Linkedin Learning – MLOps Fundamentals Of CI CD And Model Deployment 2026 BOOKWARE-LERNSTUF | Apps-Tutorials | MKV | 552.43 MiB
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Linkedin Learning
MLOps Fundamentals Of CI CD And Model Deployment
2026
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Release Date : 2026-05-17
Publisher : Linkedin Learning
Title : MLOps Fundamentals Of CI CD And Model Deployment
Year : 2026
Month : 05
Category : Unknown
Language : English
Section : BOOKWARE
Files : 03 x 200 MB
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https://www.linkedin.com/learning/mlops-fundamentals-of-ci-cd-and-model-deploy
ment
As machine learning becomes central to modern software systems, DevOps
engineers need new skills to manage models in production. This course
introduces machine learning operations (MLOps) and covers how it extends
DevOps practices to include data science workflows. Get an overview of the
MLOps lifecyclefrom CI/CD and continuous training to monitoring and
governanceto learn how DataOps, ModelOps, and DevOps work together. Find out
how to collect and prepare data using tools like Pandas, Apache Spark, and
Apache Kafka, then explore feature stores and pipeline orchestration with
Airflow and Prefect. You get hands-on with MLflow for experiment tracking and
model management, and BentoML for model deployment and serving. Along the way,
learn about monitoring with Prometheus, Grafana, and Evidently, and how to
address common data privacy, security, and compliance issues with GDPR, HIPAA,
and PCI standards. Note: This course was created by KodeKloud. We are pleased
to host this training in our library.
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.. - - _//- ___\____---' [ R E C R U i T i N G ] . - _\_ ___--'-_ -- -_ .
iF YOU HAVE ANYTHiNG TO OFFER
CONTACT US ViA THE SCENE, OR EMAiL ADDRESS BELOW
... . ___.
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GREETiNGS TO ALL THE PAST AND PRESENT SCENE GROUPS AND SCENERS WHO MADE
STUF AVAiLABLE FOR THOSE THAT MAY NOT OTHERWISE HAD THE PRiViLEGE
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.. -_- -/_- __--\__ [ C O N T A C T D E T A i L S ] --- -___-- - \ - .
lernstuf [at] proton [dot] me
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23M 001-course_introduction.mkv 8.1M 002-getting_started_with_the_machine_learning_team.mkv 6.9M 003-introduction_to_the_mlops_engineer_role.mkv 11M 004-comparison_with_devops_mlops_dataops_devops_modelops.mkv 6.0M 005-mlops_lifecycle.mkv 13M 006-continuous_integration_ci_continuous_deployment_cd_continuous_training_ct_and_continuous_monitoring_cm.mkv 9.8M 007-finding_and_exploring_the_right_tools_from_devops_for_mlops.mkv 7.3M 008-mlops_architecture.mkv 6.0M 009-data_collection_and_preparation.mkv 5.0M 010-data_ingestion_etl.mkv 7.0M 011-data_lakes.mkv 8.0M 012-data_cleaning_and_data_transformation.mkv 12M 013-small_and_medium_sized_datasets_and_data_transformation_pandas_and_polars.mkv 49M 014-demo_small_and_medium_sized_datasets_and_data_transformationpandas_and_polars.mkv 5.7M 015-large_datasets_apache_spark_pyspark_and_dask.mkv 9.2M 016-streaming_datasets_apache_kafka_and_apache_flink.mkv 30M 017-demo_stream_data_using_apache_kafka.mkv 18M 018-what_is_feature_store.mkv 12M 019-data_pipeline_orchestration_airflow_and_prefect.mkv 43M 020-demo_data_pipeline_orchestration.mkv 8.8M 021-model_development.mkv 5.1M 022-model_training_and_hyperparameter_tuning.mkv 8.9M 023-world_of_cpus_and_gpus.mkv 6.5M 024-introduction_mlflow.mkv 5.1M 025-demo_setting_mlflow.mkv 12M 026-demo_running_an_experiment_and_storing_the_result_on_mlflow.mkv 4.5M 027-demo_mlflow_model_artifact_and_versioning.mkv 15M 028-model_serving.mkv 18M 029-model_drift_and_online_offline_serving.mkv 6.4M 030-model_deployment_and_serving.mkv 29M 031-demo_model_serving_using_bentoml.mkv 12M 032-demo_upgrading_model_versions_with_bentoml_serving.mkv 6.2M 033-monitoring_tools_prometheus_grafana_and_evidently.mkv 12M 034-deploy_app_for_insurance_agents_to_upload_all_insurance_claims.mkv 6.8M 035-demo_generate_dummy_data_for_the_project.mkv 7.9M 036-demo_set_up_mlflow_server_and_run_the_ml_experiment.mkv 6.4M 037-demo_register_the_model_and_setup_bentoml_for_serving_ml_models.mkv 16M 038-demo_upgrade_python_flask_app_to_connect_to_bentoml_for_online_serving.mkv 17M 039-data_privacy_and_data_security.mkv 11M 040-data_access_management.mkv 8.5M 041-data_retention.mkv 7.5M 042-need_of_compliance_and_gdpr.mkv 5.6M 043-hipaa_compliance.mkv 6.7M 044-pci_compliance.mkv 8.0M 045-compliance_consequences_and_penalties.mkv 3.5M 046-compliance_summary.mkv 8.5M 047-overview_of_sagemaker.mkv 8.1M 048-core_components_of_sagemaker.mkv 2.7M 049-mlops_with_sagemaker.mkv 652K LERNSTUF 648K LERNSTUF/lernstuf_introduction_2025.rar 553M total
File: 001-course_introduction.mkv Size: 23116796 bytes (22.05 MiB), duration: 00:03:31, avg.bitrate: 876 kb/s Audio: aac, 44100 Hz, stereo Video: h264, yuv420p, 1920x1080, 29.97 fps(r) (eng) Subtitles: eng
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Keywords: Linkedin, Learning, 8211, MLOps, Fundamentals, And, Model, Deployment, 2026, BOOKWARE, LERNSTUFDownload from Nitroflare
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