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DevOps to MLOps Bootcamp– Build & Deploy ML Systems · LearnSpace
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courseraIT и технологии

DevOps to MLOps Bootcamp– Build & Deploy ML Systems

Курс от Packt
Средний≈ 15.9 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Master the entire lifecycle of building and deploying machine learning systems in production with this hands-on DevOps to MLOps Bootcamp. You'll learn how MLOps optimizes model development, deployment, and monitoring, gaining skills in tools like Docker, Kubernetes, MLflow, FastAPI, Streamlit, Prometheus, and GitHub Actions. This course bridges the gap between data science and scalable ML infrastructure. You'll explore MLOps concepts, trace its evolution through LLMOps and AgenticAIOps, and study real-world case studies. Apply these principles through a regression-based house price prediction project. The course covers CI pipelines with GitHub Actions and advanced production systems with Kubernetes, KEDA, and ArgoCD. The final sections focus on monitoring, autoscaling, and implementing GitOps pipelines for ML/LLM app deployment. Ideal for data scientists, ML engineers, DevOps pros, and developers, the course requires basic Python, ML knowledge, and container familiarity. By the end, you'll deploy models with containerized APIs and manage scalable systems.

Навыки, которые вы освоите

Model DeploymentModel OptimizationData PreprocessingApplication DeploymentModel EvaluationMachine Learning SoftwareDevops ToolsData ProcessingModel Training

Программа курса

8 модулей · 97 учебных материалов

01Introduction to MLOps9 материалов

Introduction to MLOps

Course IntroductionВидеоFull Course ResourcesЧтениеWhat is MLOps?ВидеоStory of Evolution of MLOps, LLMOps and AgenticAIOpsВидео

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Packt - Course Instructors

Преподаватель курса

DevOps to MLOps Bootcamp– Build & Deploy ML Systems
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Обучение откроется на Coursera
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Обучение на Coursera

≈ 15.9 ч

8 модулей

Язык: Английский

Часть программы вашего университета
Comparing Three Approaches to AIВидео
MLOps Case Studies – Learning from the PioneersВидео
Comparing DevOps vs MLOpsВидео
Emergence of MLOps EngineerВидео
Introduction to MLOps - AssessmentЗадание
02Getting Started with the Use Case and Environment Setup12 материалов

Getting Started with the Use Case and Environment Setup

Module IntroВидеоUse Case – House Price Predictor – RegressionВидеоUnderstanding End to End ML Practices and MLOpsВидеоEnvironment Setup OverviewВидеоSetting up Docker / Podman with ComposeВидеоLaunching MLflow for Experiment TrackingВидеоUnderstanding the Project Directory and ScaffoldВидеоSetting up Python Virtual Environment with UVВидеоWorking with Jupyter NotebooksВидеоSummaryВидеоUnderstanding Machine Learning Project Workflow and Environment SetupDIALOGUEGetting Started with the Use Case and Environment Setup - AssessmentЗадание
03From Raw Data to Models12 материалов

From Raw Data to Models

Module IntroВидеоLearning Data EngineeringВидеоExperimental Data AnalysisВидеоUnderstanding Feature Engineering ConceptsВидеоBuilding New Features for House Price PredictorВидеоPreparing for Model ExperimentationВидеоData Splitting with x_train, y_train, x_test, y_testВидеоDefining Algorithms and Hyperparameter GridsВидеоRunning Model Experiments to Find the Best Model and HyperparametersВидеоModule SummaryВидеоUnderstanding Data Science and ML Workflow for MLOpsDIALOGUEFrom Raw Data to Models - AssessmentЗадание
04Packaging Model along with FastAPI Wrapper and Streamlit with Containers12 материалов

Packaging Model along with FastAPI Wrapper and Streamlit with Containers

Module IntroВидеоHandover from Data Scientist to ML Engineer / MLOpsВидеоRunning Feature Engineering and Preprocessing JobsВидеоBuilding and Training Final Model with Configs from Data ScientistsВидеоWrapping the Model with FastAPI with Streamlit Client AppsВидеоWriting Dockerfile to Package Model with FastAPI WrapperВидеоDebugging and Fixing Image Failures, Launch and Validate FastAPIВидеоPackaging and Testing Streamlit AppВидеоPackaging and Model Serving Infra with Docker ComposeВидеоSummaryВидеоPackaging ML Models with FastAPI and Docker ComposeDIALOGUEPackaging Model along with FastAPI Wrapper and Streamlit with Containers - AssessmentЗадание
05Setting up MLOps CI Workflow with GitHub Actions12 материалов

Setting up MLOps CI Workflow with GitHub Actions

Module IntroВидеоDAGs, GitHub Actions and our MLOps CI WorkflowВидеоUnderstanding GitHub Actions SyntaxВидеоWriting and Executing Our First GitHub Actions WorkflowВидеоAdding Data and Feature Engineering Steps with Model TrainingВидеоModel Training Step with MLFlow for TrackingВидеоAdding Image Build and Publish Step with DockerВидеоConfiguring Registry Token and Publishing Image to DockerHubВидеоModular, Multi-Stage MLOps CI Workflow PipelineВидеоSummaryВидеоCreating and Managing an MLOps CI Pipeline with GitHub ActionsDIALOGUESetting up MLOps CI Workflow with GitHub Actions - AssessmentЗадание
06Building Scalable Prod Inference Infrastructure with Kubernetes13 материалов

Building Scalable Prod Inference Infrastructure with Kubernetes

Module IntroВидеоDesigning Scalable Infrastructure for Model InferenceВидеоIntroduction to Kubernetes for Machine LearningВидеоKubernetes Core Concepts – Pods, Deployments and ServicesВидеоSimplest Way to Build a 3 Node Kubernetes Cluster with KINDВидеоDeploying Streamlit Frontend App with KubernetesВидеоExposing the Streamlit App with Kubernetes NodePort ServiceВидеоCreating Deployment & Service for the Model Wrapped in FastAPIВидеоConnecting Streamlit with Model using Kubernetes Native DNS Based Service DiscoveryВидеоEasy Way to Generate Kubernetes Manifests and YAMLВидеоSummaryВидеоDeploying a Scalable ML Model Inference Infrastructure with KubernetesDIALOGUEBuilding Scalable Prod Inference Infrastructure with Kubernetes - AssessmentЗадание
07Monitoring and Autoscaling an ML Model16 материалов
Module IntroВидеоProject SpecВидеоInstalling Prometheus and Grafana with HelmВидеоExploring Monitoring Metrics with Grafana and PrometheusВидеоAdding Instrumentation for FastAPI along with Custom DashboardВидеоAutomatic Capacity Scaling ConceptsВидеоInstalling KEDA and Configuring Resource SpecВидеоConfiguring Scaled Objects with KEDAВидеоGetting Started with Load Testing Model InferenceВидеоAI Based Troubleshooting Monitoring with ChatGPTВидеоRunning Load Test and Analyzing AutoscalingВидеоCPU Based Auto Scaling with KEDAВидеоAdding a Vertical Pod Autoscaler (VPA)ВидеоSummaryВидеоImplementing and Troubleshooting Kubernetes Model Autoscaling with MonitoringDIALOGUEMonitoring and Autoscaling an ML Model - AssessmentЗадание
08GitOps Based Deployments for ML/LLM Apps11 материалов
Module IntroВидеоGitOps ConceptsВидеоGitOps Principle 2: Start Revision Controlling the CodeВидеоGitOps Principle 4: Setup an Agent - ArgoCDВидеоOverview of Argo Application CRDВидеоContinuous Delivery with ArgoCD ApplicationsВидеоEnd-to-End CI and CD Pipelines for ML AppВидеоSummaryВидеоGitOps Based Deployments for ML/LLM Apps - AssessmentЗаданиеFull Course Practice AssessmentЗаданиеFull Course AssessmentЗадание