ML Pipelines on Google Cloud
by Google Cloud Team · Google Cloud
Our Verdict
Worth it — with caveatsML Pipelines on Google Cloud is a short, advanced MLOps course (about 2 hours 52 minutes of video, marketed by Coursera as a one-week effort) taught by Google Cloud ML engineers, and it is genuinely the deepest free-to-audit walkthrough of TensorFlow Extended (TFX) pipelines you will find. Our independent editorial read of the official syllabus plus aggregated public student feedback is mixed: the conceptual lectures on TFX components, pipeline orchestration, CI/CD, ML Metadata, Cloud Composer and MLflow are strong, but the verified learner rating is only 3.3 out of 5 from 92 ratings on Coursera (and 3.0 on Pluralsight). The dominant, repeated complaint is that the hands-on Qwiklabs are outdated, suffer Python dependency errors, run on underpowered instances, and that staff do not respond on the discussion forums. As of mid-2026 the curriculum still centers on TFX, Kubeflow and AI Platform Pipelines rather than the current Vertex AI Pipelines workflow, so the labs in particular lag Google Cloud's own recommended stack. It is worth the audit for the TFX theory if you already work in MLOps, but treat the labs as unreliable.
The lecture content is a high-quality, Google-authored deep dive into TFX-based ML pipelines and is free to audit, but a verified 3.3/5 rating (92 ratings) and consistent reports of broken, outdated labs and unresponsive support make it conditional: take it for the TFX/MLOps concepts, not for reliable hands-on practice, and only if you already meet the heavy prerequisites.
Best for: Practicing ML engineers and MLOps practitioners who already build or deploy models and specifically want a structured deep dive into TensorFlow Extended (TFX) pipeline components, pipeline orchestration, CI/CD for pipelines, ML metadata, Cloud Composer and MLflow on Google Cloud. It also fits candidates preparing for the Google Cloud Professional Machine Learning Engineer exam who want conceptual coverage of production pipelines and are comfortable auditing for theory.
Skip if: Beginners or anyone new to machine learning, Python, or Google Cloud, since the course explicitly assumes a strong ML background and prior pipeline experience. Skip it if you want a current, hands-on Vertex AI Pipelines tutorial, if reliable working labs matter to you, or if you prefer responsive instructor support, because the labs are widely reported as outdated and forums go unanswered.
About This Course
Build and deploy ML pipelines on Google Cloud using TFX, Kubeflow, and Vertex AI for production ML workflows.
What You'll Learn
Curriculum
Covers TensorFlow Extended (TFX) as Google's production ML platform: standard pipeline components and the role of ML Metadata.
How TFX pipelines are orchestrated, including the orchestration layer and execution of pipeline runs.
Building custom TFX components and applying continuous integration / continuous deployment to pipeline workflows.
Managing, tracking, and querying ML metadata produced by TFX pipeline runs.
Continuous training using multiple SDKs and reusing pipelines across TensorFlow, PyTorch, scikit-learn, and XGBoost on Kubeflow and AI Platform Pipelines.
Using Cloud Composer (managed Apache Airflow) to orchestrate continuous training pipelines.
Using MLflow to manage the complete machine learning lifecycle, including tracking and model management.
Prerequisites
- A solid machine learning background with experience creating and deploying ML models
- Prior hands-on experience building ML pipelines
- Completion of the 'Machine Learning with TensorFlow on Google Cloud' specialization (or at least several of its courses)
- Completion of the 'MLOps (Machine Learning Operations) Fundamentals' course
- Working Python proficiency and familiarity with the Google Cloud console / Qwiklabs
Instructor
Google Cloud Team
Instructor · Google Cloud
Pros & Cons
Pros
- Authored and taught by Google Cloud ML engineers, giving an authoritative, in-depth treatment of TFX internals that is hard to find elsewhere
- Broad MLOps coverage in a short format: TFX, pipeline orchestration, CI/CD, ML metadata, Kubeflow/AI Platform Pipelines, Cloud Composer, and MLflow
- Free to audit on Coursera, so you can watch all the lecture material without paying
- Cross-framework focus (TensorFlow, PyTorch, scikit-learn, XGBoost) makes the pipeline concepts transferable beyond TensorFlow
- Useful conceptual prep for the Google Cloud Professional ML Engineer certification's production-pipeline topics
Cons
- Hands-on Qwiklabs are widely reported as outdated, with Python dependency/package errors and underpowered lab instances that make assignments hard to finish by following the instructions
- Curriculum and labs still center on TFX, Kubeflow and AI Platform Pipelines rather than the current Vertex AI Pipelines workflow, so the practical stack lags Google Cloud's own recommendations as of 2026
- Multiple learners report that course staff do not reply on the discussion forums, leaving lab issues unresolved
- Steep, explicitly stated prerequisites make it inaccessible to anyone without prior ML and pipeline experience
Alternatives To Consider
Frequently Asked Questions
Is ML Pipelines on Google Cloud free?
ML Pipelines on Google Cloud is $49/mo. Free to audit on Coursera (lecture materials accessible without payment). Full access with graded labs and a certificate requires a Coursera subscription (about $49/month); financial aid is available. Also offered on Pluralsight under a separate subscription.
Who is ML Pipelines on Google Cloud for?
Practicing ML engineers and MLOps practitioners who already build or deploy models and specifically want a structured deep dive into TensorFlow Extended (TFX) pipeline components, pipeline orchestration, CI/CD for pipelines, ML metadata, Cloud Composer and MLflow on Google Cloud. It also fits candidates preparing for the Google Cloud Professional Machine Learning Engineer exam who want conceptual coverage of production pipelines and are comfortable auditing for theory.
What will you learn in ML Pipelines on Google Cloud?
Understand TensorFlow Extended (TFX), its standard pipeline components, and the ML Metadata store; Orchestrate TFX pipelines and build custom components; Apply continuous integration and continuous deployment (CI/CD) practices to ML pipelines; Run continuous training using multiple SDKs with Kubeflow Pipelines and AI Platform Pipelines.
What are the prerequisites for ML Pipelines on Google Cloud?
A solid machine learning background with experience creating and deploying ML models; Prior hands-on experience building ML pipelines; Completion of the 'Machine Learning with TensorFlow on Google Cloud' specialization (or at least several of its courses); Completion of the 'MLOps (Machine Learning Operations) Fundamentals' course; Working Python proficiency and familiarity with the Google Cloud console / Qwiklabs.
Is ML Pipelines on Google Cloud worth it?
The lecture content is a high-quality, Google-authored deep dive into TFX-based ML pipelines and is free to audit, but a verified 3.3/5 rating (92 ratings) and consistent reports of broken, outdated labs and unresponsive support make it conditional: take it for the TFX/MLOps concepts, not for reliable hands-on practice, and only if you already meet the heavy prerequisites.
How we reviewed this course
This is an independent editorial assessment by Cursarium, based on Google Cloud's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.
Sources
- ML Pipelines on Google Cloud - official Coursera course page (specialization)
- ML Pipelines on Google Cloud - Coursera reviews (3.3/5, 92 ratings; outdated-labs feedback)
- ML Pipelines on Google Cloud - Pluralsight (syllabus, ~2h52m, 3/5 rating)
- ML Pipelines on Google Cloud - Class Central listing (free-audit, syllabus)