Speed is Everything!
Fast feedback loops are the foundation of productive development. Every second counts in the build-test-deploy cycle. In AI/ML development, fragmented toolchains turn what should be quick iterations into slow, frustrating processes.
The Hidden Cost of Fragmentation
In AI/ML development, the path from idea to production is littered with friction points that kill developer velocity. Unlike traditional software development, AI projects have unique characteristics that lead to natural fragmentation.
Consider this: your team is building a computer vision model. You need to:
- Set up different compute environments for training vs inference
- Manage multiple orchestration layers (Jenkins, GitLab CI/CD, Kubernetes)
- Handle various workload characteristics and hardware requirements
- Navigate complex artifact management across different platforms
- Deal with inconsistent observability and debugging tools
Each of these steps introduces delays, context switching, and potential failure points. The result? What should be a fast iteration cycle becomes a slow, frustrating process.
Why AI/ML Development is Different
AI/ML development naturally leads to fragmentation. Unlike traditional software development, AI projects have unique requirements:
1. Diverse Compute Requirements
Different stages of your ML pipeline need different hardware:
- Data preprocessing: CPU-intensive, high memory requirements
- Model training: GPU-intensive, specialized hardware (A100, H100)
- Inference: Optimized for latency, potentially edge devices
- Batch processing: Cost-optimized, can tolerate interruptions
2. Multiple Orchestration Patterns
Teams end up with a patchwork of tools, each with trade-offs:
GitLab + Jenkins
Direct hardware access and fine-grained control, but requires complex custom scripts and high maintenance overhead.
GitLab Native
Simple and maintainable approach, but limited AI-optimized hardware availability and no specialized job scheduling.
GitHub Native
Supports OSS but limited hardware support matrix. Good for basic workflows but lacks specialized AI/ML infrastructure.
Hybrid Approaches
Maximum flexibility for each specific task, but creates the highest maintenance burden and complexity.
3. Artifact Management Chaos
AI/ML projects generate diverse artifacts that require different handling:
- Training datasets: Terabytes of data requiring specialized storage
- Model checkpoints: Large binary files with versioning needs
- Docker images: Multi-gigabyte containers with ML frameworks
- Experiment logs: Extensive metrics and performance data
- Inference artifacts: Optimized models for production deployment
"It takes sooo many clicks through Jenkins to get to the output of a CI test failure. CI job definitions live in external infra repos. As an engineer working on our ML pipeline, if I wanted to add additional tests to our CI, or run our existing tests on a new hardware target, it would be near impossible for me to self-serve this change."
The Real Impact on Developer Velocity
These fragmentation challenges manifest in concrete productivity losses:
Slow Feedback Loops
- Long build times: Multi-architecture builds taking 3-4 hours
- Resource contention: Waiting 15-20 minutes for GPU availability
- Inefficient resource usage: Long-running jobs monopolizing expensive hardware
- Manual intervention: Infrastructure teams frequently needing to intervene
Context Switching Overhead
- Multiple interfaces: Switching between GitLab, Jenkins, Kubernetes dashboards
- Inconsistent tooling: Different debugging approaches for each platform
- Knowledge silos: Specialized expertise required for each tool in the stack
- Configuration drift: Settings scattered across multiple systems
Operational Complexity
- Debugging difficulties: Limited visibility into distributed systems
- Maintenance burden: Custom scripts and integrations requiring constant updates
- Security gaps: Inconsistent security policies across different tools
- Compliance challenges: Difficulty maintaining audit trails across fragmented systems
"Setting CI on new clusters: significant delays between engineering onboarding and CI enablement on new clusters. The complexity makes it difficult for teams to be self-sufficient."
How PloyD Eliminates Fragmentation
PloyD's approach is fundamentally different. Instead of adding another tool to your stack, we provide a unified platform that handles the complexity behind the scenes while giving you the control you need.
Velocity Metrics That Matter
PloyD build/edit/test cycles are measured in seconds, not minutes or hours. When your feedback loop is fast, everything else accelerates: experimentation, debugging, and innovation.
The "Fire & Forget" Experience
With PloyD, your CI/CD becomes truly automated:
- Code goes in, results come out: The CI system manages all complexity - builds, tests, failures, retries, notifications
- Seamless hardware access: Provide quick and effortless access to necessary hardware
- Comprehensive observability: Implement clear logs, metrics, and dashboards to provide insights into what, why, and how to resolve issues
The Unified Experience
With PloyD, your development workflow becomes seamless:
- Single interface: Manage all your AI/ML workflows from one place
- Intelligent scheduling: Automatic hardware selection based on workload characteristics
- Built-in observability: Comprehensive logging, metrics, and dashboards out of the box
- Enterprise security: Multi-tenancy, secrets management, and compliance automation
Real-World Impact
Teams using PloyD report significant improvements in key velocity metrics:
The Path Forward
Developer velocity isn't just about faster builds—it's about removing friction from the entire development experience. When engineers can focus on solving problems instead of fighting infrastructure, innovation accelerates.
The key is recognizing that AI/ML development has unique requirements that traditional DevOps tools weren't designed to handle. Purpose-built platforms like PloyD bridge this gap, providing the specialized capabilities teams need while maintaining the simplicity developers expect.
Take Action: Audit Your Development Workflow
Take a hard look at your current development workflow and ask:
- How long does your project take to compile (clean/incremental/no-op builds)?
- How long do your tests take to run?
- How much time are you losing to infrastructure complexity?
- What percentage of your engineering time is spent on tooling vs. core problems?
- How often do deployments fail due to infrastructure issues?
If these numbers are higher than you'd like, it's time to consider a different approach. The cost of fragmented toolchains isn't just measured in dollars—it's measured in missed opportunities, delayed launches, and frustrated teams.
"Large per-job overhead (minutes): Rebuilding from scratch every time, which is slow - incremental builds for simple changes would be very nice. The time adds up quickly when you're iterating on algorithms."
Ready to Accelerate Your AI/ML Development?
See how PloyD can eliminate infrastructure fragmentation and restore your team's velocity. Our platform is designed specifically for AI/ML workflows, with the enterprise features you need and the developer experience you want.