Implement GitOps workflows for ML models with automated testing, canary deployments, rollback strategies, and multi-environment promotion using ArgoCD and MLflow.
AI Editorial Team
Collective Intelligence
Your data science team produces 5 new models weekly. Each requires:
Doing this manually for 5 models weekly takes 40 engineer-hours. At scale, this becomes impossible. The solution: GitOps for machine learning.
In this advanced guide, we'll build a complete GitOps CI/CD pipeline for ML models that automates testing, validation, and deployment using ArgoCD, MLflow, and Kubernetes.
GitOps applies DevOps principles to infrastructure and applications: declare desired state in Git, automatically reconcile actual state. For ML, this means:
1# gitops-ml-architecture.yaml
2components:
3 source_repositories:
4 - ml-models: Model code, training scripts, Dockerfiles
5 - ml-pipelines: Kubeflow pipeline definitions
6 - ml-manifests: Kubernetes manifests for serving
7 - ml-configs: Monitoring, alerting, feature store configurations
8
9 artifact_registries:
10 - container_registry: Docker images for models, preprocessing
11 - model_registry: MLflow for model artifacts, metrics, metadata
12
13 deployment_controller:
14 tool: argocd
15 applications:
16 - model-serving: Deploys inference services
17 - monitoring-stack: Deploys Prometheus, Grafana, Evidently
18 - feature-store: Deploys Feast or Tecton
19
20 testing_framework:
21 components:
22 - data_tests: Great Expectations for data validation
23 - model_tests: Benchmark against champion model
24 - security_tests: Snyk, Trivy for container scanning
25 - compliance_tests: Regulatory requirements checking
26
27 promotion_pipeline:
28 environments:
29 - development: Auto-deploy from PR merge
30 - staging: Manual approval required
31 - production: Canary rollout with automated analysis
32
33 rollback_mechanism:
34 triggers:
35 - performance_drop: >5% accuracy loss
36 - high_latency: p95 > SLA
37 - security_vulnerability: CVSS > 7.0
38Organize repositories by concern:
ml-organization/
├── ml-models/
│ ├── churn-prediction/
│ │ ├── src/
│ │ │ ├── train.py
│ │ │ ├── predict.py
│ │ │ └── preprocessing.py
│ │ ├── tests/
│ │ │ ├── test_data_validation.py
│ │ │ ├── test_model_performance.py
│ │ │ └── test_inference.py
│ │ ├── Dockerfile
│ │ ├── requirements.txt
│ │ └── hyperparameters.yaml
│ └── fraud-detection/
│ └── ... (similar structure)
├── ml-pipelines/
│ ├── training-pipelines/
│ │ ├── churn-training.yaml
│ │ └── fraud-training.yaml
│ └── retraining-pipelines/
│ └── drift-triggered-retraining.yaml
├── ml-manifests/
│ ├── base/
│ │ ├── kustomization.yaml
│ │ ├── namespace.yaml
│ │ └── serviceaccount.yaml
│ ├── environments/
│ │ ├── development/
│ │ │ ├── kustomization.yaml
│ │ │ └── patch-replicas.yaml
│ │ ├── staging/
│ │ │ └── ...
│ │ └── production/
│ │ └── ...
│ └── applications/
│ ├── churn-model/
│ │ ├── kustomization.yaml
│ │ ├── deployment.yaml
│ │ ├── service.yaml
│ │ ├── hpa.yaml
│ │ └── istio-virtualservice.yaml
│ └── fraud-model/
│ └── ...
└── ml-configs/
├── monitoring/
│ ├── prometheus-rules.yaml
│ └── grafana-dashboards.yaml
├── feature-store/
│ └── feast-config.yaml
└── drift-detection/
└── evidently-config.yaml
MLflow serves as our model registry, tracking experiments, models, and deployments:
1import mlflow
2import mlflow.sklearn
3from mlflow.tracking import MlflowClient
4from datetime import datetime
5import json
6import pandas as pd
7from sklearn.ensemble import RandomForestClassifier
8from sklearn.metrics import accuracy_score, f1_score
9import pickle
10
11class MLflowRegistry:
12 def __init__(self, tracking_uri="http://mlflow-server:5000"):
13 self.client = MlflowClient(tracking_uri)
14 self.experiment_name = "churn-prediction-gitops"
15
16 # Get or create experiment
17 experiment = mlflow.get_experiment_by_name(self.experiment_name)
18 if experiment is None:
19 experiment_id = mlflow.create_experiment(self.experiment_name)
20 else:
21 experiment_id = experiment.experiment_id
22
23 self.experiment_id = experiment_id
24
25 def register_new_version(self, model, metrics, params, training_data_ref):
26 """Register a new model version in MLflow."""
27 with mlflow.start_run(experiment_id=self.experiment_id):
28 # Log parameters
29 mlflow.log_params(params)
30
31 # Log metrics
32 mlflow.log_metrics(metrics)
33
34 # Log training data reference
35 mlflow.log_param("training_data_ref", training_data_ref)
36
37 # Log model
38 mlflow.sklearn.log_model(model, "model")
39
40 # Add tags for GitOps
41 mlflow.set_tags({
42 "git_commit": os.environ.get("GIT_COMMIT", "unknown"),
43 "git_branch": os.environ.get("GIT_BRANCH", "unknown"),
44 "pipeline_run_id": os.environ.get("PIPELINE_RUN_ID", "unknown"),
45 "deployment_ready": "false" # Will be set to true after validation
46 })
47
48 # Create model version
49 run_id = mlflow.active_run().info.run_id
50 model_uri = f"runs:/{run_id}/model"
51
52 # Register in model registry
53 registered_model = self.client.create_registered_model(
54 name="churn-prediction",
55 tags={"domain": "customer-analytics", "business_unit": "growth"}
56 )
57
58 model_version = self.client.create_model_version(
59 name="churn-prediction",
60 source=model_uri,
61 run_id=run_id
62 )
63
64 # Transition to staging for validation
65 self.client.transition_model_version_stage(
66 name="churn-prediction",
67 version=model_version.version,
68 stage="Staging"
69 )
70
71 return model_version.version
72
73 def promote_to_production(self, model_name, version, validation_results):
74 """Promote model version to production after validation."""
75 # Update model version description with validation results
76 self.client.update_model_version(
77 name=model_name,
78 version=version,
79 description=json.dumps(validation_results)
80 )
81
82 # Transition to production
83 self.client.transition_model_version_stage(
84 name=model_name,
85 version=version,
86 stage="Production"
87 )
88
89 # Archive previous production version
90 prod_versions = self.client.search_model_versions(
91 f"name='{model_name}' and stage='Production'"
92 )
93
94 for prod_version in prod_versions:
95 if prod_version.version != str(version):
96 self.client.transition_model_version_stage(
97 name=model_name,
98 version=prod_version.version,
99 stage="Archived"
100 )
101
102 # Update GitOps manifest with new version
103 self.update_gitops_manifest(model_name, version)
104
105 def update_gitops_manifest(self, model_name, version):
106 """Update Kubernetes manifest with new model version."""
107 manifest_path = f"/ml-manifests/applications/{model_name}/deployment.yaml"
108
109 with open(manifest_path, 'r') as f:
110 manifest = yaml.safe_load(f)
111
112 # Update container image tag
113 manifest['spec']['template']['spec']['containers'][0]['image'] = f"registry.example.com/{model_name}:v{version}"
114
115 with open(manifest_path, 'w') as f:
116 yaml.dump(manifest, f)
117
118 # Commit and push changes
119 subprocess.run([
120 "git", "add", manifest_path
121 ], check=True)
122
123 subprocess.run([
124 "git", "commit", "-m",
125 f"Update {model_name} to version {version} [skip ci]"
126 ], check=True)
127
128 subprocess.run([
129 "git", "push", "origin", "main"
130 ], check=True)
131
132 print(f"Updated GitOps manifest for {model_name} version {version}")
133Define ArgoCD applications for ML model serving:
1# argocd-applications.yaml
2apiVersion: argoproj.io/v1alpha1
3kind: Application
4metadata:
5 name: churn-model-serving
6 namespace: argocd
7spec:
8 project: ml-models
9 source:
10 repoURL: https://github.com/org/ml-manifests.git
11 path: applications/churn-model
12 targetRevision: HEAD
13 helm:
14 parameters:
15 - name: model.version
16 value: "latest"
17 - name: replicas
18 value: "3"
19 - name: resources.requests.cpu
20 value: "500m"
21 - name: resources.requests.memory
22 value: "1Gi"
23
24 destination:
25 server: https://kubernetes.default.svc
26 namespace: ml-serving
27
28 syncPolicy:
29 automated:
30 selfHeal: true
31 prune: true
32 syncOptions:
33 - CreateNamespace=true
34 - ApplyOutOfSyncOnly=true
35
36 ignoreDifferences:
37 - group: apps
38 kind: Deployment
39 jsonPointers:
40 - /spec/replicas
41 - /spec/template/spec/containers/0/resources
42
43 # Health checks for ML services
44 healthChecks:
45 - type: kubernetes
46 value:
47 group: apps
48 kind: Deployment
49 name: churn-model-deployment
50 namespace: ml-serving
51 check: rollout
52 criteria:
53 readyReplicas: ">=3"
54
55 - type: prometheus
56 value:
57 query: |
58 increase(model_inference_requests_total{model="churn"}[5m]) > 0
59 duration: 5m
60
61 - type: custom
62 value:
63 script: |
64 # Check model performance metrics
65 curl -s http://mlflow-server:5000/api/2.0/mlflow/model-versions/get?name=churn-prediction&version=latest | jq -e '.status == "READY"'
66 initialDelaySeconds: 30
67 periodSeconds: ":60"
68---
69# Application for monitoring stack
70apiVersion: argoproj.io/v1alpha1
71kind: Application
72metadata:
73 name: ml-monitoring-stack
74 namespace: argocd
75spec:
76 project: ml-models
77 source:
78 repoURL: https://github.com/org/ml-configs.git
79 path: monitoring
80 targetRevision: HEAD
81
82 destination:
83 server: https://kubernetes.default.svc
84 namespace: monitoring
85
86 syncPolicy:
87 automated:
88 selfHeal: true
89 prune: false # Don't prune monitoring data!
90
91 # Additional applications for feature store, drift detection, etc.
92Implement comprehensive testing before deployment:
1# ml-testing-pipeline.yaml
2apiVersion: tekton.dev/v1beta1
3kind: Pipeline
4metadata:
5 name: ml-model-validation
6spec:
7 params:
8 - name: model-name
9 - name: model-version
10 - name: git-commit
11
12 workspaces:
13 - name: model-source
14 - name: test-results
15
16 tasks:
17 # Task 1: Data validation
18 - name: validate-data
19 taskRef:
20 name: great-expectations-validation
21 params:
22 - name: dataset-path
23 value: $(params.training-data-path)
24 workspaces:
25 - name: source
26 workspace: model-source
27
28 # Task 2: Model performance testing
29 - name: test-model-performance
30 taskRef:
31 name: model-benchmarking
32 runAfter: [validate-data]
33 params:
34 - name: champion-model
35 value: $(params.champion-model-version)
36 - name: candidate-model
37 value: $(params.model-version)
38 - name: test-dataset
39 value: $(params.test-data-path)
40
41 # Task 3: Security scanning
42 - name: scan-container
43 taskRef:
44 name: trivy-scan
45 runAfter: [test-model-performance]
46 params:
47 - name: image
48 value: $(params.model-image)
49
50 # Task 4: Compliance checks
51 - name: check-compliance
52 taskRef:
53 name: compliance-checker
54 runAfter: [scan-container]
55 params:
56 - name: model-type
57 value: $(params.model-type)
58 - name: region
59 value: $(params.deployment-region)
60
61 # Task 5: Generate validation report
62 - name: generate-report
63 taskRef:
64 name: validation-report-generator
65 runAfter: [check-compliance]
66 params:
67 - name: test-results
68 value: $(workspaces.test-results.path)
69
70 # Task 6: Decision gate
71 - name: deployment-decision
72 taskRef:
73 name: decision-gate
74 runAfter: [generate-report]
75 params:
76 - name: validation-report
77 value: $(workspaces.test-results.path)/report.json
78---
79# Tekton Task for model benchmarking
80apiVersion: tekton.dev/v1beta1
81kind: Task
82metadata:
83 name: model-benchmarking
84spec:
85 params:
86 - name: champion-model
87 - name: candidate-model
88 - name: test-dataset
89
90 steps:
91 - name: benchmark
92 image: python:3.11
93 script: |
94 #!/usr/bin/env python3
95 import mlflow
96 import pandas as pd
97 from sklearn.metrics import accuracy_score, f1_score
98 import json
99 import sys
100
101 # Load models from MLflow
102 champion = mlflow.sklearn.load_model(f"models:/churn-prediction/$(params.champion-model)")
103 candidate = mlflow.sklearn.load_model(f"models:/churn-prediction/$(params.candidate-model)")
104
105 # Load test data
106 test_data = pd.read_csv("$(params.test-dataset)")
107 X_test = test_data.drop('target', axis=1)
108 y_test = test_data['target']
109
110 # Make predictions
111 champion_preds = champion.predict(X_test)
112 candidate_preds = candidate.predict(X_test)
113
114 # Calculate metrics
115 champion_accuracy = accuracy_score(y_test, champion_preds)
116 candidate_accuracy = accuracy_score(y_test, candidate_preds)
117
118 champion_f1 = f1_score(y_test, champion_preds, average='weighted')
119 candidate_f1 = f1_score(y_test, candidate_preds, average='weighted')
120
121 # Determine if candidate is better
122 accuracy_improvement = candidate_accuracy - champion_accuracy
123 f1_improvement = candidate_f1 - champion_f1
124
125 # Business rule: Need at least 1% improvement in accuracy or F1
126 passes_benchmark = (accuracy_improvement >= 0.01) or (f1_improvement >= 0.01)
127
128 # Output results
129 results = {
130 "passes_benchmark": passes_benchmark,
131 "champion_accuracy": champion_accuracy,
132 "candidate_accuracy": candidate_accuracy,
133 "accuracy_improvement": accuracy_improvement,
134 "champion_f1": champion_f1,
135 "candidate_f1": candidate_f1,
136 "f1_improvement": f1_improvement
137 }
138
139 with open("/tekton/results/benchmark_results.json", "w") as f:
140 json.dump(results, f)
141
142 if not passes_benchmark:
143 print("Candidate model fails benchmark test")
144 sys.exit(1)
145 else:
146 print("Candidate model passes benchmark test")
147 command: ["python"]
148Implement progressive rollout with automatic analysis:
1# canary-deployment.yaml
2apiVersion: flagger.app/v1beta1
3kind: Canary
4metadata:
5 name: churn-model
6 namespace: ml-serving
7spec:
8 targetRef:
9 apiVersion: apps/v1
10 kind: Deployment
11 name: churn-model-deployment
12
13 # Service to expose the model
14 service:
15 port: 8080
16 targetPort: 8080
17 name: churn-model-service
18
19 # Analysis configuration
20 analysis:
21 interval: 30s
22 threshold: 5
23 maxWeight: 100
24 stepWeight: 10
25 metrics:
26 - name: request-success-rate
27 threshold: 99
28 interval: 1m
29 - name: request-duration
30 threshold: 500
31 interval: 1m
32 query: |
33 histogram_quantile(0.99,
34 sum(rate(model_inference_duration_seconds_bucket[1m])) by (le))
35
36 # ML-specific metrics
37 - name: model-accuracy
38 threshold: 0.85
39 interval: 5m
40 query: |
41 avg_over_time(model_accuracy{model="churn"}[5m])
42
43 - name: data-drift-score
44 threshold: 0.3
45 interval: 5m
46 query: |
47 avg_over_time(ml_model_drift_score{model="churn"}[5m])
48
49 - name: prediction-confidence
50 thresholdRange:
51 min: 0.7
52 interval: 5m
53 query: |
54 avg_over_time(model_prediction_confidence{model="churn"}[5m])
55
56 # Webhooks for custom validation
57 webhooks:
58 - name: load-test
59 type: pre3-rollout
60 url: http://load-test-service/load-test
61 timeout: 5m
62 metadata:
63 type: "soak"
64 duration: "10m"
65 users: "1000"
66
67 - name: business-metrics-validation
68 type: post-rollout
69 url: http://business-metrics-service/validate
70 timeout: 2m
71
72 # Progressive rollout schedule
73 # 1. 10% traffic for 5 minutes
74 # 2. 25% traffic for 10 minutes
75 # 3. 50% traffic for新15 minutes
76 # 4. 100% traffic if all metrics pass
77 canaryAnalysis:
78 steps:
79 - setWeight: 10
80 - pause:
81 duration: 5m
82 - setWeight: 25
83 - pause:
84 duration: 10m
85 - setWeight: 50
86 - pause:
87 duration: 15m
88 - setWeight: 100
89
90 # Automatic rollback on failure
91 revertOnDeletion: true
92When deployments fail, automatically rollback:
1class AutomatedRollback:
2 def __init__(self, k8s_client, mlflow_client, argo_client):
3 self.k8s = k8s_client
4 self.mlflow = mlflow_client
5 self.argo = argo_client
6
7 def monitor_deployment(self, deployment_name, namespace="ml-serving"):
8 """Monitor deployment and trigger rollback if needed."""
9 while True:
10 time.sleep(30) # Check every 30 seconds
11
12 # Get deployment status
13 deployment = self.k8s.read_namespaced_deployment(
14 name=deployment_name,
15 namespace=namespace
16 )
17
18 # Check rollout status
19 if deployment.status.unavailable_replicas:
20 self.trigger_rollback(deployment_name, "replicas_unavailable")
21 continue
22
23 # Check metrics
24 metrics = self.query_metrics(deployment_name)
25
26 if metrics['accuracy'] < 0.8:
27 self.trigger_rollback(deployment_name, "accuracy_below_threshold")
28
29 elif metrics['p95_latency'] > 1000: # 1 second
30 self.trigger_rollback(deployment_name, "high_latency")
31
32 elif metrics['error_rate'] > 0.01:
33 self.trigger_rollback(deployment_name, "high_error_rate")
34
35 elif self.check_drift(deployment_name) > 0.5:
36 self.trigger_rollback(deployment_name, "high_data_drift")
37
38 def trigger_rollback(self, deployment_name, reason):
39 """Execute rollback procedure."""
40 print(f"Rolling back {deployment_name}: {reason}")
41
42 # 1. Update ArgoCD to previous version
43 app = self.argo.get_application(deployment_name)
44
45 # Get previous successful version from Git history
46 prev_version = self.get_previous_version(deployment_name)
47
48 # Update ArgoCD manifest to previous version
49 self.argo.update_application_source(
50 app.metadata.name,
51 target_revision=prev_version['commit']
52 )
53
54 # 2. Revert MLflow model stage
55 current_version = self.get_current_model_version(deployment_name)
56 previous_prod_version = self.get_previous_production_version(deployment_name)
57
58 if previous_prod_version:
59 # Transition current version to Archived
60 self.mlflow.transition_model_version_stage(
61 name=deployment_name,
62 version=current_version,
63 stage="Archived"
64 )
65
66 # Transition previous version back to Production
67 self.mlflow.transition_model_version_stage(
68 name=deployment_name,
69 version=previous_prod_version,
70 stage="Production"
71 )
72
73 # 3. Notify team
74 self.send_alert(
75 title=f"Rollback triggered for {deployment_name}",
76 message=f"Reason: {reason}. Rolled back to version {prev_version['tag']}",
77 severity="critical"
78 )
79
80 # 4. Create incident report
81 self.create_incident_report(deployment_name, reason, prev_version)
82
83 def get_previous_version(self, deployment_name):
84 """Get previous successful version from Git history."""
85 # Query Git for last successful deployment
86 # This would use Git API to find the last healthy commit
87 return {
88 "commit": "abc123def",
89 "tag": "v1.2.3",
90 "timestamp": "2026-05-26T10:00:00Z"
91 }
92Promote models through environments with increasing rigor:
1# promotion-workflow.yaml
2apiVersion: argoproj.io/v1alpha1
3kind: Workflow
4metadata:
5 name: model-promotion-pipeline
6spec:
7 entrypoint: promote-model
8 arguments:
9 parameters:
10 - name: model_name
11 - name: model_version
12 - name: source_environment
13 - name: target_environment
14
15 templates:
16 - name: promote-model
17 steps:
18 - - name: validate-source-environment
19 template: environment-validation
20 arguments:
21 parameters:
22 - name: environment
23 value: "{{inputs.parameters.source_environment}}"
24 - name: model_version
25 value: "{{inputs.parameters.model_version}}"
26
27 - - name: run-environment-specific-tests
28 template: environment-tests
29 arguments:
30 parameters:
31 - name: environment
32 value: "{{inputs.parameters.target_environment}}"
33 - name: model_version
34 value: "{{inputs.parameters.model_version}}"
35 when: "{{steps.validate-source-environment.outputs.parameters.valid}} == true"
36
37 - - name: check-approval
38 template: approval-gate
39 arguments:
40 parameters:
41 - name: environment
42 value: "{{inputs.parameters.target_environment}}"
43 when: "{{steps.run-environment-specific-tests.outputs.parameters.tests_passed}} == true"
44
45 - - name: update-gitops-manifest
46 template: update-manifest
47 arguments:
48 parameters:
49 - name: environment
50 value: "{{inputs.parameters.target_environment}}"
51 - name: model_version
52 value: "{{inputs.parameters.model_version}}"
53 when: "{{steps.check-approval.outputs.parameters.approved}} == true"
54
55 - - name: wait-for-sync
56 template: wait-argocd-sync
57 arguments:
58 parameters:
59 - name: application
60 value: "{{inputs.parameters.model_name}}-serving"
61 when: "{{steps.update-gitops-manifest.outputs.parameters.updated}} == true"
62
63 - - name: verify-deployment
64 template: deployment-verification
65 arguments:
66 parameters:
67 - name: environment
68 value: "{{inputs.parameters.target_environment}}"
69 - name: model_version
70 value: "{{inputs.parameters.model_version}}"
71 when: "{{steps.wait-for-sync.outputs.parameters.synced}} == true"
72
73 # Template definitions...
74– Keep development, staging, production as similar as possible – Use same monitoring, testing, deployment patterns
GitOps can help control costs:
1# cost-control-policies.yaml
2apiVersion: kyverno.io/v1
3kind: ClusterPolicy
4metadata:
5 name: ml-cost-controls
6spec:
7 rules:
8 - name: limit-gpu-resources
9 match:
10 resources:
11 kinds:
12 - Deployment
13 namespaces:
14 - ml-training
15 - ml-serving
16 validate:
17 message: "GPU resources must be justified with business case"
18 pattern:
19 spec:
20 template:
21 spec:
22 containers:
23 - resources:
24 limits:
25 nvidia.com/gpu: "<=2"
26
27 - name: require-spot-instances-for-training
28 match:
29 resources:
30 kinds:
31 - Job
32 namespaces:
33 - ml-training
34 mutate:
35 patchStrategicMerge:
36 spec:
37 template:
38 spec:
39 nodeSelector:
40 cloud.google.com/gke-spot: "true"
41 tolerations:
42 - key: "cloud.google.com/gke-spot"
43 operator: "Equal"
44 value: "true"
45 effect: "NoSchedule"
46
47 - name: auto-scale-down-inactive-models
48 match:
49 resources:
50 kinds:
51 - Deployment
52 namespaces:
53 - ml-serving
54 generate:
55 kind: HorizontalPodAutoscaler
56 name: "{{request.object.metadata.name}}-hpa"
57 synchronize: true
58 data:
59 spec:
60 scaleTargetRef:
61 apiVersion: apps/v1
62 kind: Deployment
63 name: "{{request.object.metadata.name}}"
64 minReplicas: 1
65 maxReplicas: 10
66 metrics:
67 - type: Resource
68 resource:
69 name: cpu
70 target:
71 type: Utilization
72 averageUtilization: 50
73Track these metrics to improve your ML GitOps practice:
Phase 1 (Month 1): Foundations
Phase 2 (Month 2): Automation
Phase 3 (Month 3): Sophistication
Phase 4 (Ongoing): Optimization
Models can be large (GBs). Store model artifacts in dedicated registry (MLflow), not Git.
ArgoCD syncs can be slow for complex applications. Use application sets and health checks wisely.
Comprehensive testing increases deployment time. Implement parallel testing and intelligent test selection.
Too many deployment alerts cause ignoring. Tier alerts by severity and business impact.
Teams accustomed to manual deployment resist automation. Start with non-critical models, demonstrate value.
GitOps for machine learning transforms model deployment from a manual, error-prone process to an automated, reliable system. By treating models as code and applying GitOps principles, you gain:
Start small: pick one model, implement basic CI/CD, then gradually add sophistication. Remember that the goal isn't perfection—it's improvement over your current manual process.
The future of ML operations is declarative, automated, and Git-driven. Your models deserve nothing less.
mlops-production-deploymentLearn how to build your first machine learning pipeline using Kubeflow, containerize components, and orchestrate training-to-deployment workflows on Kubernetes.
Marcus Johnsonmlops-production-deploymentImplement comprehensive monitoring for production ML models with drift detection, performance tracking, and automated retraining triggers using open-source tools.
Sarah Chenmachine-learning-fundamentalsLearn the fundamentals of supervised learning by implementing linear regression from scratch without libraries. Understand gradient descent, loss functions, and the mathematics behind ML.
AI Editorial Team