Limited Time Offer!

For Less Than the Cost of a Starbucks Coffee, Access All DevOpsSchool Videos on YouTube Unlimitedly.
Master DevOps, SRE, DevSecOps Skills!

Enroll Now
Uncategorised

Learning Roadmap for MLOps and Machine Learning

November 14, 2024 Leave a Comment

Below is a structured table of problem areas, each with a primary and secondary tool recommendation to guide your learning in MLOps and Machine Learning. This table will serve as a roadmap, helping you learn and master the essential skills and tools in each area.

Problem AreaDomainMost Recommended ToolSecond Recommended ToolDescription / Learning Path
Foundational KnowledgeMLOps IntroductionN/AN/AStart with MLOps basics, covering CI/CD for ML, model lifecycle, and pipeline fundamentals. Resources: Courses, documentation on MLOps concepts from Google, Microsoft, or AWS.
Environment SetupContainersDockerPodmanLearn Docker basics for containerizing models, deploying environments, and bundling dependencies. Essential for reproducible environments.
Container OrchestrationKubernetesOpenShiftMaster Kubernetes for managing containerized workloads at scale. Start with basics (pods, deployments), then explore more complex topics (networking, storage).
Data ManagementWorkflow OrchestrationApache AirflowPrefectUse Airflow to create data pipelines and schedule ETL workflows, Prefect for simpler, Pythonic workflows. Build basic to complex data processing pipelines.
Feature Engineering & StorageFeast (Feature Store)Delta LakeFeast handles feature storage and serving, especially for real-time ML. Delta Lake helps manage data lineage and data versions.
Experiment TrackingExperiment LoggingMLflowWeights & Biases (W&B)Start with MLflow for tracking experiment parameters, results, and metadata. W&B offers a richer interface and deeper integrations.
VisualizationTensorBoardWeights & Biases (W&B)TensorBoard is ideal for visualizing deep learning training. W&B provides broader visualization across models and datasets.
Model VersioningModel Tracking & RegistryMLflowDVC (Data Version Control)MLflow handles model versioning and packaging; DVC offers data and model versioning in Git for reproducibility.
Model TrainingTraining EnvironmentJupyter NotebooksGoogle ColabUse Jupyter for local experiments, Google Colab for cloud-based training with GPU access. Develop familiarity with these interactive environments.
Framework – Classical MLscikit-learnXGBoostStart with scikit-learn for foundational ML algorithms; XGBoost for more complex ensemble models. Great for both experimentation and deployment readiness.
Framework – Deep LearningPyTorchTensorFlowPyTorch for flexible, research-oriented workflows; TensorFlow for large-scale, production-grade models. Learn basics, then progress to advanced training techniques.
Distributed TrainingHorovodDistributed TensorFlowHorovod integrates with PyTorch and TensorFlow, making distributed training simpler. Useful for handling large datasets and models.
Model Testing & ValidationUnit TestingPytestUnittestPytest is versatile and widely used for writing test cases; Unittest provides a more basic alternative in Python’s standard library.
Data ValidationGreat ExpectationsPanderaGreat Expectations is a robust tool for data quality checks; Pandera integrates with Pandas for schema and data validation.
Model TestingDeepchecksalibi-detectDeepchecks automates tests for data and model validation, alibi-detect helps detect data and concept drift.
Model DeploymentModel ServingTensorFlow ServingTorchServeTensorFlow Serving and TorchServe are model-serving frameworks optimized for TensorFlow and PyTorch, respectively. They streamline deployment into production.
API CreationFastAPIFlaskFastAPI is ideal for building APIs for model inference; Flask is simpler but also effective for deploying models.
Kubernetes IntegrationKubernetesKnativeKubernetes manages containerized deployments; Knative simplifies serverless deployments on Kubernetes.
Monitoring & LoggingInfrastructure MonitoringPrometheus + GrafanaDataDogPrometheus and Grafana are open-source tools for monitoring metrics; DataDog is a more complete observability platform with ML integrations.
Model MonitoringEvidently AIFiddler AIEvidently AI monitors model drift, performance degradation, and data quality; Fiddler AI adds explainability and additional ML-specific metrics.
LoggingELK Stack (Elasticsearch, Logstash, Kibana)FluentdELK Stack is widely used for centralized logging; Fluentd is an alternative for aggregating logs across environments.
CI/CD in MLOpsCI/CD PipelinesGitHub ActionsJenkinsGitHub Actions integrates directly with GitHub for CI/CD; Jenkins is highly customizable for more complex CI/CD pipelines.
CI/CD in Data PipelinesDVC PipelinesTectonDVC Pipelines are Git-integrated for version-controlled ML pipelines; Tecton supports feature pipelines for real-time model deployment.
CI/CD in Model PipelinesKubeflow PipelinesMLflow PipelinesKubeflow Pipelines is Kubernetes-native for end-to-end ML workflows; MLflow Pipelines allows for modular pipeline building in MLflow.

Suggested Learning Plan

  1. Start with Foundations: Learn MLOps basics, environment setup with Docker and Kubernetes, and workflow orchestration with Apache Airflow or Prefect.
  2. Model Experimentation and Tracking: Work with Jupyter Notebooks, MLflow for experiment tracking, and try basic visualizations with TensorBoard.
  3. Model Training and Testing: Gain experience with PyTorch/TensorFlow for deep learning and scikit-learn for classical ML. Use Pytest and Great Expectations for testing workflows.
  4. Model Packaging and Versioning: Use MLflow for tracking and model versioning, and Docker for containerizing models.
  5. Deployment and Monitoring: Practice deploying models using TensorFlow Serving or FastAPI, and set up monitoring with Prometheus and Grafana.
  6. Advanced CI/CD Workflows: Explore CI/CD with GitHub Actions or Jenkins, and dive into Kubeflow Pipelines for building end-to-end MLOps pipelines.
Rajesh Kumar
Follow me
Latest posts by Rajesh Kumar (see all)
Subscribe
Notify of
guest
0 Comments
Newest
Oldest Most Voted
Inline Feedbacks
View all comments
0
Would love your thoughts, please comment.x
()
x