Udacity Machine Learning Engineer Nanodegree Review (2026): AWS vs Azure Variants

Last updated: 11 October 2026 (syllabus, prices and AWS exam status re-read live). Written by Josh Hutcheson, OnlineCourseing editor. See our review methodology.

Some links on this page are affiliate links: if you enroll through them we may earn a commission at no extra cost to you. It does not change our verdict, and several of the alternatives we name pay us nothing.

Udacity’s AWS Machine Learning Engineer Nanodegree (nd189) teaches you to train, deploy and run machine learning models on Amazon SageMaker, with AWS Lambda and Step Functions for the plumbing. Udacity built it with AWS. We re-read the program page, its full outline and the current AWS certification pages on 11 October 2026; this review covers what the program actually teaches, what it costs, how it relates to the AWS Machine Learning Engineer Associate exam, and how it compares with cheaper options and with Udacity’s Azure version.

QUICK VERDICT

See the AWS ML Engineer Nanodegree →

How Much Does the AWS ML Engineer Nanodegree Cost?

Udacity sells the program inside its catalog subscription or as a one-time purchase. These were the prices on the program page on 11 October 2026:

On 11 October Udacity was advertising 40% off any plan for new members, and the discounted figures above are what the page showed with that offer applied. Our link carries the reader code onlinecourseing40, which is the same 40%. If Udacity is running a bigger sale when you read this, check that your cart shows the larger discount before you pay.

At today’s 50% off, four months on the bundle cost $423, less than the $499.50 one-time price, so the subscription wins if you finish within about four months. Buy the program outright if you expect to take five months or more; it covers this program alone, while the subscription also opens Udacity’s other Nanodegrees, including the Generative AI and Agentic AI programs.

Check the current price on Udacity →

Prefer the Azure stack? See the Azure Machine Learning Engineer variant, reviewed further down this page.

AWS ML Engineer Nanodegree at a Glance

What Is the AWS ML Engineer Nanodegree?

nd189 is an intermediate program for people who can already write Python and know what a training set is. It walks through the machine learning lifecycle the way AWS teams run it: explore and label data in SageMaker Studio, train classical and deep learning models, wire them into automated workflows, then make them cheap, scalable and secure in production. Each stage ends in a project that a Udacity reviewer grades.

It is a SageMaker program first. You will spend far more time on SageMaker training jobs, endpoints, pipelines, Debugger, Clarify and Model Monitor than on model theory, which suits engineers and data scientists moving into ML engineering. If you want the math and algorithms, start with a course from our machine learning courses guide instead.

Curriculum Breakdown

This is the outline from Udacity’s program page on 11 October 2026, with Udacity’s own pacing.

1. Introduction to Machine Learning (18 hours, about 4 weeks)

Exploratory data analysis in SageMaker Studio with Data Wrangler and Pandas, labeling new data with SageMaker Ground Truth, the ML lifecycle, supervised versus unsupervised learning, and training and tuning linear, tree-based, XGBoost and AutoGluon models with scikit-learn and SageMaker JumpStart. Project: predict bike-sharing demand with AutoGluon and climb a Kaggle-style leaderboard.

2. Developing Your First ML Workflow (about 3 weeks)

SageMaker essentials (training jobs, endpoints, batch transform and processing jobs), then designing a workflow with AWS Lambda and Step Functions and monitoring it, with Feature Store, Clarify and Model Monitor among the listed skills. Project: build and ship an image-classification workflow for Scones Unlimited, a fictional scone-delivery company.

3. Deep Learning Topics with Computer Vision and NLP (about 3 weeks)

Neural networks, cost functions and optimization, convolutional networks, transformer architectures and fine-tuning, then training and deploying deep learning models (PyTorch) on SageMaker. Project: fine-tune a pretrained image classifier using SageMaker profiling, debugging and hyperparameter tuning.

4. Operationalizing Machine Learning on SageMaker (about 4 weeks)

Managing compute to cut cost, distributed training on large datasets, pipelines for high-throughput, low-latency serving, and designing secure ML projects on AWS (VPCs and cloud security). Project: take a model to production-grade deployment with EC2, Lambda, autoscaling, concurrency settings and performance monitoring.

5. Capstone (about 1 month)

You choose a real problem, submit a proposal (dataset, evaluation criteria and AWS approach) for approval, then build it end to end, from data preparation and training to deployment and validation.

What You Actually Build

The operationalization project and the capstone are the two to polish for interviews: they show cost, scaling and monitoring decisions, which is what separates an ML engineer from a notebook-only data scientist.

What It Does Not Cover

The program page shows a last update of 6 August 2025. It has transformer and text-classification lessons, but nothing on Amazon Bedrock, foundation-model APIs, retrieval-augmented generation or agents. That matters for two reasons: AWS lists Bedrock in the target profile for the new MLA-C02 exam (below), and many 2026 ML engineer job posts ask for LLM experience. If that is your direction, the Udacity Generative AI Nanodegree or Agentic AI Nanodegree fills the gap, and both are in the same subscription.

It is also AWS-specific. The concepts transfer to Azure ML or Google Vertex AI, but the tooling does not; see the Azure variant and our AWS vs Azure comparison if you have not settled on a cloud.

Cloud Costs: What Udacity Covers

You do not need your own paid AWS account. Udacity’s support guidance says it “sets a fixed budget or credits for each student” in its AWS Cloud Labs, launched from the classroom. The budget is finite, so delete endpoints, stop notebook instances and clean up training resources when you finish a session; running out mid-program is the real cost risk, not a surprise bill.

Does It Prepare You for the AWS ML Certification?

AWS has one machine learning certification now. The Machine Learning Specialty was retired (last exam day 31 March 2026; holders stay certified for three years), and the AWS Certified Machine Learning Engineer Associate is mid-update:

The Nanodegree covers much of the SageMaker material the exam tests (data preparation, training and tuning, deployment, pipelines and monitoring), but it is not exam prep: there are no practice exams, and AWS’s MLA-C02 candidate profile adds Amazon Bedrock, which the program does not teach. For the exam itself, Frank Kane and Stephane Maarek’s AWS Certified Machine Learning Engineer Associate: Hands On! on Udemy was updated in October 2026 for MLA-C02 (4.5 stars from 6,129 ratings, 63,436 students). Our AWS machine learning certification guide covers the exam domains, prep options and which version to sit.

Alternatives to the AWS ML Engineer Nanodegree

• Frank Kane and Stephane Maarek (Udemy). The most direct route to the certificate: current for MLA-C02, cheap during Udemy sales and well reviewed. It teaches to the exam, so it will not give you graded projects.
• Coursera Exam Prep (Whizlabs). A five-course specialization with only 12 reviews (3.9 stars) so far; useful if you already pay for Coursera Plus.
• Machine Learning in Production (Andrew Ng, DeepLearning.AI). The old four-course MLOps Specialization link now leads to this single course (4.8 stars from 3,370 reviews). It teaches the ML project lifecycle and deployment concepts without tying you to a cloud.
• Zero To Mastery AI Engineering Bootcamp. 19.5 hours on SageMaker and Hugging Face, updated October 2026, with no ML experience required and a 30-day money-back guarantee. Lighter than nd189, and much cheaper.
• Other Udacity programs. Machine Learning DevOps Engineer for cloud-neutral MLOps, and Generative AI for LLM work.

Pros and Cons

Pros

• Built with AWS, centered on SageMaker. Training jobs, endpoints, pipelines, Debugger, Clarify, Model Monitor and Feature Store all appear in the skills list.
• Five graded projects plus a capstone. Each is reviewed by a person, and the operationalization project is strong interview material.
• AWS labs included. Udacity’s Cloud Labs budget means no personal AWS bill for the coursework.
• Well rated by learners. 4.7 stars from 58 reviews on Udacity.
• Named instructors and eligibility for credit toward Udacity’s MSc in AI.

Cons

• No generative AI. Last updated August 2025; nothing on Bedrock, RAG or agents.
• Not exam prep. You will still need an MLA-C02 course and practice tests for the certificate.
• Steep prerequisites. Seven listed, including deep learning and AWS familiarity.
• AWS only. The tooling does not transfer directly to Azure ML or Vertex AI.
• Expensive next to exam courses if the certificate is all you want.

Who Should Take This Nanodegree

Take it if you are a data scientist or Python developer moving into ML engineering at an AWS company, you learn best with graded projects and deadlines you set yourself, and you want portfolio work that shows deployment, scaling and monitoring rather than notebooks.

Skip it if you are new to Python or ML (start with fundamentals), your employer runs on Azure or Google Cloud, you want generative AI skills, or you only need to pass the AWS exam, where a dedicated MLA-C02 course covers more of the exam than this program does for a fraction of the price.

Is the Nanodegree Worth It in 2026?

Price it per project. With the 40% offer, finishing in four months on the bundle costs $507.60, or roughly $100 for each of the five graded projects, with human review and the AWS lab budget included. The one-time price ($599.40) works out at about $120 a project. Exam-prep courses cost far less but give you no reviewed deployment work, and free AWS material leaves you to set up and pay for your own account.

That makes the value question about what you need to show an employer. If your gap is hands-on, reviewed SageMaker deployment work you can talk through in interviews, the Nanodegree is reasonable value. If your gap is the AWS badge, or generative AI experience, put the money elsewhere first and come back to nd189 for the projects.

How to Get the Most Out of It

• Check the prerequisites honestly first. If neural networks or Python classes are shaky, spend two to three weeks on fundamentals before the clock starts on a paid month. Udacity’s own Intro to Machine Learning with PyTorch and TensorFlow programs cover the neural-network basics in 49 hours.
• Budget about 6 hours a week to finish in four months. Udacity paces the four courses at 4, 3, 3 and 4 weeks, then a month for the capstone; plan your subscription around that.
• Protect the lab budget. Shut down endpoints and notebook instances at the end of every session, and keep GPU training short while you debug on small samples.
• Write up each project. A short README with the architecture, the cost decisions and what you would change is what makes the work useful in interviews.
• Book the exam at the end, not the start. Finish the operationalization project, then spend two to four weeks on an MLA-C02 course and practice tests, including the Bedrock topics the program skips.

Start the AWS ML Engineer Nanodegree →

WHAT WE CORRECTED IN THIS UPDATE

Comparing programs? Our ranked guide to the best Udacity Nanodegrees in 2026 re-checks the hours, projects and prerequisites of 10 programs on their live Udacity pages, including this one.

Frequently Asked Questions

Is the Udacity AWS Machine Learning Engineer Nanodegree worth it?

It is worth it if you already know Python and basic machine learning and want guided, reviewed SageMaker projects with a Udacity budget for the AWS bills. It is weaker value if you mainly want the AWS certification, because cheaper exam courses cover the newer exam more directly, or if you want generative AI skills, which this August 2025 version does not teach.

How long does the AWS Machine Learning Engineer Nanodegree take?

Udacity lists 94 hours. Its outline paces the four main courses at about 14 weeks plus a month for the capstone, so 4 to 5 months at roughly 6 hours a week; faster if you already use SageMaker.

How much does the Udacity AWS ML Engineer Nanodegree cost?

On 11 October 2026 Udacity listed $249 a month, a 4-month bundle at $846 ($212 a month) or $999 to buy this program alone. With the 40% new-member offer (our code onlinecourseing40) those become $150 a month, $127 a month or $599.40.

Will I have to pay for AWS during the Nanodegree?

Udacity says it sets a fixed budget or credits for each student in its AWS Cloud Labs. The budget is limited, so shut down endpoints and notebooks when you finish; you should not need your own paid AWS account for the projects.

Does the Nanodegree prepare you for the AWS Machine Learning Engineer Associate exam?

Partly. It covers SageMaker training, deployment, pipelines and monitoring, which the exam tests, but it is not exam prep and does not cover Amazon Bedrock, which AWS lists in the profile for the new MLA-C02 version. Pair it with a dedicated exam course and practice tests.

Is the AWS Machine Learning Specialty exam still available?

No. AWS retired the Machine Learning Specialty; its last exam day was 31 March 2026. People who passed it stay certified for three years. The AWS Certified Machine Learning Engineer Associate is now AWS’s machine learning certification.

What are the prerequisites?

Udacity lists AWS familiarity, intermediate Python, API proficiency, Jupyter notebooks, deep learning and basic probability, and expects fluent written and spoken English.

Is there an Azure version?

Yes. Machine Learning Engineer with Microsoft Azure (nd00333) is a shorter 40-hour program built on Azure Machine Learning, with three projects. The AWS program is longer and covers more deep learning.

What jobs does this Nanodegree help with?

Machine learning engineer, MLOps engineer and applied ML roles at companies that run on AWS. It helps most when you can show the projects, especially the operationalization project and your own capstone, in interviews.

Final Verdict

Udacity’s AWS Machine Learning Engineer Nanodegree is a well-built, project-driven way to learn the SageMaker workflow end to end, and the included AWS labs remove the usual worry about cloud bills. We rate it 4.0 out of 5, down from our earlier 4.3: it has not been updated since August 2025, it skips generative AI entirely, and it is not the cheapest route to AWS’s certificate. Take it for the graded projects; pair it with an MLA-C02 course if you want the badge too.

Enroll in the AWS ML Engineer Nanodegree →

Related reading: All Udacity Nanodegrees reviewed · Udacity Generative AI review · Udacity Agentic AI review · AWS ML certification guide · Udacity hub · Udacity AWS Solutions Architect Associate review · Udacity AWS Cloud Architect review · Udacity Data Engineering with AWS review

Azure variant: Machine Learning Engineer with Microsoft Azure (nd00333)

Udacity’s Machine Learning Engineer with Microsoft Azure Nanodegree (nd00333) is the shorter alternative: intermediate level, 40 hours, 4.2 stars from 84 reviews, last updated 10 August 2026. Prerequisites are lighter: basic Python, HTTP and model training. It has two courses and three projects:

• Using Azure Machine Learning: workspaces and Azure ML studio, datastores and datasets, training models, the Azure ML SDK, automated ML and hyperparameter tuning, and model interpretability. Project: optimize an ML pipeline with both HyperDrive and AutoML and compare the results.
• Machine Learning Operations: deploying models, consuming and load-testing endpoints, Application Insights and logs, and publishing a batch inference pipeline. Project: operationalize a model on the Bank Marketing dataset.
• Capstone: solve a problem of your choice in Azure Machine Learning.

Instructors are Erick Galinkin, Noah Gift, Soham Chatterjee and Alfredo Deza. Pricing matches the AWS program ($249 a month, $846 for four months or $999 one-time, before the 40% offer). Microsoft has retired the Azure Data Scientist Associate certification (DP-100), so do not expect this program to line up with a current Microsoft exam. Pick it if your employer runs on Azure ML and you want a faster program; pick the AWS version for depth, deep learning and more projects. For Azure AI work, see our AI Engineer using Microsoft Azure Nanodegree review.

See the Azure ML Engineer Nanodegree →

Choosing your cloud: AWS vs Azure for this career

Choose the cloud your target employers use; job posts usually say. Our AWS vs Azure comparison covers market share, certifications and a decision framework. If data engineering is the goal rather than ML, see the AWS data engineer certification guide or the Azure data engineer certification guide. Other Nanodegrees are compared in our Udacity Nanodegree review.