulnerability assessments 
  • Logging of all actions performed in the cloud for security and compliance audit purposes 
  • Backup and DR services including cross-region replication and geo-redundancy, along with determining an acceptable recovery time objective (RTO) and recovery point objective (RPO)

MLOps.

  • CapeStart’s MLOps team includes ML engineers, data scientists, data engineers, and software engineers 
  • Continuous input stream data collection, data ingestion and preparation for downstream ML applications, and data analysis/curation 
  • Continuous delivery of ML models to production requires building pipelines to automate workflows for data validation, data preprocessing (feature stores), model training, model versioning, and model serving.
  • ML system validation and deployment to production, ML model evaluation, and model training

Expert DevOps/MLOps Consulting.

  • Auditing of existing environments, roadmap and budget creation to optimize costs and improve performance
  • Development of system architecture and cloud services purchasing decision plans
  • Technology integration and migration planning
  • Audit and compliance planning

DevOps/MLOps Platforms.

google-cloud
microsoft azure
jenkins
circleci
aws code pipeline
ansible
terraform
docker
kubernetes
selenium
maven
gradle
nagios
elastic apm
grafana
scalyr
uptimerobot
nessus
veracode
snyk
kubeflow
flow
amazon sagemaker
azure machine learning
github

Contact Us.