The Five Levels of Machine Learning Projects
Many aspiring machine learning engineers struggle to build projects that lead to job opportunities. Understanding the different levels of ML projects can help you focus your efforts and develop the skills needed for specific roles. This article maps out the five levels of machine learning projects, from beginner to cutting-edge research, providing a clear path for advancement.
Level 1: Beginner - Jupyter Notebook Experiments
Characteristics
This is the starting point for everyone. Projects at this level involve working with clean, structured datasets in Jupyter notebooks, typically on your personal laptop. You'll download pre-cleaned datasets from platforms like Kaggle and utilize libraries like pandas for data manipulation, Matplotlib or Seaborn for visualization, and scikit-learn for basic models.
Typical Project
A common level one project involves loading a CSV file into a DataFrame, performing exploratory data analysis (EDA) with simple visualizations, handling missing values (often by dropping or imputing with the mean), encoding categorical features with one-hot encoding, training a model with default hyperparameters, and evaluating its performance with basic metrics like accuracy. These projects are great for learning but lack the complexities of real-world applications.
Limitations
Kaggle datasets rarely reflect the messy realities of real data. Considerations like data leakage, sophisticated data imputation, and scalability are often overlooked at this stage.
Level 2: Intermediate - Structured Data Science Projects
Characteristics
Level two projects involve working with messier, more realistic data and structuring your projects professionally. The focus shifts from isolated notebooks to well-organized Python projects with separate modules for data processing, feature engineering, model training, and evaluation.
Tools and Workflow
-
Version Control: Using Git for version control is crucial.
-
Reproducibility: You create configuration files for experiment reproducibility.
-
Data Splitting: Employing proper train/validation/test splits, possibly with walk-forward validation for time series.
-
Class Imbalance: Tackling class imbalance with techniques like SMOTE or adjusting class weights.
-
Feature Engineering: Applying modern feature engineering tools and techniques.
-
Advanced Models: Using more complex models, such as LightGBM, simple neural networks, or even AI APIs.
-
Hyperparameter Tuning: Thinking about hyperparameter tuning, possibly using Bayesian search.
-
Pipelines: Creating simple pipelines with tools like Prefect.
Typical Project
An example of a level two project could be a customer churn prediction pipeline using data from various sources like transaction records, support interactions, and usage logs. This involves handling imbalanced classes, performing feature selection, and evaluating the model with precision-recall curves, ROC curves, and business-specific metrics.
Level 3: Machine Learning Engineering - Production Deployment
Characteristics
This level marks the transition to machine learning engineering, where models are deployed to production, serve real users, and impact business outcomes. A new ecosystem of tools and practices is introduced.
Key Technologies and Practices
-
Containerization: Using Docker to package models, ensuring consistency between development and production.
-
API Development: Creating APIs with FastAPI or Flask to serve predictions, possibly using frameworks like BentoML.
-
Load Testing: Performing load testing to ensure the system can handle real traffic.
-
Monitoring: Implementing logging and dashboards with tools like Grafana for crucial monitoring.
-
Versioning: Versioning data and models using tools like DVC, MLflow, and model registries.
Typical Project
A level three project could involve a content recommendation engine for a media platform. The model is packaged in a Docker container and deployed as a microservice. It offers both batch predictions (nightly updates) and real-time API predictions. You track metrics like click-through rates and latency, monitor for feature distribution shifts, and implement shadow deployments or circuit breakers for fault tolerance.
Level 4: Scalable ML Systems - Industrial Scale
Characteristics
Level four focuses on building robust, scalable ML systems that can thrive in complex, ever-changing environments. This involves handling industrial-scale challenges and implementing sophisticated solutions.
Infrastructure and Tooling
-
Cloud Platforms: Utilizing cloud platforms like AWS SageMaker, Google Vertex AI, or Azure ML for deployment and scaling.
-
Orchestration: Orchestrating workflows with Kubernetes and tools like Airflow or Prefect.
-
Deep Learning Frameworks: Using PyTorch or TensorFlow for custom model development.
-
Optimization: Employing optimization methods like quantization, knowledge distillation, and LoRA for efficient fine-tuning.
-
Experiment Tracking: Tracking experiments and optimizing hyperparameters with Weights & Biases or MLflow.
-
RAG: Use of AI models with Retrieval Augmented Generation, prompt tuning and in-context learning along with things like mixture of experts models.
-
Distributed Training: Implementing distributed training across GPU clusters with techniques like pipeline parallelism.
-
Feature Stores: Managing features with feature stores.
-
Automated Retraining: Setting up automated retraining pipelines triggered by data drift.
-
Monitoring & AB Testing: Using comprehensive monitoring and A/B testing frameworks.
Typical Project
An example is developing a real-time fraud detection system for a global financial institution. This involves advanced anomaly detection algorithms, ensemble methods, deployment on a cloud platform, auto-scaling groups, and comprehensive monitoring/alerting.
Level 5: Frontier Research - Cutting Edge AI
Characteristics
Level five is about pushing the boundaries of AI, inventing new approaches, and conducting research. It is not about applying existing solutions but creating them.
Research Areas
-
Custom Architectures: Exploring custom neural network architectures.
-
Self-Supervised Learning: Self-supervised learning systems leveraging vast amounts of unlabeled data.
-
Reinforcement Learning: Novel applications of reinforcement learning.
-
Hybrid Models: Hybrid models combining symbolic reasoning with neural networks.
-
Custom Hardware: Designing custom hardware accelerators.
Typical Project
A project might involve building an autonomous scientific discovery system that uses retrieval augmented generation with neuro-symbolic reasoning to form and test hypotheses in molecular biology. The system uses a large language model for hypothesis generation, symbolic logic for designing experiments, a reinforcement learning agent to simulate outcomes, and collaboration with human researchers.
Project Level Requirements for Different Roles
-
Entry-Level Data Science: Solid Level 2 projects showcasing handling real-world data and applying validation techniques.
-
Mid-Level Data Scientist/Entry-Level ML Engineer: Level 3 projects demonstrating deployment in production environments.
-
Senior Roles/MLOps/ML Platform Engineer: Level 4 projects proving the ability to build and maintain complex ML systems at scale.
-
Research-Focused Roles: Level 5 capabilities, including publications and projects pushing the boundaries of model architecture.
A comprehensive Level 3 project is often more valuable than many hastily implemented Level 1 notebooks. Start where you are comfortable and gradually master the skills to move up the levels.