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How To Become an AI Product Manager? Start Your Journey with these Actionable Steps

Last updated:
3rd May, 2021
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How To Become an AI Product Manager? Start Your Journey with these Actionable Steps

As AI professionals’ demand has increased substantially, now is the perfect time to pursue a career in this industry. In this digital age, the best way to gain a competitive advantage is to employ AI and ML technologies to innovate your business model radically. AI product managers are one such professional who pioneers business innovations through their extensive knowledge of artificial intelligence and related technologies. 

Essentially, AI Product Management aims to leverage data science technologies like artificial intelligence, machine learning, and deep learning to innovate and improve products while transforming the world around us.

This article will help you understand how you can become an AI product manager and kickstart your career.

What is an AI Product Manager? 

An AI product manager is responsible for the entire lifecycle of an AI-based product. What does it mean?

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It means as an AI product manager, you’ll be responsible for all the stages of product development and launch, including conception to launch. 

AI product managers transform business strategies into well-defined product plans. They must also conduct market research and ensure that they launch viable trending products accordingly. Senior AI product managers act as the bridge between the development teams and the stakeholders of the organisation. AI product managers work with multiple teams to ensure that all the stages of product development take place smoothly. 

Steps to Become an AI Product Manager

Every career requires certain preparation. Knowing how to pursue a career helps you plan out your goals accordingly and get your desired job quickly. The same is the case for a career as an AI product manager. The following steps will help you understand the pathway to becoming an AI product manager. 

1. Learn about AI and relevant concepts

To become an AI product manager, you’ll need to learn about AI and its various concepts. Without a keen understanding of AI, you can’t pursue a career in this field. An AI product manager’s role requires you to be an expert in artificial intelligence and its implementations. You should know how to use AI to solve complex problems and design a product accordingly.

Some of the key concepts of artificial intelligence you should know about are:

  • Algorithms and Modelling
  • Machine Learning 
  • Deep Learning
  • Natural Language Processing

At upGrad, we offer a Master of Science in Machine Learning & AI program with Liverpool John Moores University and International Institute of Information Technology Bangalore. This program teaches you all the necessary skills and concepts to become an AI professional. 

Some of the concepts you will learn in our AI course are: 

Data Science Fundamentals

A lot of concepts you will learn in AI are based on data science. Hence, you’ll have to start with learning the basics of data science and understand its applications. 

Our course will first introduce you to Python and teach you how to use Python in Data Science. Afterwards, you will learn about data visualisation, data analysis, and the use of SQL in data science. 

We will cover exploratory data analysis, which is among the most important data science concepts in AI. You will study inferential statistics and give assignments on all the topics you have learned so far. 

Machine Learning (Basics and Advanced) 

After we have covered the fundamentals of data science, our course will teach you about machine learning. Machine learning refers to processes where a system can perform and learn from specific actions without human intervention. 

You will learn about the different machine learning concepts and applications, including linear regression, logistic regression, and Naive Bayes. Once you have completed the basics, we will cover the advanced concepts of machine learning, such as:

  • Advanced regression
  • Support vector machine
  • Tree models
  • Unsupervised learning

By the end of this module, you’d be familiar with multiple machine learning algorithms and would be able to use them in real-life applications. 

Natural Language Processing

Natural language processing is a subfield of AI, computer science, and linguistics where a machine focuses on interpreting and understanding human language through text or audio. 

The auto-correct feature of your smartphone is a prominent example of how a machine can understand human language. It’s also a fine example of natural language processing (NLP, in short). 

Our course will teach you about the different NLP implementations such as lexical processing, syntactic processing, and semantic processing. NLP has various applications such as text-to-speech software, sentiment analysis, etc.

Deep Learning

Deep learning is a branch of machine learning where your machine focuses on imitating the human brain. You will create and use neural networks, understand how they work, and how you can use them in real-life applications. We will teach you about the different kinds of neural networks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). 

Reinforcement Learning

Reinforcement learning is a section of machine learning focused on taking suitable action to get the maximum rewards in a specific situation. It makes your algorithms more efficient and effective, allowing you to get better results.

It is one of the three machine learning paradigms. The other two are supervised learning and unsupervised learning, which we have covered in the course’s previous modules. This module will make you familiar with reinforcement learning and deep reinforcement learning so you can use them with other AI and ML implementations. 

Applications of AI and Machine Learning

Throughout our AI and Machine Learning course, you will be working on projects and assignments. They will test your knowledge and help you apply what you’ve learnt during the course. 

Understanding the applications of different AI and machine learning concepts is a must-have if you want to become an AI product manager. That’s because as an AI product manager, you’ll be responsible for using all the different concepts to solve problems and enhance your organisation’s product.

Our Master of Science in Machine Learning & AI program offers 12 case studies, 11 coding assignments and ten capstone projects to choose from. 

Some of the projects you’ll work on during our AI and ML course:

  • Detect skin cancer from images
  • Build a chatbot
  • Train an agent to play Tic Tac Toe
  • Gesture recognition
  • Build a recommendation system. 

Our course will make you familiar with all the industry staple technologies, including Python, TensorFlow, Keras, MySQL, etc. 

2. Choose your Specialisation Area.

Once you’ve mastered the fundamentals of AI and ML, it’s time to choose your industry and specialisation domain. Reflect on your career aspirations – which industry do you wish to enter? Finance, or eCommerce, or IT? After identifying your interests, shortlist the companies you want to work for. Do your homework and research on the chosen domain to understand better what responsibilities you will have to undertake, what skills employers demand of you, and so on. 

AI product managers must combine their tech knowledge with business acumen to craft foolproof strategies. Thus, they must always stay updated with general industry trends. The bottom line is that you must know the industry inside-out to best market your skills and expertise to potential employers. 

3. Prepare for Interviews

Even if you have the necessary skills and qualifications, if you aren’t prepared for a technical interview, it’ll be quite challenging for you to become an AI product manager.

Recruiters ask various technical interviews questions focused on understanding the candidate’s knowledge of artificial intelligence, machine learning, and relevant topics. They also want to know about the candidate’s analytical, critical thinking, and problem-solving skills throughout the interview.

That’s why you should put in the extra effort while preparing for an AI product manager interview. With every upGrad course, you get access to our Student Success Corner. Our dedicated benefits include: 

Personalised resume feedback

We will help you craft the perfect resume to become an AI product manager through personalised resume feedback. You will receive 1:1 resume review sessions with industry experts and access to profile building workshops. These live sessions will help you build a compelling and attractive CV suitable for an AI product manager’s role. 

Company-specific preparation

At upGrad, we offer just-in-time interviews where we give you company and role-specific preparation right before the actual interview. We offer mock interviews so you can get rid of any anxiety or nervousness before the actual interview. 

We have tons of carefully curated interview resources that you can use to enhance your preparation and bag the role. 

Networking opportunities

During the course, you will get access to a live discussion forum for peer to peer doubt resolution. We offer peer to peer networking opportunities with an alumni pool of 10,000+. You can network with fellow course students during the program too. 

Dedicated mentorship

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You will get a dedicated career mentor and an industry mentor to help you eliminate confusion and doubts. Having a mentor ensures that you don’t make novice mistakes, and if you do, you learn quickly from the same. 

Start your AI Journey Today!

After learning the necessary skills, getting certified, and preparing for the interview, you can easily become an AI product manager. 

With all the learnt skills you can get active on other competitive platforms as well to test your skills and get even more hands-on. If you are interested to learn more about the course, check out the page of Executive PG Programme in Machine Learning & AI and talk to our career counsellor for more information.


Pavan Vadapalli

Blog Author
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.
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Frequently Asked Questions (FAQs)

1How do I test a prototype?

Testing a prototype is essential to ensure that the prototype functions as intended and that any problems with the prototype are identified and corrected. One way to test a prototype is to ask people who are not familiar with the prototype to try to use it. This can help to identify any usability issues with the prototype. Other ways to test a prototype include using software testing tools or doing a code review. You can also try a prototype by simulating different use cases. Similarly, you can also perform load testing to see how the prototype handles various levels of traffic.

2How to model a product?

The first step in modeling a product is to understand what the product is and what it does. Once you have a good understanding of the product, you can begin to create a 3D model of it. It is essential to have accurate measurements of the product and to create a model that is as realistic as possible. You can use software such as AutoCAD or Solidworks to create the 3D model. The next step is to create a rendering of the product, which will give you a realistic view of how the product will look when it is finished. The final step is to create a prototype of the product. This will help you test the product and make sure that it is functioning correctly.

3When do I know that a product is optimal?

The product is optimal when the customer's needs are met. You can do that by doing a needs analysis and then finding the best product to meet those needs. You have to ensure that the product is affordable, accessible, and valuable to the customer. Similarly, the customer must be able to understand and use the product easily. You can also make sure that the product is reliable and meets all safety requirements. Finally, you can make the product aesthetically pleasing to the customer.

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