Excellent. We have now reached the final planned stage. Phase 4 builds upon the production-ready platform from Phase 3 to deliver enterprise-grade features that make DSPex a truly top-tier solution for building and managing sophisticated AI systems. This phase focuses on automation, advanced orchestration, and scaling.
6. Detailed Roadmap: Phase 4 - Enterprise-Ready Platform
Objective: To evolve DSPex from a powerful platform into an autonomous, enterprise-grade “AI Operating System.” This involves automating the deployment lifecycle, orchestrating multiple models intelligently, and implementing advanced, self-optimizing algorithms.
Month 7: Advanced Orchestration & Model Management
| Week | Epic | Key Tasks & Implementation Details | Success Criteria |
|---|---|---|---|
| Week 29 | (4.1) The Model Registry | Based on stage4_advanced_features.md:• DSPex.ML.Model Resource: Implement the Ash resource for managing LLMs. It should store provider details, model names, API keys (using Ash’s secret management), and performance characteristics (cost, latency, success rate).• DSPex.ML.ProgramModel Resource: A join resource connecting Programs to Models, defining their relationship (e.g., primary, fallback).• Health Check Worker: An Oban worker ( ModelHealthCheckWorker) that periodically pings the model’s API to update its status and latency metrics in the Model resource. | ✅ Can register multiple models (e.g., GPT-4o, Claude 3.5 Sonnet, a local Ollama model) in the database via the Model resource.✅ The health check worker correctly identifies and flags an unavailable model. |
| Week 30 | (4.2) The Model Router | Based on stage4_advanced_features.md:• DSPex.ML.ModelRouter: Implement the core routing logic. Given a program and its inputs, the router should select the best available ProgramModel based on its strategy.• Implement Routing Strategies: - :primary: Always use the highest-priority model.- :fallback: Use if the primary fails.- :cost_optimized: Select the model with the lowest cost_per_token that meets a quality threshold.- :latency_optimized: Select the model with the lowest average_latency_ms.• Enhance ExecutionEngine: Integrate the ModelRouter into the main execution flow to dynamically select the model for each call. | ✅ A program configured with both a primary (GPT-4o) and a fallback (Claude 3.5) will automatically use the fallback if the primary’s health status is :error.✅ A program configured for :cost_optimized routing correctly chooses a cheaper model for a simple task. |
| Week 31 | (4.3) Advanced Optimizers (MIPRO Bridge) | Based on the Optimizers gap analysis:• DSPex.Optimizers.MIPRO: Implement the native Elixir module for the MIPRO optimizer. This module does not contain the optimization logic itself.• Its compile function will:1. Serialize the program, dataset, and Variable.Space.2. Send a single :run_optimization command to the Python bridge with "optimizer": "MIPRO".3. Await the final, optimized program state from Python. • Enhance dspy_bridge.py: Add the run_optimization command handler that invokes the full dspy.MIPRO teleprompter. | ✅ Can successfully run DSPex.Optimizers.MIPRO.compile(program, trainset) from Elixir.✅ The optimization runs in the Python process, and the final optimized program state is correctly returned and saved to the Ash Program resource. |
| Week 32 | (4.4) Multi-Objective Optimization | Based on stage4_advanced_features.md:• MultiObjective Optimizer: Implement a native Elixir optimizer that balances competing objectives.• Enhance Evaluation Harness: Allow the harness to accept multiple metric functions (e.g., accuracy, latency, cost).• Pareto Frontier: The optimizer’s result should not be a single best program, but a “Pareto frontier” of programs representing the best trade-offs (e.g., the most accurate program under 500ms, the cheapest program with >90% accuracy). | ✅ Running the MultiObjective optimizer produces a set of non-dominated program configurations.✅ The user can inspect the Pareto frontier to choose the best program for their specific needs. |
Month 8: Deployment Automation & Experimentation
| Week | Epic | Key Tasks & Implementation Details | Success Criteria |
|---|---|---|---|
| Week 33 | (4.5) Deployment Pipeline Framework | Based on stage4_advanced_features.md:• DeploymentPipeline Resource: An Ash resource to define an automated deployment workflow (e.g., “when a program’s accuracy on the dev set improves by 5%, trigger a canary deployment”).• Deployment Resource: An Ash resource to track a specific deployment event, moving through states like :pending, :validating, :deploying, :deployed, :failed, :rolled_back. | ✅ Can create a DeploymentPipeline in Ash that links a program to a set of validation rules and deployment actions. |
| Week 34 | (4.6) Canary Deployment Strategy | • DeploymentWorker: An Oban worker that executes the steps in a Deployment record.• Traffic Splitting Logic: Implement a simple traffic splitter (e.g., a GenServer or ETS table) that the ModelRouter consults. The DeploymentWorker will update this splitter.• Implement Canary Logic: The worker gradually increases traffic to the new program version (e.g., 1% -> 10% -> 50% -> 100%). At each stage, it monitors performance metrics from the Execution records. If metrics degrade, it triggers an automatic rollback. | ✅ Triggering a Deployment for a program successfully executes a canary release, observable through metrics.✅ Forcing a canary to fail (e.g., by introducing a bug) results in an automatic rollback to the previous stable version. |
| Week 35 | (4.7) Advanced Experimentation | Based on Scientific Evaluation Framework:• Experiment Resource: Enhance this resource to support A/B testing between two or more program versions.• AnalyzeExperimentResults Action: Implement a manual action on the Experiment resource that performs statistical analysis (e.g., a t-test) on the performance metrics of the variants to determine if a change is statistically significant.• Hypothesis Tracking: Add fields to the Experiment resource to formally state a hypothesis (e.g., “Using ChainOfThought will improve accuracy by 10% but increase latency by 20%”). | ✅ Can define and run an A/B test between two program versions. ✅ The analyze_results action correctly identifies the winning variant and calculates the statistical significance of the result. |
| Week 36 | (4.8) Final Polish & Documentation | • Write Advanced Guides: Create documentation for the multi-model router, deployment automation, and experimentation features. • Create a Full Demo Application: Build a small Phoenix application that uses DSPex to showcase the entire MLOps lifecycle from experimentation to automated deployment.• Final Performance Tuning: Profile the entire system under load and address any remaining bottlenecks. | ✅ The demo application is fully functional and can be used for presentations. ✅ The entire test suite, including all new components, passes with 100% reliability. ✅ Performance benchmarks meet or exceed targets. |
Phase 4 Deliverables & Outcome
Upon completion of Phase 4, ElixirML/DSPex will be a feature-complete, enterprise-grade platform that stands at the cutting edge of MLOps.
Key Deliverables:
- A multi-model orchestration system with intelligent, policy-based routing.
- An automated CI/CD pipeline for ML programs, including canary deployments and automatic rollbacks.
- A suite of advanced, native optimizers complemented by a strategic bridge to Python’s best-in-class tools.
- A rigorous experiment management framework for data-driven decision-making.
Resulting State of the System:
- Autonomous: The system can now automate significant parts of the MLOps lifecycle, from optimization to deployment and monitoring.
- Intelligent: The platform makes informed, data-driven decisions about which models to use and when to deploy new versions.
- Enterprise-Grade: With features like the model registry, deployment pipelines, and advanced experimentation, the platform is suitable for use in large-scale, mission-critical applications.
- Vision Realized: The project has fully realized the initial vision of a superior AI orchestration layer in Elixir, demonstrating a clear and compelling advantage over pure Python-based systems for production use cases.