The Core Responsibilities of the AI Product Manager
Product Managers are responsible for the successful development, testing, handout, and adoption of a product, and for passing the team that implements those milestones. Product administrators for AI must satisfy these same responsibilities, tuned for the AI lifecycle. In the first two articles in this series, we suggest that AI Product Managers( AI PMs) are responsible for 😛 TAGEND
Deciding on the core perform, public, and hoped expend of the AI productEvaluating the input data pipelines and ensuring they are maintained throughout the entire AI product lifecycleOrchestrating the cross functional crew( Data Engineering, Research Science, Data Science, Machine Learning Engineering, and Software Engineering) Deciding on key interfaces and schemes: user interface and suffer( UI/ UX) and feature engineeringIntegrating the model and server infrastructure with existing software productsWorking with ML engineers and data scientists on tech stack blueprint and decision makingShipping the AI product and administering it after releaseCoordinating with the engineering, infrastructure, and place reliability squads to ensure all carried peculiarities can be supported at scale
If you’re an AI product manager( or about to become one ), that’s what you’re signing up for. In this article, we turn our attention to the process itself: how do you fetching a product to market?
Identifying the problem
The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve attained. It chimes naive to state that AI product managers should develop and send produces that improve metrics the business cares about. Though these concepts may be simple to understand, they aren’t as easy in practice.
Agreeing on metrics
It’s often difficult for businesses without a grow data or machine learning practice to define and agree on metrics. Politics, personalities, and the tradeoff between short-term and long-term outcomes can all contribute to a lack of alignment. Many fellowships face a problem that’s even worse: no one knows which bars contribute to the metrics that impact business outcomes, or which metrics are important to the company( such as those reported to Wall Street by publicly-traded fellowships ). Rachel Thomas writes about these challenges in “The problem with metrics is a big problem for AI.” There isn’t a simple fix for these problems, but for new business, devoting early in understanding the company’s metrics ecosystem will pay dividends in the future.
The worst case scenario is when a business doesn’t have any metrics. In this case, the business probably got caught up in the promotion about AI, but hasn’t done any of the preparation.( Fair warning: if the business shortages metrics, it is likely too scarcity penalize about data infrastructure, collect, governance, and much more .) Work with senior management to design and align on relevant metrics, and make sure that executive leadership concurs and consents to using them before starting your experiments and developing your AI products in earnest. Getting this kind of agreement is much easier said than done, particularly because a company that doesn’t have metrics may never have anticipated seriously about what determines their business successful. It may require intense negotiation between different subdivisions, each of which has its own procedures and its own political interests. As Jez Humble said in a Velocity Conference rehearsal period, “Metrics should be pain: metrics must be allowed to conclude you reform what you’re doing.” Don’t expect agreement to come simply.
Lack of clarity about metrics is technological pay importance paying down. Without purity in metrics, it’s absurd to do meaningful experimentation.
A product manager needs to think about ethics-and foster the produce team to think about ethics-throughout the whole product development process, but it’s particularly important when you’re defining the problem. Is it a problem that must be resolved? How can the answer be abused? Those are questions that every produce unit needs to think about.
There’s a substantial literature about ethics, data, and AI, so rather than repeat that discussion, we’ll leave you with a few assets. Ethics and Data Science is a short record that helps developers think through data problems, and includes a checklist that crew members should revisit throughout the process. The Markkula Institute at the University of Santa Clara has an excellent list of resources, including an app to aid ethical decision-making. The Ethical OS also provides excellent an instrument for conjecture through the impact of technologies. And finally-build a team that includes beings of different backgrounds, and who will be affected by your concoctions in different ways. It’s surprising( and upsetting) how many ethical questions could have been avoided if more people “ve thought about” how the products would be used. AI is a strong tool: help it for good.
Addressing the problem
Once you know which metrics are most important, and which bars affect them, you need to run ventures to be sure that the AI products you want to develop actually planned to those business metrics.
Experiments stand AI PMs not only to evaluation presumptions about the relevance and functionality of AI Concoction, but too to understand the effect( if any) of AI products on the business. AI PMs must ensure that experimentation arises during three phases of the product lifecycle 😛 TAGEND
Phase 1: ConceptDuring the concept phase, it’s important is required to determine whether it’s even possible for an AI product “intervention” to move an upstream business metric. Qualitative ventures, including research surveys and sociological studies, can be very useful here.For example, many companies use recommendation engines to boost auctions. But if your produce is highly specialized, purchasers may come to you knowing what they want, and a recommendation engine precisely get in accordance with the arrangements. Experimentation should show you how your patrons use your website, and whether a recommendation engine would help the business.Phase 2: Pre-deploymentIn the pre-deployment phase, it’s essential to ensure that certain metrics thresholds are not contravened by the core functionality of the AI product. These measures are commonly referred to as guardrail metrics, and they ensure that the product analytics aren’t devoting decision-makers the wrong signal about what’s actually important to the business.For example, a business metric for a rideshare companionship might be to reduce pickup time per user; the guardrail metric might be to maximize trip-ups per consumer. An AI product could easily reduce median getaway occasion by throw applications from users in hard-to-reach locatings. However, that act should be reflected in negative business outcomes for the company overall, and eventually slow adoption of the service. If this sounds fanciful, it’s not hard to find AI systems that took inappropriate actions since they are optimized a poorly thought-out metric. The guardrail metric is a check to ensure that an AI doesn’t make a “mistake.”When a measure becomes a target, it ceases to be a good measuring( Goodhart’s Law ). Any metric can and will be abused. It is useful( and fun) for the change team to brainstorm creative ways to game the metrics, and think about the unintended side-effects this might have. The PM time needs to gather the team and expect “Let’s think about how to abuse the pickup time metric.” Someone will surely come up with “To minimize pickup time, we could just drop all the travels to or from remote locations.” Then you can think about what guardrail metrics( or other means) you can use to keep the system working appropriately.Phase 3: Post-deploymentAfter deployment, the product needs to be instrumented to ensure that it continues to behave as expected, without harming other organisations. Ongoing monitoring of critical metrics is yet another form of experimentation. AI performance tends to degrade over duration as the environmental issues modifications. You can’t stop watching metrics merely because the product has been deployed.For example, an AI product that helps a robe manufacturer understand which textiles to buy will become stale as ways change. If the AI product is successful, it is likely to even cause those alters. You must spot when the example has become stale, and retrain it as needed.
Fault Tolerant Versus Fault Intolerant AI Problems
AI product overseers need to understand how sensitive their activity is to error. This isn’t always simple, since it doesn’t just take into account technical hazard; it also has to account for social risk and reputational shatter. As we mentioned in the first article of this serial, an AI application for product recommendations can make a lot of mistakes before anyone notices( discounting concerns about bias ); this has business impact, of course, but doesn’t cause life-threatening harm. On the other hand, an autonomous vehicle genuinely can’t afford to make any mistakes; even though they are the autonomous vehicle is safer than a human move, you( and your fellowship) will take the blame for any accidents.
Planning and managing the project
AI PMs have to build tough options when deciding where to apply limited resources. It’s the age-old “choose two” rule, where the parameters are Speed, Quality, and Features. For example, for a mobile phone app that uses object detection to identify domesticateds, rushed is a requirement. A product director may relinquish either a more diverse set of animals, or the accuracy of detection algorithms. These decisions have stunning implications on project length, resources, and goals.
Figure 1: The” elect two” regulation
Similarly, AI product administrators often need to choose whether to prioritize the scale and jolt of such products over the difficulty of product blooming. Times ago a state and fitness technology fellowship realized that its content moderators, used to manually spot and remediate offensive content on its pulpit, were experiencing extreme fatigue and very poor mental health outcomes. Even beyond the humane considerations, moderator burnout was a serious product issue, in that the company’s platform was rapidly growing, thus uncovering the average user to more potentially offensive or illegal material. The rigor of content moderation work was exacerbated by its repetition mood, determining it potential candidates for automation via AI. However, the difficulty of developing a robust material equanimity organization at the time was significant, and would have necessary years of development time and research. Ultimately, the company decided to simply drop the most social components of the platform, policy decisions which restraint overall swelling. This tradeoff between affect and evolution hurdle is particularly relevant for products based on deep teach: breakthroughs often lead to unique, plausible, and very lucrative produces, but investing in commodities with a high chance of downfall is an obvious probability. Concoctions based on deep learn can be difficult( or even inconceivable) to develop; it’s a classic “high return versus high risk” situation, in which it is inherently difficult to calculate return on investment.
The final major tradeoff that AI product overseers must evaluate is how much time to deplete during the R& D and designing times. With no restrictrictions on handout dates, PMs and engineers alike would choose to spend as much time as necessary to nail the concoction destinations. But in the real world, concoctions need to ship, and there’s rarely sufficient time to do the research necessary to ship the best possible product. Therefore, concoction managers must make a judgment call about when to ship, and that call is usually based on incomplete experimental upshots. It’s a balancing play, and admittedly, one that can be very tricky: achieving the product’s points versus get the make out there. As with traditional application, the best way to achieve your goals is to put something out there and iterate. This is particularly true for AI products. Microsoft, LinkedIn, and Airbnb has been particularly candid about their wanders towards building an experiment-driven culture and the technology required to support it. Some of the most wonderful exercises are captured in Ron Kohavi, Diane Tang, and Ya Xu’s book: Trustworthy Online Controlled Experiments: A Practical Guide to A/ B Testing.
The AI Product Development Process
The development chapters for an AI project map virtually 1:1 to the AI Product Pipeline we was reflected in the second article of this streak.
Figure 2: CRISP-DM were compatible with AI Pipeline
AI projects require a “feedback loop” in both the concoction development process and the AI products themselves. Because AI products are inherently research-based, experimentation and iterative evolution are necessary. Unlike traditional application developing, in which the inputs and results are often deterministic, the AI development cycle is probabilistic. This involves various important modifications to how campaigns are set up and executed, regardless of the project management framework.
Understand the Customer and Objectives
Product directors is necessary to ensure that AI projects assemble qualitative information about customer behavior. Because it might not be intuitive, it’s important has pointed out that traditional data measurement implements are more effective at valuing intensity than sentiment. For most AI products, the produce administrator will be less interested in the click-through rate( CTR) and other quantitative metrics than they find themselves in the continued relevance of the AI product to the user. Therefore, traditional commodity research teams must engage with the AI team to ensure that the compensate intuition is relevant for AI product increase, as AI practitioners are likely to lack the appropriate skills and experience. CTRs are easy to measure, but if you build a system designed to optimize these various kinds of metrics, you might find that the system relinquishes actual usefulness and user satisfaction. In this case , no matter how well the AI product contributes to such metrics, it’s output won’t ultimately provide the goals of the company.
It’s easy concentrated on the wrong metric if you haven’t done the proper study. One mid-sized digital media company we interviewed reported that their Marketing, Advertising, Strategy, and Product crews formerly wanted to build an AI-driven user traffic forecast tool. The Marketing team improved the first framework, but because it was from marketing, the simulate optimized for CTR and head transition. The Advertising team was more interested in cost per precede( CPL) and lifetime value( LTV ), while the Strategy team was aligned to corporate metrics( income repercussion and total active users ). As a upshot, many of the tool’s consumers were dissatisfied, even though the AI performed perfectly. The eventual solution was the development of multiple prototypes that optimize for different metrics, and the redesign of the tool so that it could present those productions clearly and instinctively to different kinds of users.
Internally, AI PMs must engage stakeholders to ensure alignment with the most important decision-makers and top-line business metrics. Put simply , no AI product will be successful if it never launches, and no AI product will propel unless the project is sponsored, money, and connected to important business objectives.
Data Exploration and Experimentation
This phase of an AI project is laborious and time consuming, but completing it is one of the strongest gauges of future success. A concoction needs to balance the speculation of resources against the risks of moving forward without a full understanding of the data landscape. Acquiring data is very difficult, especially in regulated industries. Once relevant data has been obtained, understanding what is valuable and what is simply noise necessary statistical and technical rigor. AI product managers probably won’t do the research themselves; their character is to guide data scientists, reporters, and domain experts towards a product-centric evaluation of the data, and to inform meaningful experiment design. The objective is to have a measurable signal for what data exists, solid insights into that data’s relevance, and a clear vision of where to concentrate efforts in designing features.
Data Wrangling and Feature Engineering
Data wrangling and boast engineering is the most difficult and important phase of every AI project. It’s generally accepted that, during a typical make developing cycle, 80% of a data scientist’s time is spent in feature engineering. Trend and tools in AutoML and Deep Learning have certainly shortened the time, talents, and endeavor required to build a prototype, if not an actual commodity. Nonetheless, building a superior facet pipe or pattern design will always be worthwhile. AI product overseers should make sure project designs account for the time, effort, and people needed.
Modeling and Evaluation
The modeling phase of an AI project is baffling and difficult to predict. The process is inherently iterative, and some AI projects miscarry( for the right reasons) at this station. It’s easy to understand what procreates this step difficult: there is rarely a sense of steady progress towards a objective. You experiment until something handiworks; that might happen on the first day, or the hundredth era. An AI product administrator must cause the team members and stakeholders when there is no discernible “product” to show for everyone’s labor and investment. One strategy for maintaining motivating is to push for short-term bursts to beat a rendition baseline. Another would be to start multiple weaves( maybe even multiple programmes ), so that some will be able to demonstrate progress.
Unlike traditional software engineering projects, AI product overseers must be heavily to participate in the build process. Engineering managers are usually responsible for realizing sure all the components of a software produce are properly compiled into binaries, and for organizing improve writes meticulously by version to ensure reproducibility. Many mature DevOps processes and tools, sharpened over years of successful software concoction releases, conclude these processes more manageable, but they were developed for traditional software products. The equivalent tools and processes simply do not exist in the ML/ AI ecosystem; when they do, they are rarely mature enough to use at magnitude. As a decision, AI PMs must take a high-touch, customized approaching to direct AI products through make, deployment, and release.
Like any other production software system, after an AI product is live it must be monitored. However, for an AI product, both model concert and work recital must be monitored simultaneously. Notifies that are triggered when the AI product acts out of specification may need to be routed differently; the in-place SRE team may not be able to diagnose technical issues with the pattern or data pipelines without support from the AI team.
Though it’s difficult to create the “perfect” project plan for monitoring, it’s important for AI PMs to ensure that project reserves( specially engineering aptitude) aren’t immediately secreted when the commodity has been deployed. Unlike a traditional application concoction, it’s hard to define when an AI product has been deployed successfully. The development process is iterative, and it’s not over after the produce has been deployed-though, post-deployment, the stakes are higher, and your options for dealing with issues are more limited. Therefore, members of the development team must remain on the upkeep team to ensure that there is proper instrumentation for logging and monitoring the product’s health, and to ensure that there are resources available to deal with the inevitable troubles that is an indication after deployment.( We call this “debugging” to distinguish it from the evaluation and testing that takes place during commodity improvement. The final essay in this series will be devoted to debugging .)
Among runnings designers, the idea of observability is gradually replacing monitoring. Monitoring requires you to predict the metrics you need to watch in advance. That clevernes is certainly important for AI products-we’ve talked all along about the importance of ensuring that metrics. Observability is critically different. Observability is the ability to get the information you need to understand why the system behaved the road it does; it’s less about valuing known lengths, and more about the ability to diagnose “unknown unknowns.”
Executing on an AI Product Roadmap
We’ve spent a lot of season talking about proposing. Now let’s shift gears and discuss what’s needed to build a product. After all, that’s the point.
AI Product Interface Design
The AI product director must be a member of the design team from the beginning, been assured that the commodity provides the desired outcomes. It’s important to account for the ways a product will be used. In the best AI products, users can’t tell how the underlying simulates affect its own experience. They neither know or care that there is AI in the application. Take Stitch Fix, which consumes a multitude of algorithmic approachings to provide customized mode recommendations. When a Stitch Fix user interacts with its AI products, they is compatible with the prophecy and suggestions locomotives. The information they treated with during that suffer is an AI product-but they neither know , nor help, that AI is behind everything they understand. If the algorithm makes a perfect prediction, but the user can’t imagine wearing its consideration of this agenda item they’re shown, the produce is still a failure. In reality, ML sits are far from perfect, so it is even more imperative to fingernail the user experience.
To do so, make directors is necessary to ensure that blueprint gets an equal seat at the counter with engineering. Designers are more attuned to qualitative research about user behavior. What signals show user satisfaction? How do you improve products that satisfy customers? Apple’s sense of design, seeing things that “just wreak, ” pioneered through the iPod, iPhone, and iPad products is the foundation of their business. That’s what you need, and you need that input from the beginning. Interface design isn’t an after-the-fact add-on.
Picking the Right Scope
“Creeping featurism” is a problem with any software commodity, but it’s a particularly dangerous problem for AI. Focus your produce increase campaign on problems that are relevant to the business and purchaser. A successful AI product measurably( and positively) bangs metrics that matter to the business. Therefore, limit the dimensions of an AI product to aspects that can create this impact.
To do so, begins with a well-framed hypothesis that, upon validation through experimentation, will grow meaningful aftermaths. Doing this effectively means that AI PMs must learn to alter business intuitions into produce growth implements and processes. For example, if the business seeks to understand more about its purchaser basi in order to maximize lifetime value for a subscription make, an AI PM would do well to understand the tools available for customer and product-mix segmentation, recommendation instruments, and time-series forecasting. Then, when it comes to developing the AI product roadmap, the AI PM can focus engineering and AI units on the right experiments, the remedy outcomes, andthe smoothest track to production.
It is alluring to over-value the performance additions achieved through the use of more complex modeling techniques, leading to the dreaded “black box” problem: modelings for which it’s difficult( if not impossible) to understand the relations between the input and the output. Black box representations are seldom useful in business environments for several grounds. First, being able to explain how the pattern runs is often a prerequisite for manager approval. Ethical and regulatory considerations often require a detailed understanding of the data, obtained peculiarities, pipes and valuing mechanisms to participate in the AI system. Solving problems with the simplest model possible is always preferable, and not just because it leads to simulates that are interpretable. In addition, simpler modeling approachings are more likely to be supported by a wide variety of frames, data pulpits, and speeches, increasing interoperability and decreasing technical debt.
Another scoping consideration concerns the processing engine that will power the commodity. Problems that are real-time( or near real-time) in sort can only be addressed by most performant brook processing designs. Lessons of this include product recommendations in e-commerce arrangements or AI-enabled messaging. Stream processing asks significant engineering endeavor, and it’s important to account for that campaign at the beginning of development. Some machine learning approaches( and numerous software engineering traditions) are simply not appropriate for near-real time applications. If the problem at hand is more flexible and less interactive( such as offline churn probability prediction ), batch processing is probably a good coming, and is typically easier to integrate with the average data stack.
Prototypes and Data Product MVPs
Entrepreneurial product directors are often associated with the motto “Move Fast and Break Things.” AI product mangers lives and die by “Experiment Fast So You Don’t Break Things Later.” Take any social media company that sells ads. The timing, sum, and type of ads exposed to segments of a company’s user population are overwhelmingly determined by algorithms. Clients contract with the social media company for a certain chose budget, expecting to achieve particular gathering exposure thresholds that can be measured by relevant business metrics. The fund that is actually spent successfully is referred to as fulfillment, and is directly related to the revenue that each patron generates. Any change to the underlying examples or data ecosystem, such as how specific demographic boasts are weighted, can have a spectacular impact on the social media company’s revenue. Experimenting with brand-new poses is essential-but so is yanking an underperforming simulate out of production. This is only one example of why rapid prototyping is important for squads constructing AI products. AI PMs must create an environment in which endless experimentation and failure are tolerated( even celebrated ), along with supporting the processes and tools that enable experimentation and learning through failure.
In a previous section, we introduced the importance of user research and interface design. Qualitative data collection tools( such as SurveyMonkey, Qualtrics, and Google Forms) should be joined with boundary prototyping tools( such as Invision and Balsamiq ), and with data prototyping implements( such as Jupyter Notebooks) to anatomy ecological systems for concoction growth and testing.
Once such a better environment exists, it’s important for the make administrator to codify what constitutes a “minimum viable” AI product( MVP ). This produce should be robust enough to be used for customer research and quantitative( simulate evaluation) experimentation, but simple enough that it can be quickly disposed or adjusted in favor of new iterations. And, while the word “minimum” is important, don’t forget “viable.” An MVP needs to be a product that can stand on its own, something that customers will want and use. If the commodity isn’t “viable”( i.e ., if a user wouldn’t want it) you won’t be able to conduct good consumer study. Again, it’s important to listen to data scientists, data engineers, software makes, and motif unit representatives when deciding on the MVP.
Data Quality and Standardization
In most societies, Data Quality is either an engineering or IT problem; it is rarely addressed by the product team until it blocks a downstream process or project. This relationship is impossible for squads developing AI products. “Garbage in, scrap out” holds true for AI, so good AI PMs must concern themselves with data health.
There are many excellent aids on data quality and data governance. The specifics are outside the scope of this article, but here are some core principles that should be included in any commodity manager’s toolkit 😛 TAGEND
Beware of “data cleaning” approaches that expense your data. It’s not data scavenging if it changes the core dimensions of the underlying data. Look for peculiarities in your data( for example, data from legacy organisations that truncated text realms to save room ). Understand the risks of bad downstream standardization when strategy and implementing the data collected( e.g. arbitrary stanch, stop parole removal .). Ensure data stores, key grapevines, and queries are properly documented, with structured metadata and a well-understood data flow.Consider how time repercussions your data assets, as well as seasonal accomplishes and other biases.Understand that data bias and artifacts can be introduced by UX selects and investigation intend.
Augmenting AI Product Management with Technical Leadership
There is no instinctive mode to prophesy what will work best in AI product increase. AI PMs can build amazing things, but this often comes predominantly from the right frameworks rather than the correct tactical activities. Countless new tech capabilities have the potential to enable software engineering squandering ML/ AI techniques more quickly and accurately. AI PMs shall be required to leverage newly emerging AI techniques( image upscaling, synthetic verse generation using adversarial systems, buttres ascertain, and more ), and partner with expert technologists to articulate these tools to use.
It’s unlikely that every AI PM will have world-class technical intuition in addition to excellent product sense, UI/ X know-how, patron knowledge, leadership knowledge, and so on. But don’t told that make dejection. Since one person can’t be an expert at everything, AI PMs it is necessary formation a partnership with a engineering supervisor( e.g ., a Technical Leador Lead Scientist) who knows the state of the arts and is well aware of current study, and trust that tech leader’s educated intuition.
Finding this critical technical partner can be difficult, especially in today’s competitive geniu market. However, all is not lost: there are many excellent technical commodity chairwomen out there pretense as skilled engineering managers.
Product manager Matt Brandwein suggests mentioning what potential tech leads do in their idle age, and taken due note of which domains they find handsome. Someone’s current persona often doesn’t reveal where the best interest and knack lie. Most importantly, the AI PM should look for a tech result who is able mitigate their own weaknesses. For example, if the AI PM is a dreamer, picking a technological leading with operational event is a good idea.
Testing ML/ AI Products
When a product is ready to ship, the PM will work with user research and engineering units to develop a exhaust contrive that collects both qualitative and quantitative user feedback. The majority of this data will be concentrated on user interaction with the user interface and front end of the product. AI PMs must also plan to collect data about the “hidden” functionality of the AI product, the division no consumer ever considers instantly: framework rendition. We’ve discussed the need for proper instrumentation at both the pose and business stages to gauge the product’s effectiveness; this is where all of that strategy and hard work pays off!
On the pose feature, operation metrics that were validated during improvement( predictive dominance, pattern fit, accuracy) must be constantly re-evaluated as the prototype is exposed to more and more unseen data. A/ B testing, which is frequently used in web-based software development, is useful for evaluating model performance in creation. Most corporations previously have a framework for A/ B testing in their exhaust process, but some may need to invest in testing infrastructure. Such financings are well worth it.
It’s inescapable that the pose will require adjustments over day, so AI PMs is necessary to ensure that whoever is responsible for the concoction post-launch has access to the development team in order to investigate and resolve issues. Now, A/ B testing has another benefit: the ability to run champion/ challenger pose evaluations. This framework allows for a deployed simulate to run uninterrupted, while a second model is evaluated against a test of the entire population. If the second model outshines the original, we are to be able to simply be swapped out-often without any downtime!
Overall, AI PMs should be continued to actively participate in the early liberation lifecycle for AI products, taking responsibility for coordinating and coping A/ B tests and user data collection, and resolving issues such as the product’s functionality.
In this article, we’ve focused primarily on the AI product development process, and mapping the AI product manager’s responsibilities to each stage of that process. As with many other digital commodity proliferation repetitions, AI PMs is a requirement rest assured that the problem to be solved is both a number of problems that ML/ AI can solve and a problem that is vital to the business. Once this criteria has been met, the AI PM has to determine whether the commodity should be set up, considering the myriad of technological and ethical considerations at comedy when developing and releasing a yield AI system.
We propose the AI Product Development Process as a blueprint for AI PMs of all industries, who may develop myriad different AI products. Though this process is by no means careful, it emphasizes the kind of critical thinking and cross-departmental collaboration necessary to success at each stage of the AI product lifecycle. However, regardless of the process you use, experimentation is the key to success. We’ve said that frequently, and we aren’t tired: the more ventures you can do, the more likely you are to build a product that works( i.e ., positively impacts metrics the company to be concerned about ). And don’t forget qualitative metrics that help you understand user behavior!
Once an AI system is liberated and in use, however, the AI PM has a somewhat distinct role in product upkeep. Unlike PMs for many other software commodities, AI PMs must ensure that robust testing fabrics are constructed and utilized not only during the development process, but also in post-production. Our next article focuses on perhaps the most important phase of the AI product lifecycle: upkeep and debugging.
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