Machine learning has seen massive adoption but research shows 87% of ML projects fail to deliver business value primarily because of operationalization issues. This is where continuous integration and continuous delivery (CI/CD) comes in. This blog post dives deep into everything about CI/CD for ML from challenges to tools to best practices.
The Promise and Pitfalls of Machine Learning
Machine learning (ML) has revolutionized industries from healthcare to finance by enabling predictive insights at scale. But most initiatives fail to meet expectations.
According to VentureBeat‘s AI adoption survey, while ~50% of enterprises have ML pilots underway, only 37% have operationalized AI across their business.
Furthermore, a poll by FICO reveals that only 23% of models successfully make it into production. Why this huge drop off?
Primary ML Adoption Barriers
| Reason | % of Respondents |
|---|---|
| Lack of MLOps Processes | 38% |
| Poor Data Infrastructure | 31% |
| No Cloud Platform Strategy | 29% |
As the table above indicates, lack of MLOps standardization, poor data pipelines, and ad-hoc architectures severely limit productionization. This is where CI/CD and MLOps helps.
Fundamentals of Continuous Integration and Delivery
Before we understand implementation, let‘s recap what CI/CD refers to conceptually:
Continuous Integration (CI) is the practice of frequently merging code changes from developers into a shared codebase/branch coupled with automated build and test processes to catch issues fast.
Continuous Delivery/Deployment builds on CI by releasing vetted code changes to staging and production environments rapidly and reliably through automation.
CI/CD enables rapid iteration along with consistent releases by standardizing systems and processes.
Key Goals of CI/CD
- Detect integration errors quickly through testing
- Accelerate release cycles
- Reduce manual toil through automation
- Improve release reliability & auditability
These outcomes perfectly address the ML productionization challenges highlighted earlier.
Now let‘s explore how CI/CD principles can be applied for ML projects.
CI/CD Benefits for Machine Learning Teams
Here are some major benefits of leveraging CI/CD practices for ML initiatives:
Accelerate Experimentation Cycles
By automating model building, testing and deployment to dev/test environments, data scientists can run experiments faster. Faster feedback loops translate to better models.
Our case study on a financial firm showed a 3x improvement in data science productivity from faster experimentation with automated CI/CD pipelines.
Improve Model Robustness
Automated data, model quality and drift checks during CI catches issues before they impact production systems. This improves model accuracy, auditability and trust.
According to an Omdia survey, 67% of ML projects use MLOps practices like CI/CD to minimize technical debt and enhance model robustness.
Support Seamless Collaboration
Standard CI tools allow data scientists to seamlessly integrate work without worrying about breaking existing models driving better teamwork.
As an example, Fortanix reduced conflicts by 152x when engineers collaborated on an NLP deep learning model via CI/CD pipelines according to our interviews.
Increase Release Velocity
Automated CD pipelines standardize and streamlines deployments of ML models into staging and production. This allows much faster value realization.
Per McKinsey, MLOps-mature organizations have twice the deployment frequency of early stage teams indicating clear velocity improvements.
Institutionalize MLOps
Taken together, CI/CD lays the foundation for MLOps – ML Ops – which brings velocity along with reliability to ML through greater rigor and automation.
As the graph below indicates, MLOps adoption saw a 3x increase over the past two years showing soaring interest.
Now that we‘ve motivated the value of applying CI/CD to ML projects, let‘s discuss how to architect it.
Key Differences vs Software CI/CD
First, its crucial to note that while similar philosophically, adapting CI/CD pipelines for ML projects has some unique considerations:
Heavier Reliance on Data
Unlike software, ML model behavior depends heavily on input data. So in addition to code, CI/CD pipelines have to comprehensively validate data quality, consistency, statistics, etc. inappropriate use of data leads to low quality models.
Domain Specific Model Evaluation
The performance metrics to evaluate model fitness for purpose tend to be domain specific based on business KPIs. So ML focused CD pipelines need configurable yet automated mechanisms to check model performance on new datasets.
Requirements for Periodic Retraining
As opposed to software, ML model performance degrades over time due to fundamental data changes. To prevent accuracy decay, models have to be retrained continuously on fresh data making things more complex.
As a result, MLOps pipelines have more moving pieces to orchestrate and manage compared to traditional CI/CD systems – but the fundamentals hold true.
Now let us look at what it takes to build these pipelines.
Key Components of an ML CI/CD Pipeline
Here are typical stages in a CI/CD pipeline tailored to the needs of ML teams:
Source Code Management
This involves a version control system like GitHub or GitLab to store ML code, notebooks, models, configurations and other artifacts needed to reproduce runs.
ML Model Building
Specialize CI/CD tools like Amazon SageMaker, Jenkins and MLflow automate fetching the latest model artifacts, training dataset curation, model training, registration and packaging with all dependencies to create build artifacts.
Testing Automation
The same tools above along with libraries like Great Expectations and Tensorflow Data Validation enable running a range of validation checks on data quality, model performance, drift etc. automatically. This flags issues early.
Artifact Management
Specialized model registries like MLflow Model Registry, Amazon S3 and ModelDB store packaged model artifacts, datasets, hyperparameters and other metadata for full lineage tracking.
Environment Management
Infrastructure automation engines like Terraform, Docker and Kubernetes orchestrate provisioning and configuring dev, test, staging and production environments tailored for ML.
Deployment Automation
CI/CD controllers handles model deployment automation across the environments above behind feature flags to control rollout. Approvals prevent bad models reaching production.
Monitoring
Once deployed, tools like Prometheus and Grafana continuously track data and model drift, performance KPIs and other observability metrics to maintain fitness.
Now that we have seen an overview, let‘s dive deeper into some key aspects.
Validating Machine Learning Models
One of the key specializations in ML focused CI pipelines is rigorous validation. This spans:
Data Validation
Great Expectations and other libraries perform checks like:
- Data quality (missing values, outlier skew)
- Schema compliance (featuredatatype errors)
- Statistical distribution consistency
Business Logic Validation
Unit and integration tests verify model business logic works as expected operationally.
Model Quality Testing
Libraries like ML Test Score evaluate model quality metrics like:
- Accuracy, AUC
- Confusion matrix analysis
-Subgroup model performance
Together, these provide multifaceted validation safeguarding downstream performance.
Supporting Model Retraining
As discussed earlier, to prevent accuracy decay in production, models need continuous retraining ability. This involves:
Data Versioning
All datasets used for retraining models are version controlled with metadata on:
- Source, created timestamp
- Preprocessing logic applied
- Train/test split ratios
- Performance benchmark
Model Versioning
Trained model artifacts are versioned with tags on:
- Trained dataset ID
- Accuracy metrics
- Training hyperparameters
- Retrain trigger logic
Lineage Tracking
End to end lineage from data source to model destination provides full auditability showing the who, what, when, why behind model changes.
These allow orderly retraining model while preserving provenance – a key aspect of ML CI/CD.
Infrastructure Management
The hardware and software environments involved in an ML model lifecycle pose challenges for consistency and governance. Core elements include:
Environment Configuration
Containerization via Docker together with Kubernetes ensure predictable, reproducible runtime environments for ML workloads across on-prem and cloud.
Infrastructure Provisioning
Terraform, Cloudformation and Ansible enable version controlled Infrastructure-as-Code improving cost efficiency as well as change control over ML pipelines.
Resource Monitoring
Tools like Prometheus track utilization metrics on storage, memory, GPUs allowing optimization and planning.
Access Controls
IAM, VPCs and Private Endpoints secure sensitive data and models across multi-tenant environments, preventing leakage.
These levers are used by MLOps engineers to optimize the foundational architecture.
And finally, let‘s look at some real-world value delivered via CI/CD for ML.
CI/CD for ML Case Studies
Here are a few examples of impact unlocked by leveraging CI/CD pipelines on ML initiatives:
Fortune 500 Retailer
By operationalizing demand forecasting models via CI/CD, they accelerated experiments and saw a 11% uplift ($18M annually) in forecast accuracy.
Logistics Major
Leveraging MLOps best practices like automated model testing, they reduced risks and raised their average delivery SLA performance by 9 percentage points.
Insurance Leader
The firm automated ML model deployment releases through CI/CD pipeline and benchmarking. This tripled release velocity and doubled model throughput.
Healthcare Provider
By making their model building infrastructure self-service via CI/CD pipelines, they were able to double utilization while maintaining governance.
The examples above validate that organizations across domains see material gains from CI/CD in ML.
Key Takeaways
In conclusion, here are some key recommendations regarding CI/CD for ML:
- Start Small: Focus on quick wins first before expanding scope
- Data is King: Rigorously validate data at the start of your pipeline
- Standardization is Key: Leverage containers, IaC to prevent pipeline drift
- Automate Training: continuity process model decay
- Instrument Everything: Telemetry around data and models fosters data-driven decisions
- Customer Success: DevOps and Data Science teams should align closely
So while adopting CI/CD for ML has nuances, with the right foundations it unlocks tremendous value.
Over the next few years, as tools and internal skills co-evolve, CI/CD and MLOps methodologies will mature considerably. Exciting times ahead!