What does an AI product manager need to know to succeed

There is no dought that AI will lead the industry in the years to come, with massive data continuing to grow and cloud computing that allows easy and affordable technologies to analyze and process the data, using AI becomes to be a must for business improvements, military systems enhancement, and healthcare. In fact, I don't see any domain that AI will not touch is.

After many years as a product leader, I took a new role in a small startup that uses AI or to be more clearly responsible AI (that's a topic for a different post). As a product manager, I believe that to be successful in your domain you have to be an expert in it. For example, as a product manager, I have the same knowledge and skills an average DevOps engineer has in Kubernetes, why? first I love technology, second, it's always better to talk the “development language” and not be at a high level, personally, I don't think it will work if you don't know the details. I hold several cloud certifications that help with providing requirements for cloud-based products. I'm also a network expert who ties everything together which leads me to security, in my view if you don't understand networking how can you be a cyber expert? That is my view on product management, some will say the product should be on the business side only and architects should engage the developers, and each firm decides what's best for her.

Going back to AI, in my plan to understand this domain I wrote the skills a product needs and the common algorithms a product should know. This is the learning plan that I'm sharing in this post.

Product manager skills

AI product managers are responsible for overseeing the development and launch of artificial intelligence products. They work closely with a team of engineers and data scientists to identify market opportunities and develop a product roadmap that meets customer needs. They also work to ensure that the product is technically feasible and financially viable.

Some key skills for AI product managers include:

1. Strong technical background: It is helpful for AI product managers to have a strong understanding of artificial intelligence technologies, including machine learning, natural language processing, and computer vision.
2. Business acumen: AI product managers should have a strong understanding of business strategy and the ability to identify market opportunities.
3. Data analysis skills: AI product managers should be able to analyze data to inform product decisions and to track the performance of the product after launch.
4. Project management skills: AI product managers should be able to effectively manage cross-functional teams and coordinate the development of the product.
5. Communication skills: AI product managers should be able to clearly communicate their vision for the product to both technical and non-technical stakeholders.
6. Leadership skills: AI product managers should be able to lead and motivate their teams to achieve product goals.

Algorithms to learn

There are many different algorithms used in artificial intelligence, and the specific algorithms that you should learn will depend on your goals and the type of AI work you are interested in pursuing. Here are some of the most common and important algorithms used in AI:

1. Machine learning algorithms: These algorithms allow computers to learn and improve their performance without being explicitly programmed. Some popular machine learning algorithms include linear regression, logistic regression, decision trees, and support vector machines.
2. Deep learning algorithms: These algorithms are a type of machine learning that is inspired by the structure and function of the brain. They are often used for tasks such as image and speech recognition. Some popular deep learning algorithms include convolutional neural networks and recurrent neural networks.
3. Natural language processing algorithms: These algorithms are used to process and understand human language. They are often used for tasks such as language translation and text classification.
4. Computer vision algorithms: These algorithms are used to analyze and understand images and video. They are often used for tasks such as object recognition and image segmentation.
5. Planning and decision-making algorithms: These algorithms are used to make decisions and take actions based on data. They are often used in autonomous systems such as self-driving cars.
6. Optimization algorithms: These algorithms are used to find the optimal solution to a problem. They are often used in machine learning to find the best set of parameters for a model.

It’s important to note that these are just a few examples, and there are many other types of algorithms used in AI. It’s a good idea to start by learning some of the fundamental algorithms and then branching out from there based on your interests and goals.

wish me luck and connect