"Death, Taxes, and a Few Other Things: Connecting the Dots Between Living Standards and Economic Views while Scaling Machine Learning Product Teams"

"Death, Taxes, and a Few Other Things: Connecting the Dots Between Living Standards and Economic Views while Scaling Machine Learning Product Teams"

Living in a world where death and taxes are inevitable, it's only natural that we strive for improvements in our living standards. After all, progress and advancements have become synonymous with human civilization. But here's the catch - even as our living standards improve, our perception of being better off doesn't necessarily follow suit. The goalpost for contentment and satisfaction keeps moving up, parallel to the improvements we witness.

It's an interesting phenomenon, really. We often find ourselves reaching for more, driven by the desire to constantly elevate our circumstances. And in this pursuit, we often push the boundaries and test the limits of what we consider acceptable. We only come to realize the breaking point when it's already behind us, when we look back and say, "Oh, OK, apparently 50 times earnings was too much," a realization we couldn't grasp when we were at 49 times earnings. It's through these experiences that we discover the risks and rewards associated with our choices.

Investors, in particular, are constantly seeking opportunities that provide them with a favorable risk-reward balance. If there's no risk involved, they will bid up the price of an asset until there's no longer any potential for reward. It's like free money lying on the sidewalk - it will always be picked up. This fundamental principle of economics holds true as long as people have wildly different economic experiences. It's these differing experiences that shape our economic views, leading to a wide spectrum of perspectives.

As we navigate the complexities of our ever-evolving world, we encounter another aspect that requires our attention - the role of machine learning in product teams. With the rise of artificial intelligence and data-driven decision making, there is a need to establish suitable roles, skills, and organizational structures for machine learning product teams. One critical aspect is the productization and scaling of data cleanup and processing, a predominantly backend task.

To effectively address this, engineers collaborate closely with data scientists to ensure the scalability and quality of models in production. Here, we come across three options for organizing machine learning product teams:

Option 1: Data Science Reports to Engineering

By having data science report to engineering, we create a seamless alignment between the two disciplines. This approach eliminates the need for a clear delineation between data science and engineering skills, promoting a holistic understanding of the product's requirements.

Option 2: Data Science Reports to Product

Alternatively, data science can report to product teams, placing the emphasis on aligning data science projects with product needs. With this approach, the goals and deliverables of the data science team are fully aligned with the product team's objectives.

Option 3: Data Science Separate from Product and Engineering

Lastly, there is the option of separating data science from both product and engineering, giving the data science team visibility and accessibility throughout the organization. This approach allows the team to operate independently while still contributing to the broader goals of the company.

As a rule of thumb, joint reporting often leads to better alignment between teams, as it ensures a single decision-maker at the top. This fosters clear communication, streamlined workflows, and a shared understanding of objectives.

In conclusion, the interconnectedness of living standards, economic views, and machine learning product teams reveals the intricate nature of our world. While we continue to strive for progress, it's crucial to recognize that our perception of being better off will always be relative. Additionally, as we embrace the advancements in AI and data-driven decision making, we must carefully consider the roles, skills, and organizational structures needed for successful machine learning product teams.

To navigate these complexities effectively, here are three actionable pieces of advice:

• 1. Embrace a growth mindset: Recognize that the goalpost for contentment will always move with progress. Cultivate a mindset that embraces continuous improvement, acknowledging that there will always be room for growth.

• 2. Foster collaboration and alignment: Whether it's within your organization or between different disciplines, seek alignment and collaboration. Joint reporting and clear communication channels can help create a shared understanding of objectives and promote teamwork.

• 3. Embrace change and adaptability: The world is in a constant state of flux, and it's crucial to embrace change and adaptability. Be open to new ideas, technologies, and approaches, as they can often lead to breakthroughs and advancements.

By incorporating these principles into our lives and work, we can navigate the complexities of our ever-changing world, striving for progress while maintaining a balanced perspective.