Shipping software projects — especially in machine learning (ML) and AI—can feel like navigating a maze of unknowns. Over the years, I’ve learned (often the hard way) what works and what doesn’t when it comes to delivering projects on time and with impact. Below, I share my most valuable lessons, distilled into actionable advice to help you ship your project faster, with fewer headaches, and greater confidence.
Write for a Specific Audience and Topic
One of the easiest ways to reduce complexity in a project is to focus on a specific audience and topic. Building for “everyone” leads to endless scope creep, vague requirements, and delayed delivery. When you narrow your audience and define your topic, you’ll find it much easier to: - Define the core problem you’re solving. - Decide which features and functionality are essential.
In short, specificity speeds up decision-making—and delivery.
Set a Deadline and Stick to It
Deadlines are your best friend, not your enemy. View them as a challenge: “What can I ship by this date, no matter what?”
The key here is to ship whatever is ready—even if it’s incomplete or imperfect. Deadlines force you to prioritize ruthlessly and get the project out the door. Remember, you can always iterate later.
Work Backwards from the End Goal
When starting any project, first ask: “What does success look like?” Work backwards from there, breaking the project into essential questions:
- What’s the end result look like, exactly?
- How many features are needed?
- What’s absolutely required vs. what’s nice to have?
- How perfect does it need to be?
- How long will each step take?
- What tasks need to happen first, and what preparation is required?
By answering these questions, you’ll create a clear roadmap, reduce uncertainty, and avoid aimless wandering through your project.
Break It Down into Manageable Pieces
Big projects can feel overwhelming, which is why breaking them into smaller components is essential. Tackle each piece in focused, deep work sessions. Every time you complete a component, you’ll feel a sense of accomplishment—which keeps motivation high.
Example: If you’re building an ML pipeline, treat data preprocessing, model training, and evaluation as separate milestones rather than one big, nebulous task.
Be Crystal Clear on Completion Criteria
Ambiguity is the enemy of progress. For every stage of your project, define what “done” looks like. This clarity not only helps you move faster but also ensures you avoid endlessly tweaking and polishing.
Start Small (and Learn Quickly)
Start with a small prototype or proof of concept. This gives you a realistic sense of what it will take to deliver the full project while helping you identify potential roadblocks early.
Starting small also lowers the stakes, making it easier to experiment and iterate without fear of failure.
Track Progress and Celebrate Wins
Use tools like Trello, Jira, or even a simple Kanban board to track your tasks and progress. Keep completed tasks visible—seeing progress in real time is a great motivator.
Why? Because software projects are as much about emotional management as technical execution. Visible progress keeps momentum alive and helps you push through inevitable roadblocks.
Don’t Reinvent the Wheel
Time is your most valuable resource. Wherever possible, lean on existing tools, libraries, and frameworks to speed up development.
For instance, in ML and AI, libraries like TensorFlow, scikit-learn, or pre-trained models can save you weeks of effort. You can always revisit and optimize later if needed. The goal is to get something functional in front of users quickly.
Every Version Should Be Better
A common pitfall is aiming to ship a perfect product from day one. Don’t. Instead, focus on shipping a better version with each iteration.
Why? Because you’ll learn more from real-world usage and user feedback than from endless internal debates or polishing. Each version builds on the last, so it doesn’t have to be perfect—it just has to be better.
Define Success Criteria for the First Version
Before you start coding, define what “success” means for the first version. What must it accomplish to be considered shippable?
Write these criteria down. This clarity will keep you focused and prevent scope creep.
Do Your Homework (Research Before You Start)
Before diving into implementation, spend time researching:
- What tools, frameworks, or libraries can you use?
- What’s the best structure for the project?
- How have others solved similar problems?
A solid research phase will save you countless hours of trial and error later.
Prioritize Must-Haves vs. Nice-to-Haves
Not all features are created equal. Early in the project, create two lists:
- Must-haves: Features that are absolutely necessary for the project to function.
- Nice-to-haves: Features that would be great to include but aren’t essential.
Focus on the must-haves first. Nice-to-haves can wait for later versions.
Mistakes Will Happen—Don’t Take Them Personally
No matter how well you plan, mistakes will happen. Bugs will slip through, features might not work as expected, and some users may complain. Don’t take it personally.
Instead, treat every mistake as an opportunity to learn and improve. Fix the issue, iterate, and move forward.
Ship Now, Improve Later
Shipping something today—even if it’s imperfect—is far more valuable than shipping something “perfect” six months from now. Why? Because you learn more from trial and error than from endless planning.
Fast iterations drive faster learning—and faster progress.
Final Thoughts: Start Shipping
Shipping software projects is never easy, especially in ML and AI, where complexity is the norm. But by following these principles—working backwards, starting small, tracking progress, and focusing on iteration—you can consistently deliver impactful projects on time.
The most important lesson? Just ship it. You’ll learn far more by putting your work into the world than by waiting for perfection.