I want to start learning more about AI and ML because I’ve been interested in them. I believe that gaining knowledge in AI and ML will not only satisfy my curiosity but also open up new opportunities for me in the future. Additionally, understanding these technologies can help me stay updated with the latest advancements and contribute to solving real-world problems using AI and ML techniques.
Which challenges do AI/ML PMs answer, and what use cases do they create?
What prerequisites exist for PMs working in the AI field?
At the established tech company I work for, I am currently the PM for an ML algorithm (think Search, Recommendations, Relevance, Risk/Fraud Detection, etc).
The use cases aspect interests me since, in contrast to “normal” PMs, I have a lot of difficulty with it. Because it’s a mature organization, the algorithm’s functionality was established before I joined the team as PM, and our goal result metric is well specified. However, the issue arises when various stakeholders want the algorithm to do different things (for example, suppose the reddit ranking algorithm was optimized for “most entertaining” and now a stakeholder requests that it show new posts or self-posts more effectively). My team is trying to either push back against this or must perform a great deal of analysis work to determine whether or not there is a problem. For me, a data science relationship is far more important to product discovery than traditional user interviews.
Prior to joining my algorithmic team, the majority of PMs had experience managing another algorithmic product. Initially, I pursued a career in data analysis (on the DS track rather than the business track). However, I ultimately concluded that I wasn’t interested in DS enough to pursue a PhD, so I obtained my MBA and transitioned to project management. Having the background in DS is incredibly helpful since it allows me to discuss the model with my DSes in an informed manner and explain the changes I would like to see made (e.g., modifying the loss function, Thompson sampling). Additionally, I can spot when someone is trying to con me >:D
The roadmap is basically a list of ways to improve algorithms. For example, adding new data from team x to see if it improves target metric, changing the algorithm target to see if it improves related metric while keeping target metric the same, and so on. Additionally, some tooling work (e.g., pipeline hardening; alarm if data from team Z is absent) or upgrading the backtesting package to include x metric are needed.
While having technical knowledge can be beneficial for a non-technical PM transitioning into an AI/ML PM role, it is not necessarily essential to become as technical as a data scientist. Understanding the fundamental concepts and principles of AI/ML, along with their applications and limitations, can enable the PM to effectively collaborate with technical teams and make informed decisions.
Additionally, focusing on developing skills in areas such as project management, stakeholder communication, and market analysis can greatly contribute to success in an AI/ML PM role. As an AI/ML PM, the ability to manage resources, timelines, and deliverables becomes crucial in leading successful projects. Effective communication with stakeholders, including understanding their concerns and translating technical jargon into meaningful insights, is also pivotal in building trust and ensuring project alignment.
Furthermore, staying updated with market trends, competitive analysis, and user feedback can help AI/ML PMs make informed decisions and drive product evolution.
Overall, while technical knowledge is valuable, a well-rounded skill set that combines technical understanding with strong project management and communication abilities can greatly enhance the success of an AI/ML PM.
You should be aware of the components of the overall algorithm, including the data pipeline, the prediction model itself (including its training and scoring cycle), the model’s optimization and generalization to actionable units, and the deployment and push pipeline.
To learn about the inner workings of the algorithm, the significance of data collection and cleanup (and the value of a high-quality data pipeline), and the fundamentals of machine learning and neural networks, I would suggest enrolling in some DS-focused online courses. A possible place to start would be Google Developers, which offers some ML crash courses.
Disclaimer: That course covers a wide range of topics, including machine learning engineering, but I haven’t taken it.
It seems to me that all you really need to know is enough to be dangerous or to be able to poke your DSes. I mean, I have no idea how to configure TensorFlow or create models in it. I can, however, identify things like, “Hey, if you make that modification here, won’t that then impact this other part of the model?” because I understand basic principles well enough.
Having a basic understanding of principles allows me to have meaningful discussions and contribute valuable insights to the development process. While I may not possess the expertise to configure TensorFlow or create complex models, my grasp of fundamental concepts enables me to ask critical questions and provide valuable input during discussions or collaborations with experts in the field.
Being in a situation where there is no process and the AI/ML team is inexperienced can be quite challenging. My two biggest challenges have been to:
increase my knowledge of AI, ML, and general data science to the point where the team is being pushed to do better; and
persuade the team leader that a product manager is not a secretary or scapegoat who helps the team avoid doing work.
In order to address the first challenge, I have taken proactive steps to enhance my understanding of AI, ML, and data science by attending relevant workshops and conferences. Additionally, I have actively sought out resources and online courses to further expand my knowledge in these areas. As for the second challenge, I have been consistently communicating with the team leader, highlighting the value a product manager brings in terms of strategic planning, prioritization, and ensuring alignment between business goals and technical execution. By effectively conveying these points,
Indeed! It is ten times tougher when there is no precedent. mostly because the deadlines for agile product management and agile ML product management are entirely different. In agile product management, the focus is on delivering frequent iterations and responding to customer feedback quickly. This requires constant adaptation and prioritization of features.
On the other hand, agile ML product management involves not only managing the development of ML models but also addressing challenges related to data collection, preprocessing, and model training. Therefore, navigating these distinct deadlines can be particularly challenging without any previous experience or established best practices.
I find this AI/ML PM stuff to be really strange. The way you are forcing a technological solution into the job description is that you are starting with the solution and then looking for problems, rather than knowing customers, discovering problems, and solving them with a solution. In my opinion, it is more likely that you will wind up managing the operations of AI/ML-related projects as a technical product manager than as an advocate for the overarching goals of the business or product.
This approach may limit your ability to truly understand the needs and pain points of customers, which is crucial for delivering successful products. Additionally, by focusing solely on the technical aspects, you may overlook important business considerations such as market demand and profitability. It’s important to strike a balance between technology and customer-centricity to ensure long-term success in AI/ML product management.
Even with a broader technical scope than a standard PM, you can still use traditional product approaches (e.g., discovery, creating product vision, test & learn, etc.). AI/ML PMs are responsible for overseeing the development and implementation of artificial intelligence and machine learning technologies within a product. They work closely with data scientists and engineers to ensure the successful integration of these technologies. Additionally, AI and ML PMs must stay updated on the latest advancements in AI and ML to effectively guide their teams and make informed product decisions.
This also applies to other technical PM positions, such as those in data products, infrastructure, APIs, services, security, etc. In these technical PM positions, staying updated on the latest advancements in their respective fields is crucial. This allows them to effectively collaborate with cross-functional teams and make informed decisions that drive product innovation and success. Furthermore, they must possess strong communication skills to bridge the gap between technical and non-technical stakeholders, ensuring a clear understanding of product goals and requirements.
In my last position, I was the technical product manager for data science, which was a challenging job. particularly when applying ML to already-existing items. The agile ML process differs significantly from the traditional agile product workflow when it comes to algorithm development since iterations take place prior to product release for user viewing. Usually, we collaborate with the different teams to identify the most challenging issues to resolve. Frequently, non-ML tasks (basic filtering, matching, etc.) may address the difficult problems, so we pass those along. If machine learning is actually required, we obtain the relevant data and begin munging.
After the data has been cleansed and prepared, we begin the iterative phase by measuring various data points in order to identify the data patterns that have an impact on the problem we are attempting to address. The algorithm can be developed, taught, and evaluated once such patterns are recognized. It cannot then be included in the product that users interact with directly.
This lengthy discovery phase is the reason the roadmap is much longer than for normal agile products. It is your responsibility as PM to make this work understandable to all non-ML experts and to inspire your data scientists to go to work as soon as possible, genuinely understanding the situation. Once the algorithm is out and successful, it’s challenging but gratifying!
AI/ML Product Managers (PMs) tackle various challenges, such as identifying and prioritizing business problems that can be solved using AI/ML, ensuring data quality and availability for training models, and collaborating with cross-functional teams to deploy AI/ML solutions successfully. They also focus on understanding user needs and translating them into product requirements while considering ethical implications and mitigating biases in AI algorithms.
In terms of use cases, AI/ML PMs create applications for personalized recommendations, fraud detection, and natural language processing. They are responsible for conducting market research to identify emerging trends and opportunities for AI/ML applications in various industries.
Additionally, AI/ML PMs work closely with data scientists and engineers to iterate on models, assess their performance, and fine-tune them based on user feedback. They also play a crucial role in educating and training stakeholders on the benefits and limitations of AI/ML technologies to foster a better understanding and adoption of these solutions.
Ultimately, AI/ML PMs aim to leverage the power of AI/ML to drive innovation and enhance user experiences while ensuring ethical and responsible implementation.
Prerequisites for PMs working in the AI field include a strong understanding of AI/ML concepts and technologies, as well as experience in managing complex technical projects. Additionally, knowledge of data management and analysis is crucial, along with the ability to navigate regulatory and privacy considerations related to AI. Familiarity with industry best practices and staying updated on the latest advancements in AI/ML is also important for effectively leading teams and driving innovation in this rapidly evolving field.
In addition to technical skills, effective communication and collaboration are essential qualities for professionals in the AI field. The ability to clearly explain complex concepts to both technical and non-technical stakeholders is crucial for successful project management and implementation. Furthermore, staying abreast of ethical considerations and addressing potential biases in AI algorithms are fundamental to ensuring the responsible and equitable use of AI technology.
Overall, success in the AI field requires a combination of technical expertise, adaptability, and an understanding of the broader social and ethical implications of AI.
The general features of our roadmaps include a clear vision and strategic direction for implementing AI and ML solutions within an organization. We prioritize identifying key milestones and deliverables, as well as setting realistic timelines and resource allocations. Our roadmaps also incorporate regular evaluations and feedback loops to ensure continuous improvement and adaptability to changing circumstances.
Additionally, we emphasize collaboration and communication among stakeholders to foster alignment and support throughout the implementation process. This ensures that all departments and teams are on the same page and working towards a common goal. Our roadmaps also consider the potential risks and challenges associated with AI and ML implementation, allowing us to proactively address any obstacles that may arise.
By employing a systematic and comprehensive approach, we not only ensure the successful implementation of AI and ML solutions but also lay the foundation for long-term success and growth within the organization. Overall, our strategic direction and roadmaps provide a framework to maximize the benefits of AI and ML while minimizing risks and ensuring a smooth and efficient implementation process.
Here’s what an AI machine (ChatGPT) would respond to your question like:
Diving into AI and machine learning can be both fascinating and rewarding. As an AI/ML Product Manager (PM), you’ll encounter various challenges and focus on creating solutions that harness the power of these technologies. Here are some challenges and use cases AI/ML PMs often address:
Challenges:
Data Quality and Quantity: Handling and ensuring the quality and sufficiency of data for training AI models is a significant challenge. PMs need to devise strategies for data collection, cleaning, labeling, and augmentation.
Model Interpretability and Explainability: AI/ML models often function as black boxes. PMs face the challenge of making these models explainable, especially in critical domains like healthcare and finance, where interpretability is crucial.
Ethical and Bias Considerations: Mitigating biases in AI models and ensuring ethical use of AI technologies are paramount concerns. PMs must design frameworks to identify and rectify biases in algorithms.
Scalability and Performance: Building AI systems that can scale effectively to handle large datasets and perform efficiently in real-time scenarios is another challenge. PMs work on optimizing algorithms and architectures for scalability and performance.
Use Cases:
Personalized Recommendations: In e-commerce and entertainment, AI is used for recommending products, movies, music, etc., based on user preferences and behavior.
Healthcare Diagnostics: AI/ML models assist in diagnosing diseases, analyzing medical images, predicting patient outcomes, and personalized treatment plans.
Natural Language Processing (NLP): NLP is used in chatbots, sentiment analysis, language translation, and content summarization.
Predictive Maintenance: AI predicts equipment failure, enabling proactive maintenance in industries like manufacturing, aviation, and automotive.
Prerequisites for AI/ML PMs:
Technical Understanding: A foundational understanding of AI/ML concepts is crucial. You don’t need to be a data scientist, but familiarity with machine learning algorithms, data preprocessing, and model evaluation is essential.
Domain Knowledge: Having domain expertise in the industry you’re working in (healthcare, finance, etc.) helps in understanding specific needs and challenges.
Communication and Leadership Skills: PMs in AI/ML need strong communication skills to bridge the gap between technical teams, stakeholders, and end-users. Leadership qualities are also valuable in guiding teams and making strategic decisions.
Features of Roadmaps:
Roadmaps in AI/ML product management typically include:
Goal Identification: Defining clear goals and objectives aligned with the business and user needs.
Research and Development Phases: Planning for research, experimentation, and development cycles for AI models or products.
Data Strategy: Outlining strategies for data collection, preprocessing, labeling, and ensuring data privacy and security.
Model Deployment and Monitoring: Strategies for deploying models into production, ongoing monitoring for performance, and model maintenance.
Iterative Improvement: Incorporating feedback loops and continuous improvement processes based on real-world usage and outcomes.
Starting with foundational knowledge in AI/ML concepts, and then gaining experience through hands-on projects or courses, would be a great way to embark on this journey.