Itinai.com hyperrealistic mockup of a branding agency website 406437d4 4cdd 41bb aaa1 0ce719686930 0
Itinai.com hyperrealistic mockup of a branding agency website 406437d4 4cdd 41bb aaa1 0ce719686930 0

Building Your Model Is Not Enough — You Need To Sell It

The text emphasizes the importance of selling machine learning models beyond just building them. It provides five key insights derived from the author’s documentation experience, including logging experiments, demonstrating performance, describing the model building steps, assessing risks and limitations, and testing data stability. The author outlines their personal experiences in handling complex machine learning projects.

 Building Your Model Is Not Enough — You Need To Sell It

“`html





AI Solutions for Middle Managers

Building Your Model Is Not Enough — You Need To Sell It

5 tips that can help your models go live in production

Model Deployment

When I started in Data Science, my focus was primarily on improving technical skills like programming and model building. After a few years, my interest shifted towards model deployment and MLOps, leading to my transition into Machine Learning Engineering. Public speaking and presentations were always part of the job, especially when conveying results to a non-technical audience. However, things changed last year when I started working on more complex projects with potential reputational or financial risks for the hiring companies.

At this point, models required validation by a committee of both technical and non-technical reviewers before going live in production. This necessitated proper documentation, covering everything from architecture and training methodology to performance reports and experiment history. It meant that having good performance was not enough; I had to convince others, from data scientists to risk assessment specialists, that my models were not only effective but also safe.

In essence, I had to learn how to sell them.

Initiating the process of detailing my models initially served as a crucial requirement for validation, but it swiftly evolved into a routine ingrained in my approach, extending even to personal projects. Within this article, I aim to impart five valuable insights derived from my documentation experience that may assist you in crafting your own.

  1. Log your experiments
  2. This is the foundation of all your documentation. When you start developing a model, you engage in a trail and error process during which you try different kind of pre-processing, model architectures, hyperparameters, and feature engineering.

  3. Show performance and explain metrics
  4. One of your goals is to show that your model has good performance. But what does that mean? And compared to what?

  5. Describe the model building steps
  6. It’s easier to convince people to use a model when they feel confident in how to implement it and understand how it’s made.

  7. Thoroughly assess the risks and the limitations of your model
  8. That part can be one of the most important depending on who reads your documentation.

  9. Test the stability of your data
  10. The first time I was asked to do backtesting, I was skeptical but it can be insightful and I now integrate it in my habits.

In wrapping up, I hope the insights shared can be a helpful guide for creating strong machine learning models and making a case for putting them into action. Drawing from personal experiences in handling complex projects with potential risks, I’ve outlined 5 key takeaways:

  1. Log Your Experiments
  2. Show Performance and Explain Metrics
  3. Describe the Model Building Steps
  4. Thoroughly Assess Risks and Limitations
  5. Test the Stability of Your Data

If you have any questions, don’t hesitate to leave it in the comments, I’ll do my best to answer you!

If you want to evolve your company with AI, stay competitive, use for your advantage Building Your Model Is Not Enough — You Need To Sell It.

Discover how AI can redefine your way of work. Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI.

Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes.

Select an AI Solution: Choose tools that align with your needs and provide customization.

Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously.

For AI KPI management advice, connect with us at hello@itinai.com. And for continuous insights into leveraging AI, stay tuned on our Telegram t.me/itinainews or Twitter @itinaicom.

Spotlight on a Practical AI Solution:

Consider the AI Sales Bot from itinai.com/aisalesbot designed to automate customer engagement 24/7 and manage interactions across all customer journey stages.

Discover how AI can redefine your sales processes and customer engagement. Explore solutions at itinai.com.



“`

List of Useful Links:

Itinai.com office ai background high tech quantum computing 0002ba7c e3d6 4fd7 abd6 cfe4e5f08aeb 0

Vladimir Dyachkov, Ph.D – Editor-in-Chief itinai.com

I believe that AI is only as powerful as the human insight guiding it.

Unleash Your Creative Potential with AI Agents

Competitors are already using AI Agents

Business Problems We Solve

  • Automation of internal processes.
  • Optimizing AI costs without huge budgets.
  • Training staff, developing custom courses for business needs
  • Integrating AI into client work, automating first lines of contact

Large and Medium Businesses

Startups

Offline Business

100% of clients report increased productivity and reduced operati

AI news and solutions