Mariko is a Principal Applied Scientist at Microsoft who leads the development of agentic AI workflows for cybersecurity operations. Her work centers on using large language models and agentic systems to bring frontier AI research into practical products and operational environments.
## Professional Role
- Principal Applied Scientist at Microsoft.
- Leads agentic AI workflow development for cybersecurity operations.
## Research and Technical Focus
- LLM-powered systems.
- Agentic workflows.
- Applying frontier AI research to real-world products and operations.
Overall, Mariko’s work connects advanced AI research with practical cybersecurity and enterprise applications.
GitLab has launched a global Managed Service Provider (MSP) Partner Program for qualified providers to deliver GitLab as a fully managed DevSecOps service. The program combines financial incentives, technical enablement, marketing support, and a structured onboarding process. It aims to help customers adopt and operate GitLab while enabling MSPs to build recurring, services-led revenue.
## Why the Program Matters
- Many organizations lack the resources to deploy, administer, migrate, and continuously optimize a DevSecOps platform.
- MSP partners can manage GitLab’s operational needs while development teams focus on building and delivering software.
- The program provides formal requirements, enablement, dedicated support, and financial benefits for MSPs worldwide.
- It addresses common customer challenges such as complex migrations, fragmented toolchains, and expanding security requirements.
## Benefits for MSP Partners
- Partners receive standard GitLab margins plus an additional MSP premium on transactions, new business, and renewals.
- MSPs retain all service fees from deployment, migration, training, enablement, and consulting.
- Quarterly technical bootcamps cover releases, new features, best practices, roadmap updates, and peer experiences.
- Recommended certifications include AWS Solutions Architect Associate and GCP Associate Cloud Engineer.
- Go-to-market resources include:
- A GitLab Certified MSP Partner badge
- Co-brandable marketing assets
- Eligibility for joint customer case studies
- Partner Locator placement
- Marketing Development Funds for qualified campaigns
## Customer Experience
Customers receive a structured and repeatable managed DevSecOps service, including:
- Documented implementation and migration methodologies
- Platform deployment, administration, and ongoing support
- Regular business reviews
- Defined response and escalation procedures
- Continuous platform optimization handled by the MSP
## Supporting AI Adoption
GitLab MSPs can help organizations introduce AI-assisted development through the GitLab Duo Agent Platform.
- MSPs can provide governance and operational guidance for AI adoption.
- Customers can test AI workflows in controlled environments.
- Managed services can help address data residency, compliance, and scaling requirements.
- This approach reduces the burden on internal teams while enabling broader adoption of agentic AI.
## Who Should Apply
The program is designed for MSPs that:
- Already manage cloud, infrastructure, or application operations
- Want to expand into DevSecOps services
- Have, or plan to develop, relevant technical expertise
- Prefer long-term customer relationships and recurring services revenue
- Are existing GitLab Select or Professional Services Partners seeking a repeatable managed offering
## How to Get Started
- Confirm business and technical eligibility using the program handbook.
- Apply through the GitLab Partner Portal with supporting documentation.
- Complete a structured 90-day onboarding process covering contracts, technical training, sales enablement, and an initial customer engagement.
- Package the managed service, define SLAs, and launch the offering.
- Applications are reviewed in approximately three business days.
For MSPs with the necessary operational capabilities, the program offers a formal path to build a recurring GitLab managed services practice while helping customers adopt DevSecOps and AI more effectively.
AWS Lambda durable functions introduce a simplified way to manage complex, long-running workflows directly within the standard Lambda experience. By utilizing a checkpoint and replay mechanism, developers can now write sequential code for multi-step processes that automatically handle state management and retries without the need for external orchestration services. This feature significantly reduces the cost of long-running tasks by allowing functions to suspend execution for up to one year without incurring compute charges during idle periods.
### Durable Execution Mechanism
* The system uses a "durable execution" model based on checkpointing and replay to maintain state across function restarts.
* When a function is interrupted or resumes from a pause, Lambda re-executes the handler from the beginning but skips already-completed operations by referencing saved checkpoints.
* This architecture ensures that business logic remains resilient to failures and can survive execution environment recycles.
* The execution state can be maintained for extended periods, supporting workflows that require human intervention or long-duration external processes.
### Programming Primitives and SDK
* The feature requires the inclusion of a new open-source durable execution SDK in the function code.
* **Steps:** The `context.step()` method defines specific blocks of logic that the system checkpoints and automatically retries upon failure.
* **Wait:** The `context.wait()` primitive allows the function to terminate and release compute resources while waiting for a specified duration, resuming only when the time elapses.
* **Callbacks:** Developers can use `create_callback()` to pause execution until an external event, such as an API response or a manual approval, is received.
* **Advanced Control:** The SDK includes `wait_for_condition()` for polling external statuses and `parallel()` or `map()` operations for managing concurrent execution paths.
### Configuration and Setup
* Durable execution must be enabled at the time of the Lambda function's creation; it cannot be retroactively enabled for existing functions.
* Once enabled, the function maintains the same event handler structure and service integrations as a standard Lambda function.
* The environment is specifically optimized for high-reliability use cases like payment processing, AI agent orchestration, and complex order management.
AWS Lambda durable functions represent a major shift for developers who need the power of stateful orchestration but prefer to keep their logic within a single code-based environment. It is highly recommended for building AI workflows and multi-step business processes where state persistence and cost-efficiency are critical requirements.
