Why Migrate to Valohai?
TL;DR: Migrate to Valohai in phases starting with one high-impact project. Most organizations see 50% faster experimentation within weeks and achieve full adoption in 3-6 months. Zero vendor lock-in and your code stays yours.
Why Teams Migrate to Valohai
Your ML teams are likely battling at least one of these challenges:
Infrastructure Overhead Is Killing Productivity
Data scientists spending 40% of time on DevOps tasks
"Works on my machine" blocking production deployments
Each team maintaining their own MLOps stack
Cloud costs spiraling due to idle resources
Experiments Aren't Reproducible
Can't recreate that model from 6 months ago
Missing dependency versions breaking reruns
Audit requirements forcing manual documentation
Teams re-running identical experiments unknowingly
Scaling Hits a Wall
Single-machine limits blocking larger experiments
Manual provisioning creating bottlenecks
No resource sharing between teams
Every new project starts from zero
Teams Can't Collaborate Effectively
Models trapped in individual laptops
No standard deployment pipeline
Knowledge lost when people leave
Integration nightmares between team tools
The Strategic Migration Path
Phase 1: Prove Value Fast (Weeks 1-4)
Start with one team's biggest pain point. Get a working project in hours, not weeks.
Choose Your Pilot:
High-impact project with clear metrics
3-5 person team eager for better tools
Existing code that runs today
Success Looks Like:
First execution running within 2 hours
50% reduction in experiment setup time
Zero infrastructure debugging by data scientists
Team asking "can we migrate more projects?"
Deliverables:
Working project with automated tracking
Before/after metrics showing time saved
Initial cost analysis
Team testimonial for internal buy-in
Phase 2: Standardize Your Workflows (Months 2-3)
Scale what works. Move from individual wins to team transformation.
Expand Scope:
Full project lifecycle (data → training → deployment)
Advanced features (pipelines, hyperparameter optimization)
Team-wide best practices
Success Metrics:
All team projects using Valohai
70% reduction in "plumbing" work
Models deploying in hours, not weeks
30% infrastructure cost reduction
Deliverables:
Reusable templates for common workflows
Automated CI/CD pipelines
Team playbook documented
Quarterly cost savings report
Phase 3: Scale Across the Organization (Months 4-6)
Transform ML from cost center to innovation engine.
Organization-Wide Impact:
All ML teams onboarded
Cross-team model sharing
Enterprise governance active
Executive visibility enabled
Success Metrics:
60% faster model delivery to production
90% experiment reproducibility
Full compliance audit trail
50% reduction in total ML infrastructure costs
Common Migration Scenarios
"We Need to Replace Our Existing Platform"
Your current platform promised the world but delivered complexity.
Migration Strategy:
Run Valohai parallel to existing platform
Migrate your most painful workflows first
Compare metrics side-by-side
Sunset old platform once value proven
Timeline: 2-3 months for complete transition
"We're Drowning in Tool Sprawl"
Different teams, different tools, zero standardization.
Migration Strategy:
Map current tool landscape and overlaps
Identify common workflows across teams
Replace tool-by-tool with unified platform
Calculate maintenance hours saved
Timeline: 3-4 months to consolidate
"We Can't Get Models to Production"
Research breakthroughs dying in deployment purgatory.
Migration Strategy:
Start with research workflow
Add deployment in same platform
No handoffs between teams
Measure time-to-production improvement
Timeline: 1-2 months for first production model
"We're Starting Our ML Journey"
Green field opportunity to build it right.
Migration Strategy:
Implement best practices from day one
Avoid accumulating technical debt
Scale gradually as team grows
Learn from others' mistakes
Timeline: Immediate value, scales with growth
Addressing Executive Concerns
"What About Lock-In?"
Reality Check:
Your code runs unchanged—no Valohai SDK required
Standard Docker, Git, and YAML throughout
Full API access to export everything
Your migration path out is as easy as your path in.
"How Do We Justify the Investment?"
Measurable Returns:
Month 1:
50% reduction in experiment setup time
Infrastructure debugging eliminated
First cost optimizations visible
Months 2-3:
30-50% infrastructure cost reduction
2x faster development cycles
Tool consolidation savings
Months 4+:
60% faster time-to-market
5x improvement in asset reuse
Complete compliance coverage
"Will Our Teams Actually Adopt This?"
Why Teams Love Valohai:
Keep using familiar tools (Python, notebooks, Git)
No new languages or frameworks
Less time on plumbing, more on ML
Success spreads organically
Early adopters become internal champions.
"What If Something Goes Wrong?"
Risk Mitigation Built In:
Enterprise SLAs with 24/7 support (by separate agreement)
Gradual migration reduces risk
Your code remains portable
Professional services available
Making the Decision
Key Decision Factors
Cost of Delay: Every month without proper ML infrastructure costs you:
Lost innovation opportunities
Accumulated technical debt
Growing compliance risk
Widening competitive gap
Why Valohai Over Alternatives:
Only true bring-your-own-code platform
Managed service = zero maintenance
Proven with enterprises like yours
Scales from startup to enterprise
Next Steps
Technical Validation
Review the technical migration guide
Run proof-of-concept with pilot team
Benchmark against current setup
Business Case Development
Calculate current infrastructure + maintenance costs
Estimate productivity gains from metrics above
Factor in compliance and risk reduction
Get Expert Input
Schedule migration assessment with Valohai
Connect with similar organizations who've migrated
Review security and compliance requirements
Ready to transform your ML operations? Start with our technical migration guide or contact our team at [email protected] for a migration assessment.
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