How To Get Generative AI Into Production

Let’s talk about something that’s been buzzing around the AI community: sprints for generative AI. As amazing and magical as Gen AI sounds, getting those models up and running can be a real roller coaster. From hitting roadblocks with output quality to facing the demons of production deployment, it’s not all smooth sailing.

But hey, don’t let that scare you off just yet. Planning your sprints effectively can be your secret weapon to navigating these challenges. If you have already tried out some of the existing sprint methodologies and quickly realized that they don't really work, then you are in the right place. Let’s dive into how you can plan sprints that set your Gen AI projects on the path to success.

The Bumps in the Road

You’ve likely faced this: you pour countless hours into developing a Gen AI model, only to find the output quality is subpar. Or maybe you’ve struggled with measuring that output quality — what even are the parameters? And let’s not even start on how exhausting R&D can be when it doesn’t pay off. Then, just when you think you’ve nailed it, transitioning your model from a test setup to a full-blown production environment turns out to be a nightmare.

Sounds familiar? You’re not alone. These challenges are common, but the good news is, there’s a way around them.

A Game Plan for Gen AI Sprints

Let’s break it down. Here’s a strategy that can streamline your Gen AI sprints:

1. Always aim for an MVP in production.
2. Divide development into two main tracks: ML and Production Serving.
3. Have alternating R&D and Production weeks.
4. Release iteratively and frequently.

Step 1: Start with an MVP Mindset

First things first, always aim to get a Minimum Viable Product (MVP) out there. You want something functional in the users’ hands as soon as possible. Why? Because real user feedback is like gold. It helps you fine-tune and iterate better. The Goal is to deliver something in the hands of the users every single week.

Step 2: Two Tracks Are Better Than One

Let’s talk about dividing and conquering. Split your development into two tracks — one focusing on the ML side and the other on Production Serving.

ML Track:

• Focus on training the models.
• Tuning and optimizing — it’s all about those hyper-parameters, algorithms, and datasets.

Production Serving Track:

• Think scalability — your solution should be robust enough for the real world,
• Integrate those models smoothly into your production pipeline.

Step 3: Alternate Between R&D and Production Weeks

Here’s the magic sauce — have alternating weeks dedicated to R&D and Production.

R&D Weeks:

• Dive into exploratory research.
• Develop quick prototypes to test out your wild ideas.

Production Weeks:

• Stabilize and refine existing prototypes.
• Get ready for deployment.

Step 4: Release Early, Release Often

This is where you bring step 2 and step 3 together.

The mantra here is to release iteratively and frequently. But here’s the twist: stagger your releases. When your ML team is in the throes of development, have your Production team focus on the release cycle. It’s like a symphony where each section has its moment to shine.

Wrapping Up

There you have it — a friendly guide to planning your sprints for Gen AI. By aiming for MVPs, dividing your tracks, alternating your focus weeks, and releasing frequently, you’re setting up a system that thrives on continuous improvement and real-world feedback.

Don’t just take my word for it, give it a try and see how it transforms your Gen AI projects. And hey, why not share your experiences and tips in the comments below? Let’s keep the conversation going.

Check out what AI I build at zpqv.com
Until next time, happy coding and may your AI models be predictable!

Reference:

Interaction Design Foundation — IxDF. (2020, November 23). Minimum Viable Product (MVP) and Design — Balancing Risk to Gain Reward. Interaction Design Foundation — IxDF. https://www.interaction-design.org/literature/article/minimum-viable-product-mvp-and-design-balancing-risk-to-gain-reward

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