Hands-on Guide to Building Real World ML Systems - Part 1

A practical, step-by-step guide to designing and building machine learning systems that work in real-world settings.

👋 Hey! This is Manisha Arora from PrepVector. Welcome to the Tech Growth Series, a newsletter that aims to bridge the gap between academic knowledge and practical aspects of data science. My goal is to simplify complicated data concepts, share my perspectives on the latest trends, and share my learnings from building and leading data teams.

Why Most ML Projects Fail (And How Holistic Thinking Can Save Yours)

“The model worked perfectly in the notebook… but crashed in production.”
“We spent 6 months training models — and never used them.”
“Our accuracy was great… until the data drifted.”

Sound familiar?

If you’ve worked on machine learning (ML) projects — or even read about them — you’ve likely encountered stories like these. Models that hit 95% accuracy in training but never made it to production. Or worse, models that silently degrade over time and eventually stop adding value.

So, why does this happen?

At its core, the answer is simple but often ignored:

ML projects aren’t just modeling problems. They’re full-system engineering and product problems.

To succeed, we must stop treating ML like an academic exercise and start thinking of it as a product — one that needs to function reliably in the real world.

❌ Why ML Projects Fail

Let’s break down the most common pitfalls that sink ML initiatives:

1. Fuzzy Problem Scoping
   “Let’s predict something” isn’t a strategy. Many projects begin with unclear objectives, undefined success criteria, or no alignment with business needs.
   

2. Weak Data Foundations
   Garbage in, garbage out. Data issues like missing values, label leakage, or inconsistencies can introduce silent but devastating problems downstream.
   

3. Over-Focus on Model Training
   Ironically, the most glamorized part of ML — model training — is often just 10–15% of the overall effort. Yet, it receives the lion’s share of attention.

4. No Deployment or Monitoring Plan
   Even well-trained models are useless if there’s no strategy for how they’ll be served, observed, or retrained in production. Many fail not because the model is wrong, but because there’s no system to support it.
   

5. Lack of Feedback and Learning Strategy
   ML isn’t a “train once, deploy forever” exercise. Models must evolve with the data — which means establishing systems for continual learning.
   

✅ What Makes Successful ML Teams Different?

Successful ML teams think end-to-end.

They don’t just build models — they build systems. They treat ML as a lifecycle, not a one-off task. This means they invest effort across the following stages:

• Problem framing

• Data sourcing and processing

• Feature engineering

• Model training and experimentation

• Evaluation and validation

• Deployment, monitoring, and iteration

They also ensure technical excellence in often-overlooked areas like:

• Feature consistency across training and serving

• Model versioning and reproducibility

• Monitoring for drift, latency, and accuracy degradation

• Building ethical and explainable AI systems
  

This mindset shift — from “build a model” to “build a system” — is what separates real-world ML success from wasted effort.

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🧭 The ML Lifecycle at a Glance

Here’s an overview of the full ML project lifecycle that this series will cover:

Problem Scoping involves clearly defining the business objectives for the machine learning project, establishing measurable success criteria, and framing the problem in a way that is suitable for a machine learning solution.

Data Collection & Labeling focuses on identifying and gathering relevant data from various sources. This section also covers the process of annotating data, whether manually or through weak labeling methods, while ensuring high label quality, managing different versions of the data, and continuously monitoring its overall quality.

Feature Engineering is the process of transforming raw data into features that can be used by machine learning models. This includes extracting meaningful attributes from the data and applying transformations such as encoding categorical variables and scaling numerical features.

Model Training encompasses the selection of appropriate models, ranging from simple baselines to more advanced architectures. Key aspects include tracking experiments to compare model performance and utilizing distributed or cloud-based training environments for efficiency.

Model Evaluation & Validation involves a comprehensive assessment of the trained model. This includes using offline metrics, creating realistic data splits to ensure generalizability, testing for potential biases and fairness issues, assessing the model's robustness and explainability, and conducting real-world or simulated testing such as shadow or online testing.

Deployment & Monitoring covers the methods for putting the trained model into production, such as setting up model APIs and continuous integration/continuous delivery (CI-CD) pipelines. This section also emphasizes the importance of logging model inferences, detecting data and concept drift over time, and creating comprehensive monitoring dashboards to track model performance.

Continual Learning establishes a crucial feedback loop where model performance is continuously evaluated, and an auto-retraining pipeline is implemented to update the model with new data and adapt to changing conditions.

📚 What This Series Covers

This blog series is your hands-on guide to building machine learning projects that actually work in the real world — not just in a Kaggle notebook.

Over 5 parts, we’ll explore:

1. Scoping the right problem

2. Building the right data and feature pipeline

3. Training the right model (with versioning & experiment tracking)

4. Validating it with real-world constraints

5. Deploying, monitoring, and improving it continuously

🧭 Who Should Read This?

• ML Engineers building production pipelines

• Data Scientists looking to productionize their work

• Tech Leads trying to understand why ML projects stall

• Founders & PMs curious about the ML lifecycle and hidden costs

🔜 Coming Up Next

📖 Part 2: From Business Question to ML Problem — Scoping It Right

We'll look at how to avoid the most expensive mistake in ML: solving the wrong problem.

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1. Product Data Science

   Master Product Sense and AB Testing, and learn to use statistical methods to drive product growth. I focus on inculcating a problem-solving mindset, and application of data-driven strategies, including A/B Testing, ML, and Causal Inference, to drive product growth.

2. AI/ML Projects for Data Professionals

   Gain hands-on experience and build a portfolio of industry AI/ML projects. Scope ML Projects, get stakeholder buy-in, and execute the workflow from data exploration to model deployment. You will learn to use coding best practices to solve end-to-end AI/ML Projects to showcase to the employer or clients.

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