Machine learning is quickly becoming a powerful tool in the world of product management. A machine learning product manager takes on an important role in this growing field and is responsible for many different things.
We understand machine learning as the process by which a machine is fed information and taught to learn from it. A machine learning product manager can use this technology in many different ways when it comes to developing a product that meets customers’ expectations.
In this post, we will discuss what exactly a machine learning product manager does, as well as their roles and responsibilities.
What is Machine Learning?
Machine learning is the process by which a machine is fed information and taught to learn from it. Basically, a machine learns how to do something by looking at examples of what that thing should be like.
That being said, machine learning can be used in many different ways when developing products for customers. For instance, if you have an app where people are uploading pictures of their favorite food items on Instagram, then you could create machine learning algorithms so that your product identifies what type of dish someone’s uploaded based on certain features (e.g., color) or even just keywords they’ve typed into their caption.
The responsibility falls on the machine learning product manager because they must decide whether this algorithm will be the best use of machine learning or not, and what type of algorithm is used to gather that information.
This task falls on a machine learning product manager because they are responsible for making sure an organization’s decision-making process stays informed about recent developments in machine learning research. They also need to be able to identify when data scientists have made errors with their models and analyze which versions will result in better outcomes for customers.
Finally, by knowing how different types of algorithms work (e.g., supervised versus unsupervised), this person can make decisions around whether one would be more appropriate than another given certain circumstances related to customer needs or business goals.
What Does a Machine Learning Product Manager Do?
A machine learning product manager is responsible for the end-to-end process of building a machine learning application. This includes identifying customer needs, determining what data to use, and building models on top of it with which to address these needs, then deploying (and maintaining) them in their final production environment.
Machine learning product managers need to meet some PM education requirements in order to perform many of their tasks. The role involves working closely with developers who know how to turn this into an actual functioning software solution that can be deployed and used by customers across any number of business functions – from finance to marketing or sales forecasting.
A great product manager should also have some knowledge around best practices when it comes time for scaling or optimizing such solutions as they grow beyond initial limitations.
Machine Learning Product Manager Roles and Responsibilities
The main task for a machine learning product manager is to devise successful products that are tailored for a specific need. Let’s take a look at some of the specific tasks a machine learning PM completes as part of their job:
1. Develop ML product roadmaps
One of the main responsibilities of the machine learning product manager is to develop a roadmap for future products, which can include predicting how current models will perform in new markets and identifying opportunities for growth.
Developing an ML roadmap might also involve deciding on what data to use as inputs when training a machine learning model; this largely depends on the business goal that needs to be achieved by creating an ML-based solution.
2. Do problem mapping
Part of the machine learning product manager’s role is to identify user needs and then clarify how a system can address these.
A problem map will usually take into account different aspects, such as the necessary tools or infrastructure needed for building ML-based solutions. Also, the resources that are needed from both technical staff and data scientists; any legal considerations (e.g., compliance with privacy regulations); security requirements; cost estimates for implementation; etc.
This provides an overview of what’s required in order to develop a new ML-driven solution that meets all specified criteria while also considering obstacles – like costs or time constraints – that may be encountered during development.
3. Develop validation and test data sets
A machine learning product manager should also be able to help plan an effective validation and testing process.
This includes developing a data set that’s used for training the ML algorithm, as well as another one used for validating its performance and accuracy before releasing it into production. Ideally, this is done in isolation from any live datasets so the model doesn’t get confused with information about real users or their actions.
Validation data sets can be created through sampling (e.g., by randomizing some records), while test sets are usually developed by using segments of previously collected user activity logs or other available resources like social media profiles, etc.)
4. Work with the engineering team and data scientists
Another important LM Product manager task is to work with the product team as well as the engineering team and data scientists to ensure that their machine learning efforts are integrated into existing products, as well as new ones.
Usually, machine learning is integrated into existing products by updating models and algorithms, incorporating new features that are relevant to the machine-learning task at hand (e.g., for regression or classification), or providing feedback on the performance of live production systems with ML models enabled.
In other cases, it can be used as a way to improve pre-existing products through continuous improvement loops.
LM product managers also collaborate very closely with data scientists during all stages of development – from initial strategy conception, where they help identify appropriate user needs and define desired outcomes and PM metrics, to deployment validation testing and ongoing optimization efforts once the model is deployed.
5. Do gap analyses to ensure successful machine-learning implementation.
Doing gap analysis is often an exercise that machine-learning product managers will participate in during the design phase of a project.
Machine learning PMs will work on determining if there are any important attributes missing from the data and whether this could impact performance. Also, mapping out different user journeys to determine which tasks take longer than expected or require repeat visits for completion.
6. Analyze complex statistics
Machine-learning product managers are often tasked with analyzing new data and complex statistics. In order to do that, they should have a decent grasp of the basics (mean, median, standard deviation) as well as more difficult stats like ROC curves.
Analyzing statistics is important as it helps machine-learning product managers determine what metrics are the best for their project.
7. Define product strategy
Product strategy is an important aspect of machine-learning product management. This involves mapping out the different user journeys to determine which tasks take longer than expected or require repeat visits for completion, determining if there are any important attributes missing from the data and whether this could impact performance, as well as analyzing complex statistics.
8. Run beta and pilot programs
Machine-learning product managers are often responsible for running beta and pilot programs. This involves setting up the program, determining metrics to track that would indicate success or failure of the test, as well as analyzing data from these tests in order to identify trends that can be used to optimize new products.
9. Conduct product capabilities evaluations
Machine-learning product managers also need to conduct performance evaluations. This means determining the capabilities of a machine learning system and how its functionality performs according to specific metrics, such as accuracy rates or speed.
10 Do detailed risk analysis
Another important responsibility of machine-learning product managers is detailed risk analysis. This involves assessing the probability and severity of risks that could arise from a system or process, as well as recommending appropriate courses of action to mitigate these risks.
Doing this is crucial because not doing so could lead to unexpected risks that may not have been anticipated. Therefore, machine learning product managers need to be able to make recommendations for mitigating the risk, as well as monitoring and reviewing those actions in order to ensure they are successful. These responsibilities can include finding ways of reducing or eliminating any potential hazards before they occur.
11. Identify mistakes and biases and correct them
Machine learning product managers are also in charge of identifying any biases or mistakes that may exist within an organization. They need to be able to identify these and correct them for the sake of fairness.
Machine learning products can have a huge impact on society, so machine learning product managers need to work hard at ensuring they do no harm by being proactive with corrections before anything bad happens as well as through continuous training efforts.
12. Conduct experimentation and data collection
Machine learning product managers need to present reports on plan and conduct experimentation. They may be in charge of designing the experiment, conducting it, analyzing results, drawing conclusions from those experiments, and then presenting them to stakeholders for feedback before making a decision on what to do next.
They will often work with engineers and developers as they are the ones who implement changes that machine learning products dictate after data is collected.
For example, if there’s an issue with an algorithm or any other aspect of the software development process that needs fixing—the machine learning product manager would identify this issue and oversee its resolution while collaborating closely with these members of their team.
13. Manage high stakeholder technical expectations
Machine Learning Product Managers must have a strong technical background and be fluent in machine learning concepts. They will likely work alongside machine learning engineers to implement changes that their team’s machine learning products dictate after data is collected.
For example, there might be an issue with the algorithm or any other aspect of the software development process that needs fixing—the machine learning product manager would identify this issue and oversee its resolution while collaborating closely with these members of their team.
Other essential ML product management skills include managing high stakeholder expectations, as well as negotiating trade-offs between timelines, resources, and quality requirements on behalf of the organization.
14. Explore and analyze user experience
The machine learning product manager will explore and analyze the user journey to find opportunities where they can improve products with machine learning in the real world.
They’ll create a deep understanding of how customers or users engage with their company, identify patterns in behavior across different demographics as well as any moments when machines are inserted into this process, and determine what types of data need to be collected for future iterations of the software using machine learning toolsets.
They might also have some responsibility for designing customer journeys that leverage AI-powered features like chatbots or virtual assistants. This way, these programs can follow conversational norms while addressing common queries without human intervention.
How to Become a Machine Learning Product Manager?
Machine learning product managers require a deep understanding of machine-generated data and how to integrate AI into an existing business. This includes competencies in mathematics, statistics, computer science, cognition, and cognitive psychology as well as design thinking principles for human-computer interaction.
They should also have familiarity with programming languages like Python or R.
If you’re looking to break into the field without any experience yet there are plenty of options that don’t involve having a degree.
Some of them are:
- Taking online courses from sites like Coursera
- Reading books on machine learning or related topics such as artificial intelligence
- Attending meetups where ML experts present their latest research papers or
- Attending conferences.
Machine learning PM skills
In order to become a machine learning product manager, you’ll need to have some specific skills product strategists require. These can be acquired by joining a PM bootcamp or enrolling in a product management certification training program.
1. A solid understanding of how ML work
You need to know the machine learning basics in order to build products that are actually effective.
This includes being knowledgeable about statistics, probability, and advanced algorithms such as neural networks or self-organizing maps. You’ll also have a good understanding of different types of data structures like trees, graphs, or matrices.
2. Math skills
You’ll need to know calculus and linear algebra in order to model the data, understand its behavior, etc.
If you’re a software developer or engineer working on machine learning products, you will also have knowledge of these topics since they are heavily used with Python programming language for example.
3. Communication skills
At the end of the day, you are a product manager so your team needs to trust and respect you. You need to be able to speak intelligently about machine learning topics with experts in different fields like data science or engineering.
You should also know how to communicate clearly–both verbally as well as on paper–with other managers and stakeholders.
4. Product management skills
You need to be able to plan, build and launch machine learning products. That includes making sure that the product goals are aligned with or support company-wide objectives. It also means understanding and prioritizing customer needs in order for your team to create a valuable solution.
5. Agile methodology knowledge
In order to be a machine learning product manager, you need to have some knowledge of agile methodology. Agile is an iterative process that includes different stages like prototyping, testing, and validation before launching the final solution.
6. Data analysis skills
A machine learning product manager needs to be able to analyze data and use it for the benefit of their company. This means understanding what kind of data is needed, how to collect it, as well as determining its value.
7. Product development skills
A machine learning product manager needs to be able to prioritize, oversee and manage the different stages of development. This means creating a timeline for each stage while also ensuring that deadlines are met.
Machine learning PM average salary
According to Glassdoor, the average salary of a machine learning product manager is $110,843 in the United States.
Why is Machine Learning Important?
Machine learning is important because it has the power to make predictions and decisions without relying on human input, which can lead to innovation. Machine learning product managers are in charge of understanding machine learning models and applying them effectively in a business setting.
There’s no set path for how one becomes a machine learner product manager, but they typically have knowledge about machine learning models, proficiency with math skills, strong communication skills, and aptitude for developing products that utilize machine-learned concepts.
Although machine learning might be quite technical and complex, it is a highly rewarding career that is leading to innovation in many industries.