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Udacity AI Product Manager Nanodegree Review 2023: Learn AI Product Management

AI Product Manager Home Screen

In this Udacity AI Product Manager Nanodegree Review, I will be sharing my experience, insights into the program and telling you how this Nanodegee can help you become an efficient AI Product Manager.

Udacity is offering personalized discount.

I am a Ukrainian living in Poland. I know six languages: Ukrainian, Polish, English, Russian, German, and Norwegian. My educational background is various: first, I obtained a Master’s degree in Translation and Philology. 

Later, I completed post-graduate studies in Accountancy and Corporate Finance. I also successfully graduated from Udacity’s “AI Product Manager” Nanodegree program, mostly as the Bertelsmann Scholarship (I and II phases). 

I also lived in different countries (Germany, Norway, Denmark) where I met people from all over the world and gathered cultural experiences. Now, I am a student of the Warsaw School of Economics going to obtain a Master of Finance and Accounting, together with Project Management. 

I also do translations from Polish to Ukrainian. My hobbies are kickboxing and programming. I continuously improve my professional competency by gaining new language, economics, and technological skills.

In this Udacity AI Product Manager Nanodegree Review, I will be sharing my experience of completing the AI Product Manager Nanodegree with the insights, syllabus, projects, pricing, pros, and cons.

If you buy the course through the links in this article, we can earn some affiliate commission. This helps us to keep this blog up and running for your benefit.

My Motivation to enroll in Udacity

For the first time, I got to know about Udacity at my university. I won the Google scholarship (the Android development Nanodegree) where I accomplished two projects. 

Unfortunately, I didn’t graduate from the Nanodegree program. Udacity kept in touch with me in the next years. They send me a scholarship and Nanodegree offers, and personalized discounts. So, I learned to trust them.

Besides, I had to take a break, from my university studies last year. So, I decided to take another, chance with Udacity winning the Bertelsmann scholarship for the “AI Product Manager”

My immediate motivation was that they didn’t require any special prerequisites (like advanced programming skills or math) to get enrolled. Another reason was that the Nanodegree combined management and technology which was great for me. 

The certificate is also fine to have on my CV now or show to my friends and family. I also like that Udacity sends all the necessary information eg. various links very often to keep me updated. 

For me, it is important to feel connected with the organization as that motivates and drives me ahead. Udacity also offers good possibilities to build a network on Slack and in the peer chat. 

Oksana AI Product Manager Nanodegree Certificate

Obtaining new friends and business contacts is very important for me.

My thoughts on AI Product Manager Nanodegree Syllabus

Let’s start with the syllabus in this Udacity AI Product Manager Nanodegree Review.

The syllabus covers great plenty of elaborated topics in the fields of product management, artificial intelligence, machine learning, and deep learning. 

I understand that more advanced students could expect more lectures about more advanced items of ML-like Autoencoders or Monte Carlo methods. But for me, this syllabus was more than enough, and even overwhelming sometimes when it goes about metrics, linear interpolation, or activity functions. 

I went through the course twice to review and hone my knowledge.

Structure: The lecture videos are put in small chunks. 

There are additional explanations, cheat sheet links, and learning sources (for those who want to extend their knowledge) under almost every video. Every lesson offers many real-world examples and various quizzes to consolidate the acquired knowledge. 

Every lesson has a summary with key points. The “AI Product Manager” Nanodegree has three projects. A separate section is devoted to each of them. 

There you can find needed templates and lots of additional resources to give a helping hand. One can find the instructions on how to do the projects in the video lectures and in the form of text and screenshots in the special sections.

One is asked to fulfill the first project only at the end of the second lesson. Yes, it is a kind of technical assignment, created on the Appen platform, but everyone can manage using the thorough instructions given

Lecture time: introductions or summaries – around half a minute; 1,5-2,5 minutes – lecture videos; around 4 min – videos showing an example for the first project on the Appen platform.

Lesson 1: Introduction to AI in Business

My general impression of the first lesson is like this. Alyssa S. R. took me by the hand and easily led like “Alice” into the crazy land, hitherto unknown to me, of AI models and neural networks that learn themselves and do wonders afterward. 

The lectures were easy to understand and contained much knowledge at the same time. The lecturer moved from one topic to another very smoothly. She has a good command of English as Alyssa is a Native speaker

The lecturer is an AI Product Manager, so she generously shares her business experience with students, what problems she has encountered and how they have been solved.

Maybe, somebody more advanced than me would expect more complicated material. For me, as a beginner, it was a perfect start in AI in business. I created the “Changing the future” study group later in the Slack community. 

Based on the material of the 1st lesson I was able to find and present new curious cases of ML and AL application in the business to educate my group’s members (80 people).

Alyssa described various business cases where AI was efficient, provided the description of AI’s current capabilities such as image or speech recognition (then you understand all the tales of AI invading the Earth are only tales so long). 

I could understand the difference between AI, ML, and DL as it’s not completely the same. I got a deeper insight into the deep learning concept and why it is good for business. 

I learned about the benefits of AI for business. All those topics were represented in short videos, and the syllabus’ structure was agile. Much time was devoted to a business case that teaches how to formulate a specific and narrow business problem to start the AI model process with. 

There are quizzes related to the case. There are multiple real-world examples, too.

To compartmentalize the first lesson, I learned about AI/ML/DL definitions, their current state, different AI/ML/DL application cases in business industries such as agriculture, food industry, social media, stores, customer services, etc. 

The other topics were Machine learning and its division into supervised learning, unsupervised learning, reinforcement learning, neural networks, human-in-the-loop. Then, the AI approach was described incl. data preparation, model training, testing and deployment, and model performance metrics, too. 

Alyssa also paid much attention to project and product management and building cross-functional teams.

Also Read: Upgrad Product Management Review 2021: Should You Enroll It?

Lesson 2: Creating a Dataset

Thus, from the “big picture of AI in Business,” we transition to the “heavy weapon”. As for me, the 2nd lesson is a kind of manual on how to create a dataset(s) for an AI model, not too small, not too big, relevant, and complete according to the “garbage in, garbage out” motto. 

Colorful charts and tables in the course videos and descriptions make it easier to absorb the knowledge. The next lecturer, Karsten Gaki, is a product manager at Figure Eight and very professional. 

She “feeds” us with the material gradually, step by step, explaining difficult points thoroughly. Karsten helped me to understand the importance of feed my model with qualitative data to obtain the best practical results.

It is not all. The 2nd lesson is also interesting with regard to the first project. There is an introduction to the first project already in the middle of this lesson. 

There is an explanation of what is data annotation followed by detailed instructions on how to create an account on the Appen platform where I had to accomplish my project. I was taught how to create and do my annotation assignment on two example case studies. 

Here, I dealt with coding for the first time. Though, there was nothing to be afraid of as I used a template of Appen with some code and only adjusted it to my case. To knock the lesson’s syllabus down, I learned about data size and annotations, data completeness, and relevance. 

In this lesson, I also used precision, recall, F1 score, and confusion matrix metrics to evaluate an AI model’s performance for the first time. To summarize, the lesson is about dataset building, image labeling, and dataset updating.

Project 1: Create a Medical Image Annotation Data Set with Appen

The initial project was the first hands-on workshop in the field of machine learning and artificial intelligence for me. It was a bomb as for the first time I could watch AI in action and even steer it! 

The project was aimed to help doctors to confirm pneumonia cases and discard healthy cases. I designed an annotation job on the Appen platform. All the necessary instructions (videos and texts) and project files were provided by Udacity. 

I got a medical dataset through the link Udacity directed me to. The model had to distinguish between healthy cases and pneumonia cases. 

It was not perfect but classified the images into the classes defined by me. First, I downloaded the dataset on the platform, then, adjusted a piece of CML code. It was very interesting, but not difficult at all, even for me who had never dealt with AI before. 

Appen has its own knowledge base where I could find the needed CML commands. The most difficult part of the assignment was to create proper instructions and test questions for my future annotators. 

They couldn’t be vague, but precise and specific so that annotators would do a good job labeling the images. 

The project was about human life. Having received improper aid from an AI system, a doctor could make a mistake and start treating a healthy man, or on the contrary, ignore a serious pneumonia case that could cause death.

From the first project, I learned to look for advice on the peer chat and the Knowledge base of Udacity. You can enter them being in your classroom space. The chat helped me very much as I found the necessary information in discussions there. 

I also asked mentors in the Knowledge and they answered me in no time. We also had channels on Slack and they were fantastic as Udacity’s workers, mentors and peers helped and motivated students very much there. 

Believe me, it is much easier to accomplish the most difficult assignment when the community cheers for you! As I have said the model was not perfect as it was confused by a few obscure pictures in each class. 

There were signs of strange articles on several images. In my proposal, I had to address the issues and suggest how to improve the model’s performance. I really appreciated that Udacity asked me not to launch my image labeling job on the Appen to avoid costs. 

My goal with the first project was to create it without launching it and submit it to the reviewer. To sum up, the project was very exciting for me and I learned about image labeling. I successfully passed the 1st project on the 1st submission.

X-ray Picture of Human Lungs

Source: Pneumonia incidence example on the Appen platform, “Create a medical image annotation job” project by Oksana Tsvar, AI Product Manager Nanodegree, Udacity

Contributor Satisfaction

Source: “Create a medical image annotation job” project by Oksana Tsvar, AI Product Manager Nanodegree, Udacity

Lesson 3: Building a Model

The 3rd lesson focuses on neural networks as the most widespread models. Neural networks are called so after brain neurons. 

The lecturer, Kiran Vajapey, elaborates on how neural networks work. I must confess that activation functions were difficult for me at first, but the videos and additional articles helped me get my head around the topics, the same was with backpropagation and weighting. 

All these notions are necessary to build ML algorithms. Yes, this lesson requires some simple math, but no worry. There are examples of calculations eg. perceptron math on the example of assessing a chance to enter a University. 

I found it great that Udacity richly applied data visualization for better teaching technical issues. Those colorful charts and pictures helped me to better understand the most difficult points.

The lecturer is also very experienced. He is a Senior Developer in Figure Eight. Being an engineer Kiran feels very confident about what he tells us in the most technical part of the course. 

First, the lecturer presents the modeling process in general and then breaks it into elements such as model training, model testing, and model evaluation. Before I did my second project I had gone through a case study of a pet model. 

Thus, I learned how to train an AI model, and to evaluate its performance calculating precision, recall, F1 score for separate pet image classes and the whole model. There are also lots of quizzes across all the 3rd lessons to check my progress. 

What I appreciate, new terms and notions are repeated and even explained several times in different parts of the lesson. Thus, I felt that terms and notions were locked in my head forever. Practice makes perfect.

The 3rd lesson is not only about the general modeling process, but also about such alternatives as transfer learning and automated modeling platforms, too. 

It is important for me to have such options: to build a custom AI model or use an automated external platform as Google AutoML. The first variant is better for students with some ML development background. 

Instead, the automated variant is convenient for those who do not possess many ML skills yet to create complicated algorithms. To cite Kiran: “automated ML makes AI developing more accessible without a Ph.D. degree in Data Science”.

To summarize, the 3rd lesson is about training and evaluating an AI model, transfer learning, and automated platforms; neural networks, activation functions, backpropagation, weight updating, pros/cons of custom modeling, and automated ML.

Project 2: Build a Model with Google AutoML

The second project was the development of the first one. I built 4 models on the Google AutoML Vision platform playing with the pneumonia dataset.

First, I created a simple binary classifier to detect pneumonia incidences. I also created a more complicated classifier with pneumonia class divided into 2 types of pneumonia: viral and bacterial (in reality, there are 3 types incl. fungal pneumonia). 

Two other experiments were feeding the model with dirty and unbalanced data. At the end of each experiment, the system generated statistics of success metrics so I could compare precision, recall, and F1 score of all 4 models for my project report.

I concluded that the main goal of the 2nd project was to show how an AI model’s performance depends on the quality of a dataset and the number of classes. It also taught me how to optimize my model’s performance. 

However, one always has to choose an optimization parameter eg. accuracy or precision. To my mind, another goal of the project was to show that a product manager can build AI models and evaluate their performance not being a coder or a Software Engineer

They can just build models on one of the external platforms like Google AutoML Vision. Of course, there are flaws but this way is very convenient and accessible to non-technical managers who want to lead ML/AI production.

I appreciated that Udacity pointed out to me how to build an AI model applying the Google AtutoML platform free of charge. I could choose a free 300$ card. 

Udacity also reminded me to disable the billing after I had finished my project. I think it is a fair practice – they think of their student’s good, not only of their partners’ profits.

I didn’t have any difficulties during the 2nd project. I had only one issue when downloading the x-ray image dataset for the first model as the system showed 2 pictures less as was needed. 

I solved the problem by downloading 2 pictures more, then the system showed the needed number of data points. Each model training session ran very fast. The first training lasted the longest, the others took only several hours. 

I simply had to observe the experiments, compare them and fill out the Auto-Modeling-Report (the project form provided by Udacity). As a result, I successfully passed the 2nd project on the 1st submission.

Auto Modeling Report

Source: Binary Classifier with clean and balanced data on the Google AutoML Vision, “Build a model” project by Oksana Tsvar, AI Product Manager Nanodegree, Udacity.

Matrix and Graph

Source: Binary Classifier with clean and balanced data on the Google AutoML Vision, “Build a model” project by Oksana Tsvar, AI Product Manager Nanodegree, Udacity

Lesson 4: Measuring Impact and Updating Models

The 4th lesson asks and answers all the crucial questions I would have to address before letting a “newborn” AI model into the world. I have to be able to optimize my model, would it be a higher precision or a higher recall, as we cannot have sun and snow in one performance. 

Moreover, this “AI baby” must be capable of continuous learning to become better. I learned how to assess the business impact a model can make on the world. For this purpose, I will use success metrics. 

I also discovered A_B tests/versioning to choose the best version of my product. The lecturer Meeta drew my attention to the problems an AI model can have such as various biases, ethical problems, or compliance issues. 

Finally, she shared some thoughts on how to scale and grow my product. All the obtained knowledge served as a proper foundation to accomplish my capstone project. In my opinion, the final lesson is of high quality and variety. I have no objections to its syllabus either. 

Maybe, somebody would notice the lecturer’s accent as she is not a native speaker, it is not a problem for me at all. Meeta Dash is a great expert and teacher. She is a product director in Figure Eight. 

In the same way, she eagerly shares her experience and thoughts about AI product management with students. What I am very grateful to Udacity for is another big case study devoted to video annotation before the capstone project. 

The last lesson also includes many valuable examples eg, continuous learning is demonstrated in the example of a spam filter. I also learned how Netflix, GE, or Bluer River had challenges and managed to improve their services by applying an AI solution.

Project 3: Capstone Project

The capstone project was the most interesting to me as it demanded not only knowledge/thinking but also creativity. I had to invent a concept of a product powered by ML/AI. I had free rein to choose a business industry the product could be applied for. 

My product is “AI Kickboxing Tracker/Corrector” for teaching basic techniques to beginners and supporting a kickboxing coach so that he/she could focus on preparing advanced athletes for championships. 

I have chosen the sports industry because I am a kickboxer and I practiced other martial arts too. In essence, I had to submit a business proposal (the template was provided by Udacity) together with sketches of my product. 

The questions to discuss covered all from AI and ML, product management, and even some marketing. 

The goal of the project was to teach me the whole process of ML/AI product management: specifying a narrow industry problem, building a specific business case, planning my model’s outcomes and outputs, measuring my product performance in terms of business and ML/AI metrics, planning datasets, and their quality, selecting proper labels, preparing strategies how to resource/outsource building the model, monitoring and mitigating bias, etc.

I drew sketches of the MVP of my product. I am not a painter, but the prototype drawings are technical and can be done with a pen. There was also a little marketing to think about: I invented user personas and a market-to-go plan on how to launch and market my “AI Kickboxing Tracker/Corrector”. 

Finally, I considered measures for my product’s longevity like the model’s continuous learning or A/B testing/versioning. Nevertheless, I would like to emphasize two weak points when it concerns the capstone project. 

I found no mention of how many pages the business proposal should have in the project instructions in my classroom. I had written too many pages what was the reason of two failed submissions, and I had to summarize my project. Later the reviewer told me that the proposal has to contain 5-10 pages.

I also noticed some discrepancies between the capstone project starter file and the project rubric file. The questions in those files slightly differ. I made a table with questions from both files to compare them. So, I could cover all the required items in my business proposal.

Though the most interesting, the project was the most difficult for me. I passed it successfully until after the 4th submission. To summarize, this project was a great adventure for me! I had to invent, design, and grow my knowledge and expertise.

So this was about the syllabus and projects in this Udacity AI Product Manager Nanodegree Review, further let’s know about pricing, durations, etc.

Business Case

Source: The capstone project by Oksana Tsvar, AI Product Manager Nanodegree, Udacity

Model Building

Source: The capstone project by Oksana Tsvar, AI Product Manager Nanodegree, Udacity

Minimum Viable Product

Source: The capstone project by Oksana Tsvar, AI Product Manager Nanodegree, Udacity

Check this out -> Udacity Data Product Manager Nanodegree Review 2022: My Experience


Udacity AI Product Manager Nanodegree Project Experience

Let’s know more about the Project and my experience with it in this Udacity AI Product Manager Nanodegree Review.

As the “AI Product Manager” Nanodegree demands no technical or math prerequisites apart from computer skills. It means everybody can have a try. I can state now that these three projects are challenging but “feasible”. 

Everybody can do them after they have finished the video course and work over the assignments systematically every day. For less technically educated people like me, the projects are demanding, but also give much. 

For more advanced “technophiles” two first projects can be easier, while the more creative capstone project will be more challenging. Besides, I had the support of peers and mentors all the time, and the advice of my reviewers on how to improve my projects.

To handle the assignments I used the videos, text instructions, and example cases provided by Udacity, and peer discussions on the chat in my classroom, and the Slack community. There were also bonus resources available for the initial x-ray image labeling project. 

They were very helpful.

To my mind, the projects were valuable. I learn best in practice. The assignments were constructed so that I had to broaden my knowledge and skills all the time. 

The first project taught me to create an image labeling job. Then, I trained my models and evaluated their performance as the second project. 

I also made the most out of my capstone project as I had to design an ML/AI-powered product, evaluate its performance and make a plan on how to launch it, lead to market and provide post-launch care. I went through the whole process of AI product management.

What about the reviews, they were very detailed and included additional sources to learn more about the discussed items eg. mitigating biases, or how to create a user persona. 

The reviewer always explained why he approved an item or gave advice when something was missing. I received suggestions on how to do better when it was to the point, or motivation and praise when the projects passed at last.

I am generally satisfied with my reviews apart from that my reviewer returned my capstone project twice as “ungradable”. He didn’t want to review it because it was too long. At first, I was disappointed but later we agreed the business proposal had to be summarized to meet the size specifications. My project was all right otherwise. 

As a result, I successfully passed the capstone project on the 4th submission.

Must see -> My Thoughts: Udacity Product Manager Nanodegree Review 2022

My Views on Pricing

Coming to the Pricing of this Nanodegree in this Udacity AI Product Manager Nanodegree Review.

The Nanodegree was free of charge for me as I did it for the Bertelsmann Scholarship. 

Otherwise, the price of $399/month is very high for me. I think it is a justifiable fee as the Nanodegree involves managers and directors as lecturers. 

They also pay to their reviewers. Udacity provides high-quality content and projects based on technical tools. Admitting this, I would still have to save before I would pay the price for two months the course lasts. 

However, if I would have to pay for an extension to finish my project I would do it. Yes, Udacity sent me discount offers (even 70%) during the program to take another Nanodegrees.

My Views on Course Timeline

So, talking about the course outline in this Udacity AI Product Manager Nanodegree Review, they give you two months to watch the video course, solve the quizzes, and submit two “midterm” projects and the final capstone project in the frame of the “AI product Manager” Nanodegree. 

I had more time for the projects as I participated in this Nanodegree as a Bertelsmann Scholarship recipient. I did my video course in two weeks. 

I think it is possible to accomplish the projects in two months if you work intensively and systematically towards your goal (it surely takes more than 30 minutes daily). Besides, I knew people who finished this Nanodegree in two months.

The most difficult challenges on my way to graduation were the time shortage and accumulation of daily matters now and again. I also procrastinated sometimes or felt bad because of bad weather. However, I was determined to graduate and mobilized myself to work.

Views on Interesting Udacity features –

Now, let’s have a look at some of the Udacity features in this Udacity AI Product Manager Nanodegree Review.

1. Mentorship:

Everybody can get the advice of mentors through the classroom. I asked them sometimes through the Knowledge base, too. They always and quickly provided the needed information. You can also get the help of mentors on Slack.

2. Project reviews:

My project reviewers did a good job. I received their feedback very quickly, in several hours my reviews were ready. They kept to the project specifications and were very demanding. I had to resent my capstone project four times – it says much about quality. 

At the same time, the reviewers were very friendly and nice. They motivated and praised me for my accomplishments. I felt they wanted me to succeed. The process was transparent for me. All the projects were reviewed against the specific rubrics that I had gotten acquainted with.

I was angry a bit with my capstone project reviewer at first, but I was wrong. My project was too long to meet the specifications. I had to summarize my business proposal. My reviewer led me through the process very professionally. Therefore, my capstone project finished with success.

3. Career services:

I didn’t use Udacity’s career services during the program though I was offered as a part of the Nanodegree. It had a form of another project(s) to be reviewed. I didn’t use the offer willing to focus on my technological projects. 

However, the career service would help me to improve my CV, LinkedIn profile, and GitHub. I have graduated from the Nanodegree program and still have the career service offer in my classroom available.

Also, Read -> Udacity Digital Freelancer Nanodegree Review 2022: Build a Successful Business Online

Pros and Cons of the Udacity AI Product Manager Nanodegree

So let’s move on to the Pros and Cons of this Nanodegree in this Udacity AI Product Manager Nanodegree Review.

Pros – 

Most of all, I like the variety of courses and Nanodegrees Udacity offers. I also appreciate that they offer scholarships to make their Nanodegrees available to more people. 

Their Slack communities and professional/technical support are fantastic. The classroom space has a convenient interface in soft colors. One has buttons of the technical support and the peer chat inside the classroom.

The customer service workers are very amiable and experienced. I always received quick and relevant support. 

Moreover, they keep in touch with students after graduation too, sending some information about scholarships, programs, and discounts. Furthermore, it is great that Udacity invites industry experts to lecture. 

The organization always has not theoretical, but hand-on projects based on innovative technologies. The content of  Udacity’s courses and projects is updated in the course of time.  Finally, I simply like the name “Udacity”!

Cons – 

I have a few comments more concerning the Bertelsmann Scholarship than the “AI Product manager” Nanodegree. Udacity had a very small pool of scholarship places for those who wanted to go over to the 2nd  phase. 

I had to work really hard to win the scholarship continuation. It took me very much time to become one of the 1600 winners selected out of 15000 participants. 

I had to finish my video course first and work on Slack at the same time (posting, helping, managing a study group, making presentations, etc.). I think Udacity should make the scholarship pool larger to give more people the chance to obtain their certificates.

I also think their prices are too high for non-Americans such as people from Africa, South America, Asia, or Eastern Europe. However, Udacity partly compensates it by offering discounts.

Conclusion: Do I recommend it to others?

In general, I recommend the courses of Udacity. Their certificates will make your CV extra special. 

Udacity offers lectures and projects conducted by business experts. One gets a professional customer and technical support.

A background in AI may be helpful if you are a project manager now or are aspiring to be one. Not only will there be a sizable and need for you, but you’ll also have a little more job stability to go along with it.

Sincerely speaking, I would recommend the “AI Product Manager” Nanodegree to those who want to initiate/manage/supervise/monitor/lead the process of ML/AI product building and marketing. 

I am sure this course is perfect for any product owner or project manager to stand out in the market. 

This program will also be very useful to entrepreneurs who want to open startups creating, growing, and scaling AI/ML-powered products. 

While for software developers and engineers the Nanodegree will be a precious additional qualification for their CV.  Hope you like this Udacity AI Product Manager Nanodegree Review and found it useful and informative.

This program will be interesting for specialists in any industry field. AI and ML can be used to improve things, processes, and systems everywhere.

Udacity is offering personalized discount.

Oksana Tsvar

Oksana Tsvar

I am a Ukrainian living in Poland. I know six languages: Ukrainian, Polish, English, Russian, German, and Norwegian. My educational background is various: first, I obtained a Master’s degree in Translation and Philology. Later, I completed post-graduate studies in Accountancy and Corporate Finance. I also successfully graduated from Udacity’s “AI Product Manager” Nanodegree program, mostly as the Bertelsmann Scholarship (I and II phases). 

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