> ML_LIBRARY // SPARK-MLLIB_v1.0
Apache Spark MLlib
Apache Software Foundation — Scalable machine learning library for distributed big data clusters.
distributed-computationv3.5.3Apache-2.0qualified
Model Training
Accelerators:
CPUCUDA
Distributed Training:Yes
Model Inference
Inference Accelerators:
CPUCUDA
Deployment Targets:server
What It Does
- +Distributed training for classical algorithms (logistic regression, trees, k-means, ALS)
- +Uniform ML Pipeline abstraction (Transformers and Estimators)
- +Scale transparently across thousands of CPU nodes
What It Does Not Do
- -Natively train modern deep learning transformers or LLMs
- -Deliver sub-10ms single-record real-time online inference
- -Run inside lightweight edge runtimes
>Suitable Work Types
- Petabyte-scale tabular feature engineering and training
- Collaborative filtering on massive user-item interaction matrices
- Enterprise batch scoring pipelines in Hadoop/Kubernetes
>Unsuitable Work Types
- Real-time online microsecond transaction scoring
- Deep neural network computer vision and audio modeling
Data Residency Implications
Distributed across enterprise data lake nodes.
Security Considerations
Enforce Kerberos, TLS encryption in transit, and role-based ACLs.
Operational Profile & Known Limitations
Maturity:mature
Learning Curve:high
Ops Complexity:high
Cost Tier:high-compute
> Known Limitations:
- JVM garbage collection pauses and high driver-worker coordination overhead.
- High latency for single-record scoring.
Associated Incident Patterns (Incidentpedia)
Enforce safeguards and monitoring to guard against these documented real-world failure modes:
> Primary Evidence & Benchmark Citations
Apache Spark Machine Learning Library (MLlib) Guideofficial-docs • >=3.3.0, <=3.5.x
2026-09-25HIGH