Businesses across the globe are acknowledging the necessity of utilizing artificial intelligence in becoming a data-driven organization, improving processes, better understanding customer needs, and driving innovation with data. The global AI market value is projected to grow from $47.7 billion in 2021 to $360 billion in 2028 at a CAGR of 33.6%. Despite a promising future, most companies are struggling to scale their AI initiatives. Large volumes of data, governance issues, finding and prioritizing a business use case, etc., are common barriers to AI adoption.
Operationalizing ML Models at scale is complicated, and 87% of data science projects never make it to production because of common challenges with operationalizing ML Models including:
- Setting up CI/CD pipelines so that updates are continuously built and ready for production accurately, securely, and seamlessly
- Longer than expected model development to model deployment lifecycles
- Lack of cross-functional alignment causing a delay in production
- Tediousness of maintaining data and hyperparameter versioning
- Absence of an iterative process enabling rapid experimentation and deployment
- Complexity of monitoring ML models in production
To combat the above challenges, the field of MLOps has emerged. MLOps is an ML engineering practice that aims to unify ML system development (Dev) and ML system operations (Ops). MLOps advocates for better collaboration and communication between data scientists and operations professionals and provides an optimization framework based on DevOps practices for machine learning lifecycle management. By leveraging MLOps, companies can reduce the friction in implementing AI, thereby converting core business needs into AI services significantly faster.
As AI becomes more commonplace in our everyday lives, we also have an increased duty to practice responsible AI and be more conscious of ethics when creating these technologies. With all these new practices coming into the picture, who is that go-to person at your organization who can help connect the dots and work on delivering tangible business value? The answer lies in AI Product Management. There is a growing demand for AI/ML Product Managers as more companies are trying to leverage AI in some form to gain a competitive advantage, whether they’re trying to make more informed business decisions or provide increased value to customers. AI/ML PMs advocate for creating products powered by AI/ML that solve some of the most complicated problems across various industries.
AI/ML PMs have a unique set of responsibilities. They are constantly analyzing the growing landscape of AI/ML and identifying opportunities applicable for their company’s use cases. They drive value, impact, and insights from ever-growing digital data using AI/ML approaches and techniques. Significantly, AI/ML PMs use this deep understanding of business requirements to strategize and build AI-powered products that provide value for customers and improve a company’s bottom line. Businesses can then use the knowledge provided by AI/PMs on both the market and competitors to identify core ways to differentiate and position themselves as key players in the industry.
With the required AI business know-how and skillsets, AI/ML PMs bridge the gap between the functional teams critical for the success of AI implementation, such as Software Engineers, Data Scientists, Data Engineers, ML Engineers, DevSecOps and more. The focus area through these collaborations is on building, training, and deploying ML models in the production environment where it can start extracting real value for the business. They work on ensuring that good quality data is used for training the model, the best model is selected to be operationalized, and once the model is in production, the performance of the model and infrastructure is maintained. Unlocking the black box and explaining the results of ML models is also critical for compliance purposes and in providing visibility to leadership demonstrating the impact of using AI.
Conducting user interviews, working closely with the design team to identify the user workflow, and ideating on a prototype that is easy to use and provides a delightful user experience is also a common occurrence in the day of AI/ML PMs. They envision the long-term roadmap for the company based on different use cases and maintain balance by working closely with engineering to execute on 3-4 months product implementation plan successfully. Collaboration with the sales and customer success representatives helps AI PMs understand the common factors that may hinder product adoption, customer growth and what features can be enhanced using AI/ML solutions to retain customers and reduce churn. Lastly, their partnership with the marketing department on creating product messaging for spreading awareness and capturing the right audience’s attention is also critical to ensure the success of AI products and solutions.
AI/ML Product Managers help adopt a lean approach for building new solutions. They have a broad view of every component that goes into building a successful AI-driven product. Without AI/ML PMs, a company may end up taking a siloed approach to implementing AI and risk investing its resources in poorly designed solutions that could end in catastrophic results. Thus, to thrive in the new age of scaling AI adoption, companies should have AI PMs as an essential part of their organization to help them overcome existing challenges, accelerate AI adoption, and bring positive returns for long-term success.
About the Author
Alankrita Priya is a dynamic AI/ML product manager who specializes in creating B2B SaaS products. She has held multiple PM/TPM roles at big data professional services company, unicorn startups, and Accenture. In her current role at Hypergiant, she is building the Model Operations console for Hyperdrive (Hypergiant’s enterprise AI platform) and responsible for curating best practices surrounding explainable AI, ethical AI, security and regulatory compliance. She holds a degree in Masters of Engineering Management from Duke University and a B.Tech in Engineering from Manipal Institute of Technology, India.
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Good one Alan 🙂