মূল বিষয়বস্তুতে যান

Data Science Platform

Ilum is a comprehensive end-to-end data science platform that streamlines the entire machine learning lifecycle—from data exploration and model development to production deployment and monitoring. Built on enterprise-grade infrastructure, Ilum provides data scientists and ML engineers with powerful tools, seamless integrations, and automated workflows that accelerate innovation while maintaining scalability and reliability.

The Modern Data Science Challenge​

Traditional data science workflows are fragmented across multiple tools, requiring extensive setup, configuration, and maintenance. Data scientists spend more time on infrastructure management than on actual modeling and analysis. Common challenges include:

  • Complex Tool Integration: Connecting notebooks, data sources, compute engines, and deployment platforms
  • Environment Management: Setting up consistent development and production environments
  • Data Access Bottlenecks: Complicated data pipelines and access controls slowing down exploration
  • Model Lifecycle Management: Tracking experiments, versioning models, and managing deployments
  • Scaling Challenges: Moving from prototypes to production-ready, scalable solutions

Ilum's Unified Data Science Approach​

Ilum eliminates these challenges by providing a unified, cloud-native data science platform that integrates all essential components into a cohesive ecosystem. Our approach centers on four core principles:

1. Seamless Data Access​

Direct connectivity to modern data lake formats (Delta, Iceberg, Hudi, Paimon) through pre-configured catalogs, enabling instant access to enterprise datasets without complex setup.

2. Integrated Development Environment​

Production-ready notebooks with built-in Spark and Trino connectivity, comprehensive ML libraries, and collaborative features that support the entire data science workflow.

3. Automated MLOps​

End-to-end automation from experiment tracking and model registry to scheduled training pipelines and production deployment, reducing manual overhead and accelerating time-to-market.

4. Enterprise-Grade Infrastructure​

Scalable, secure, and compliant platform built on Kubernetes with advanced monitoring, resource management, and multi-cluster support for enterprise requirements.

Platform Architecture & Kubernetes Integration​

Ilum leverages a cloud-native architecture designed to run Spark-based data science workloads directly on Kubernetes. This design ensures resource isolation, dynamic scalability, and operational efficiency compared to legacy Hadoop Yarn setups.

Kubernetes Operator & Pod Lifecycle​

At the core of the platform is the Spark Operator, which manages the lifecycle of Spark applications as native Kubernetes Custom Resources (CRDs).

  • Pod-per-User Isolation: Each interactive session (Jupyter/Zeppelin) runs in its own dedicated Pod. This ensures that a memory leak or crash in one user's environment never impacts others.
  • Dynamic Executor Provisioning: When a user executes a Spark action, Ilum requests executors from the Kubernetes API. These pods are spun up on-demand and terminated immediately after the job completes, optimizing cloud costs.
  • Node Selectors & Taints: Workloads can be pinned to specific node pools (e.g., high-memory nodes for training, general-purpose for ETL) using standard Kubernetes affinity rules.

Resource Quotas & Limits​

Administrators can define granular ResourceQuota policies at the namespace level to control compute consumption:

apiVersion : v1 
প্রকার :  ResourceQuota
মেটাডেটা : 
  নাম : ডাটা - science- দল - একটি 
স্পেসিফিকেশন : 
  hard: 
    requests.cpu:  "100"
    requests.memory:  200Gi
    requests.nvidia.com/gpu:  "10"
    pods:  "50"

This prevents "noisy neighbor" issues where a single massive grid search consumes all available cluster resources.

Ilum Data Science Platform Overview

Why Choose Ilum for Data Science?​

Accelerated Development Cycles​

Ilum's pre-wired notebook environments eliminate setup friction, connecting directly to Spark clusters and data catalogs. Data scientists can load DataFrames from cataloged datasets without any additional plumbing, reducing time-to-insight from days to minutes.

Production-Ready from Day One​

Unlike traditional notebook environments that struggle with productionization, Ilum notebooks are designed for both exploration and production deployment. Code developed in notebooks can seamlessly transition to scheduled jobs and automated pipelines.

Comprehensive ML Library Support​

Built-in support for industry-standard libraries including scikit-learn, XGBoost, PyTorch, TensorFlow, and more. Starter notebooks and pipeline templates for common use cases (classification, regression, time-series) help teams quickly adopt best practices.

Enterprise MLOps at Scale​

Integrated experiment tracking, model registry, and automated deployment pipelines provide enterprise-grade MLOps capabilities without the complexity of managing multiple tools and platforms.

Core Data Science Features​

Pre-Configured Notebook Environments​

Ilum provides production-ready Jupyter and Zeppelin environments that are seamlessly integrated with the data platform. These environments are not just standalone containers but are deeply integrated into the cluster's networking and security mesh.

Instant Data Connectivity​

  • Direct Spark Integration: Notebooks act as Spark Drivers, connecting to executors within the same Kubernetes namespace via a headless service.
  • Catalog Access: Immediate access to Delta, Iceberg, Hudi, and Paimon tables through a shared Hive Metastore or Nessie catalog.
  • Multi-Engine Support: Choose between Spark (for batch/training) and Trino (for interactive query speed) within the same notebook.
  • সংস্করণ নিয়ন্ত্রণ : Built-in Git integration ensures all code is versioned, facilitating code review and CI/CD pipelines.

Advanced Dependency Management​

Managing Python dependencies in distributed Spark environments is a critical challenge. Ilum solves this through a multi-layered approach ensuring consistency between the Driver (Notebook) and Executors.

1. Runtime Environment (Conda/Virtualenv)​

For rapid prototyping, data scientists can install libraries directly within their session scope. These libraries are automatically shipped to executors using Spark's archive distribution mechanism.

# In-notebook installation
%pip install scikit-learn==1.3.0 torch==2.1.0

2. Immutable Docker Images​

For production stability, Ilum encourages the use of custom Docker images. Teams can build images containing their specific ML stack (e.g., specific CUDA versions for deep learning) and define them in the job configuration.

# Spark Profile Configuration
স্পেসিফিকেশন : 
  প্রতিচ্ছবি :  "registry.company.com/ml-team/pytorch-gpu:2.1.0-cuda11.8"
  imagePullPolicy :  Always

This guarantees that the exact same environment used during exploration is used for large-scale distributed training, eliminating "works on my machine" issues.

3. Shared Volume Mounts (PVCs)​

Persistent Volume Claims (PVCs) can be mounted to notebook pods to share large static assets (like pre-trained model weights or reference datasets) across the team without duplicating data.

Comprehensive ML Stack​

# Example: Loading data and building models with zero setup
আমদানি  pandas হিসেবে  pd
আমদানি এমএলফ্লো 
থেকে পাইস্পার্ক . এসকিউএল আমদানি স্পার্কসেশন 
থেকে স্কলার্ন . ensemble আমদানি  RandomForestClassifier
আমদানি  xgboost হিসেবে  xgb
আমদানি  torch

# Direct access to cataloged datasets
ডিএফ = স্ফুলিঙ্গ . সারণী ( "analytics.customer_features") 

# Seamless integration with ML libraries
মডেল =  RandomForestClassifier( n_estimators= 100 ) 
মডেল . fit( X_train,  y_train) 

# Automatic experiment tracking
সাথে এমএলফ্লো . start_run ( ) : 
এমএলফ্লো . log_params( মডেল . get_params( ) ) 
এমএলফ্লো . log_metric ( "যথার্থতা" ,  accuracy_score( y_test,  predictions) ) 
এমএলফ্লো . স্কলার্ন . log_model ( মডেল ,  "random_forest_model")

Model Development Workflow​

Data Lakehouse Reproducibility (Time Travel)​

A key requirement for MLOps is the ability to reproduce a specific model version. Ilum leverages ডেল্টা লেক এবং হিমশৈল capabilities to ensure that training data is immutable for a given version.

Data scientists can query the exact state of a dataset as it existed at the time of training, eliminating data drift issues during debugging:

# Train on the exact dataset version used in Experiment ID #452
df_train = স্ফুলিঙ্গ . রিড . format( "delta") \ 
    . বিকল্প ( "versionAsOf",  145) \ 
    . load( "s3a://warehouse/analytics/customer_features") 

# Or query by timestamp
df_validation = স্ফুলিঙ্গ . রিড . format( "iceberg") \ 
    . বিকল্প ( "as-of-timestamp",  "2023-10-25 12:00:00") \ 
    . load( "glue_catalog.default.transactions")

Model Development and Experiment Tracking

Starter Templates and Best Practices​

Ilum includes curated notebook templates for common ML scenarios:

  • Classification Problems: Binary and multi-class classification with feature engineering pipelines
  • Regression Analysis: Linear, polynomial, and ensemble regression models
  • Time Series Forecasting: ARIMA, Prophet, and deep learning approaches
  • Clustering and Segmentation: K-means, hierarchical, and density-based clustering
  • Deep Learning: PyTorch and TensorFlow templates for neural networks

Feature Engineering Pipeline​

# Example: Automated feature engineering pipeline
থেকে পাইস্পার্ক . ml. feature আমদানি  VectorAssembler,  StandardScaler
থেকে পাইস্পার্ক . ml আমদানি  Pipeline

# Define feature engineering pipeline
assembler =  VectorAssembler( inputCols= feature_columns,  outputCol= "features") 
scaler =  StandardScaler( inputCol= "features",  outputCol= "scaled_features") 

# Create reusable pipeline
feature_pipeline =  Pipeline( stages= [ assembler,  scaler] ) 
transformed_data =  feature_pipeline. fit( training_data) . transform( training_data)

MLOps and Model Lifecycle Management​

এক্সপেরিমেন্ট ট্র্যাকিং ​

Integrated এমএলফ্লো provides comprehensive experiment tracking:

  • Automatic Logging: Parameters, metrics, and artifacts tracked automatically
  • Experiment Comparison: Visual comparison of model performance across runs
  • Reproducibility: Complete environment and code versioning for reproducible results
  • Collaborative Tracking: Team-wide visibility into experiments and results

Model Registry and Versioning​

# Example: Model registration and lifecycle management
থেকে এমএলফ্লো . ট্র্যাকিং আমদানি MlflowClient 

ক্লায়েন্ট = MlflowClient ( ) 

# Register model with versioning
model_uri =  f"runs:/{ run_id} /model"
registered_model = ক্লায়েন্ট . create_registered_model ( "customer_churn_predictor") 

# Create model version
model_version = ক্লায়েন্ট . create_model_version ( 
নাম = "customer_churn_predictor", 
    source= model_uri, 
    run_id= run_id
) 

# Promote model through lifecycle stages
ক্লায়েন্ট . transition_model_version_stage( 
নাম = "customer_churn_predictor", 
সংস্করণ = model_version. সংস্করণ , 
    stage= "Production"
)

Automated Training and Inference Pipelines​

Declarative Pipeline Configuration​

Define training and inference pipelines using simple YAML configurations:

# training_pipeline.yaml
নাম :  customer_churn_training
schedule:  "0 2 * * *"  # Daily at 2 AM

data_sources: 
  -  ক্যাটালগ :  analytics
    সারণী :  customer_features
    ফিল্টার :  "created_date >= current_date() - interval 30 days"

preprocessing: 
  -  টাইপ :  feature_engineering
    কনফিগার : 
      numeric_features:  [ "age",  "tenure",  "monthly_charges"] 
      categorical_features:  [ "contract_type",  "payment_method"] 
      
মডেল : 
  টাইপ :  xgboost
  hyperparameters: 
    n_estimators:  100 
    max_depth:  6 
    learning_rate:  0.1
    
evaluation: 
  মেট্রিক্স :  [ "যথার্থতা" ,  "precision",  "recall",  "f1_score"] 
  validation_split:  0.2

deployment: 
  model_registry:  "customer_churn_predictor"
  stage:  "staging"
  auto_promote:  সত্য 
  promotion_criteria: 
    accuracy:  "> 0.85"

Scheduled Training Jobs​

# Example: Automated model retraining
থেকে ইলুম . jobs আমদানি  ScheduledJob
থেকে ইলুম . pipelines আমদানি  MLPipeline

# Define scheduled training job
training_job =  ScheduledJob( 
নাম = "customer_churn_retrain", 
    schedule= "0 2 * * 1",   # Weekly on Monday at 2 AM
    pipeline= MLPipeline. from_yaml( "training_pipeline.yaml") , 
ক্লাস্টার = "production-cluster", 
সংস্থান = { 
        "driver_memory":  "4g", 
        "executor_memory":  "8g", 
        "executor_instances":  5 
    } 
) 

# Deploy to production
training_job. deploy( )

Build & Deploy AI Applications​

AI Application Deployment

Ilum's "Build & Deploy AI Apps" feature enables rapid deployment of ML models as production-ready applications:

Model Serving Infrastructure​

  • Auto-scaling Endpoints: Automatically scale based on demand
  • A/B Testing: Built-in support for model comparison and gradual rollouts
  • Monitoring & Alerting: Real-time performance monitoring and anomaly detection
  • Security & Compliance: Enterprise-grade security with role-based access control

Application Templates​

# Example: Deploy model as REST API
থেকে ইলুম . deployment আমদানি  ModelApp,  ModelEndpoint

# Create application from registered model
অ্যাপ =  ModelApp( 
নাম = "churn-prediction-api", 
মডেল = "customer_churn_predictor", 
সংস্করণ = "latest"
) 

# Configure endpoint
শেষ বিন্দু =  ModelEndpoint( 
পথ = "/predict", 
    input_schema= { 
        "customer_id":  "স্ট্রিং" , 
        "features":  "array"
    } , 
    output_schema= { 
        "customer_id":  "স্ট্রিং" , 
        "churn_probability":  "float", 
        "risk_category":  "স্ট্রিং" 
    } 
) 

অ্যাপ . add_endpoint( শেষ বিন্দু ) 
অ্যাপ . deploy( ক্লাস্টার = "production-cluster")

Security Architecture: Identity & Network Isolation​

Ilum employs a "Defense in Depth" strategy critical for enterprise environments dealing with sensitive PII or financial data.

Identity Propagation (OAuth2)​

Security in Ilum is not just at the perimeter. We implement Identity Propagation, where the user's identity (via OAuth2/OIDC token) is passed from the Notebook session through to the Spark Driver and Executors.

  • Storage Access: When a Spark Executor reads from S3, it uses the user's credentials, not a generic service account. This ensures that file-level permissions defined in AWS IAM or MinIO Policies are strictly enforced.
  • Audit Trails: All data access logs in the storage layer reflect the actual user (e.g., [email protected] ) rather than a generic spark-user, satisfying strict compliance requirements (GDPR, HIPAA).

Network Policies & Namespace Isolation​

Ilum utilizes Kubernetes NetworkPolicies to isolate tenants:

  • Ingress Deny-All: By default, pods in a data science namespace cannot receive traffic from outside.
  • Egress Whitelisting: Notebooks can only connect to approved endpoints (e.g., PyPI, Maven, internal Git), preventing data exfiltration to unauthorized external servers.

Integration with Ilum Ecosystem​

Data Platform Integration​

  • Seamless Data Access: Direct connectivity to all Ilum-managed data sources
  • Catalog Integration: Automatic discovery of tables, schemas, and metadata
  • Lineage Tracking: Automatic data lineage generation for ML pipelines
  • Quality Monitoring: Built-in data quality checks and validation

Compute Engine Flexibility​

  • Spark Integration: Distributed computing for large-scale feature engineering
  • Trino Connectivity: High-performance analytics for exploratory data analysis
  • রিসোর্স অপটিমাইজেশন : Automatic resource allocation based on workload requirements
  • মাল্টি-ক্লাস্টার সমর্থন : Deploy across multiple clusters for scalability

Security and Governance​

  • Role-Based Access: Fine-grained permissions for data and model access
  • Audit Logging: Complete audit trail for compliance and governance
  • Model Governance: Approval workflows for model promotion and deployment
  • Data Privacy: Built-in support for data masking and privacy protection

Getting Started with Data Science in Ilum​

পূর্বশর্ত ​

  • Ilum core platform deployed
  • Notebook environments enabled (JupyterLab/JupyterHub)
  • MLflow experiment tracking configured
  • Access to data catalogs (Hive Metastore)

Quick Start Guide​

  1. Access Your Notebook Environment

    # Access from Ilum UI: Modules > JupyterLab
    # Or via direct URL: https://your-ilum-instance/jupyter

  2. Load Your First Dataset

    # Connect to Spark and load cataloged data
    থেকে পাইস্পার্ক . এসকিউএল আমদানি স্পার্কসেশন 

    স্ফুলিঙ্গ = স্পার্কসেশন . builder. অ্যাপের নাম ( "DataScience") . getOrCreate ( ) 
    ডিএফ = স্ফুলিঙ্গ . সারণী ( "analytics.customer_data") 
    ডিএফ . দেখান ( )

  3. Build Your First Model

    # Use starter template for classification
    থেকে ইলুম . templates আমদানি  ClassificationPipeline

    pipeline =  ClassificationPipeline( 
        target_column= "churn", 
        feature_columns= [ "age",  "tenure",  "monthly_charges"] 
    ) 

    মডেল =  pipeline. fit( ডিএফ ) 
    predictions = মডেল . transform( test_data)

  4. Track and Deploy

    # Automatic experiment tracking
    সাথে এমএলফ্লো . start_run ( ) : 
        # Training code here
    এমএলফ্লো . স্ফুলিঙ্গ . log_model ( মডেল ,  "churn_model") 

    # Deploy to production
    থেকে ইলুম . deployment আমদানি  deploy_model
    deploy_model( "churn_model", শেষ বিন্দু = "/predict/churn")

Advanced Data Science Workflows​

Distributed Training & GPU Acceleration​

For deep learning workloads that exceed the capacity of a single machine, Ilum provides native support for distributed training on Kubernetes.

Requesting GPU Resources​

Ilum integrates with the NVIDIA Device Plugin for Kubernetes. Data scientists can request GPUs directly from their notebook configuration or Spark job definition:

# Spark Executor Configuration
সংস্থান : 
  limits: 
    nvidia.com/gpu:  2

Distributed Strategies (Horovod / TorchDistributor)​

Instead of complex SSH setups, Ilum utilizes Spark's scheduling to manage distributed training contexts.

Example: PyTorch Distributed Training with Spark TorchDistributor

থেকে পাইস্পার্ক . ml. torch. distributor আমদানি  TorchDistributor

ডিএফ  train_fn( learning_rate) : 
    # Standard PyTorch training loop
    # ... 
    ফিরে ইতিহাস 

# Launch distributed training across 4 nodes with 1 GPU each
distributor =  TorchDistributor( 
    num_processes= 4 , 
    local_mode= False, 
    use_gpu= সত্য 
) 

distributor. চালনা ( train_fn,  1e-3)

Multi-Model Ensemble​

# Example: Ensemble learning with multiple algorithms
থেকে স্কলার্ন . ensemble আমদানি  VotingClassifier
থেকে  xgboost আমদানি  XGBClassifier
থেকে  lightgbm আমদানি  LGBMClassifier

# Define ensemble
ensemble =  VotingClassifier( [ 
    ( 'xgb',  XGBClassifier( ) ) , 
    ( 'lgb',  LGBMClassifier( ) ) , 
    ( 'rf',  RandomForestClassifier( ) ) 
] ) 

# Track ensemble experiments
সাথে এমএলফ্লো . start_run ( ) : 
    ensemble. fit( X_train,  y_train) 
    predictions =  ensemble. predict( X_test) 
    
এমএলফ্লো . log_metric ( "ensemble_accuracy",  accuracy_score( y_test,  predictions) ) 
এমএলফ্লো . স্কলার্ন . log_model ( ensemble,  "ensemble_model")

Distributed Deep Learning​

# Example: PyTorch distributed training
আমদানি  torch
আমদানি  torch. distributed হিসেবে  dist
থেকে  torch. nn. parallel আমদানি  DistributedDataParallel

# Initialize distributed training
dist. init_process_group( "nccl") 

# Define model and distribute
মডেল =  MyNeuralNetwork( ) 
মডেল =  DistributedDataParallel( মডেল ) 

# Train with automatic experiment tracking
সাথে এমএলফ্লো . start_run ( ) : 
    জন্য  epoch মধ্যে  সীমা ( num_epochs) : 
        train_loss =  train_epoch( মডেল ,  train_loader) 
        val_loss =  validate( মডেল ,  val_loader) 
        
এমএলফ্লো . log_metrics( { 
            "train_loss":  train_loss, 
            "val_loss":  val_loss
        } , পদক্ষেপ = epoch)

Real-Time Feature Engineering​

# Example: Streaming feature engineering
থেকে পাইস্পার্ক . এসকিউএল . functions আমদানি  * 
থেকে পাইস্পার্ক . এসকিউএল . types আমদানি  * 

# Define streaming transformations
ডিএফ  feature_engineering_pipeline( ডিএফ ) : 
    ফিরে ডিএফ . withColumn( "age_group",  
যখন ( col( "age")  <  25,  "young") 
                        . যখন ( col( "age")  <  65 ,  "adult") 
                        . otherwise( "senior") ) \ 
            . withColumn( "monthly_avg",  
                       col( "total_charges")  /  col( "tenure") ) 

# Apply to streaming data
streaming_features =  streaming_df. transform( feature_engineering_pipeline)

Performance Optimization​

Resource Management​

# Optimal resource configuration for ML workloads
spark_config: 
  ড্রাইভার : 
    memory:  "8g"
    cores:  4 
  executor: 
    memory:  "16g"
    cores:  8 
    instances:  10 
  
  # ML-specific optimizations
  spark.sql.adaptive.enabled:  সত্য 
  spark.sql.adaptive.coalescePartitions.enabled:  সত্য 
  spark.serializer:  org.apache.spark.serializer.KryoSerializer

Data Caching Strategies​

# Example: Intelligent data caching
# Cache frequently accessed training data
training_data = স্ফুলিঙ্গ . সারণী ( "features.training_set") 
training_data. cache( ) 

# Persist intermediate results
feature_engineered =  raw_data. transform( feature_pipeline) 
feature_engineered. persist( StorageLevel. MEMORY_AND_DISK) 

# Clean up cache when no longer needed
training_data. unpersist( )

Monitoring and Observability​

Model Performance Monitoring​

# Example: Production model monitoring
থেকে ইলুম . নজরদারি আমদানি  ModelMonitor

monitor =  ModelMonitor( 
model_name = "customer_churn_predictor", 
মেট্রিক্স = [ "যথার্থতা" ,  "precision",  "recall"] , 
    data_drift_threshold= 0.1, 
    performance_threshold= 0.8
) 

# Set up alerts
monitor. add_alert( 
    condition= "accuracy < 0.8", 
    action= "ইমেইল" , 
    recipients= [ " [email protected] " ] 
) 

monitor. deploy( )

Data Quality Validation​

# Example: Automated data quality checks
থেকে পাইস্পার্ক . এসকিউএল . functions আমদানি  * 

ডিএফ  data_quality_checks( ডিএফ ) : 
    checks =  { 
        "null_percentage": ডিএফ . ফিল্টার ( col( "target") . isNull( ) ) . গণনা ( )  / ডিএফ . গণনা ( ) , 
        "duplicate_percentage":  ( ডিএফ . গণনা ( )  - ডিএফ . dropDuplicates( ) . গণনা ( ) )  / ডিএফ . গণনা ( ) , 
        "data_freshness": ডিএফ . agg( max( "created_date") ) . collect( ) [ 0 ] [ 0 ] 
    } 
    
    # Log to MLflow
এমএলফ্লো . log_metrics( checks) 
    ফিরে  checks

Best Practices for Data Science in Ilum​

Development Workflow​

  1. Start with Exploration: Use notebooks for initial data exploration and hypothesis testing
  2. Modularize Code: Move proven code from notebooks to reusable modules
  3. Version Everything: Use Git integration for code and MLflow for experiments
  4. Test Early: Implement data validation and model testing from the beginning
  5. Monitor Continuously: Set up monitoring before deploying to production

Code Organization​

project/
├── notebooks/
│   ├── 01_data_exploration.ipynb
│   ├── 02_feature_engineering.ipynb
│   └── 03_model_development.ipynb
├── src/
│   ├── data/
│   │   ├── preprocessing.py
│   │   └── validation.py
│   ├── models/
│   │   ├── training.py
│   │   └── evaluation.py
│   └── deployment/
│       ├── app.py
│       └── monitoring.py
├── pipelines/
│   ├── training_pipeline.yaml
│   └── inference_pipeline.yaml
└── tests/
    ├── test_preprocessing.py
    └── test_models.py

Model Lifecycle Management​

  1. Experimentation Phase: Track all experiments with MLflow
  2. Development Phase: Use model registry for version control
  3. Staging Phase: Deploy to staging environment for validation
  4. Production Phase: Automated deployment with monitoring
  5. Monitoring Phase: Continuous performance and drift monitoring
  6. Retirement Phase: Graceful model retirement and replacement

Troubleshooting Common Issues​

Performance Issues​

  • Slow Data Loading: Optimize partition size and file format
  • Memory Errors: Adjust Spark executor memory and enable adaptive query execution
  • Long Training Times: Consider distributed training or feature selection

Environment Issues​

  • Library Conflicts: Use isolated conda environments in notebooks
  • Resource Contention: Monitor cluster utilization and adjust resource allocation
  • Network Connectivity: Verify catalog and storage connectivity

Model Deployment Issues​

  • Version Conflicts: Ensure model and serving environment compatibility
  • Performance Degradation: Monitor model drift and retrain as needed
  • Scaling Problems: Configure auto-scaling based on traffic patterns