Become a Machine Learning Engineer
Everything you need to know; What ML engineers do, what tools they use, and exactly how to start building your skills.
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What Does a Machine Learning Engineer Actually Do?
ML Engineers explore data, build predictive models, and uncover insights that guide smarter business decisions.
Deploy and Maintain Models (15 - 25%)
A key difference from Data Scientists, Machine Learning Engineers don’t just build and experiment with models, they put them into production. This means packaging models, automating pipelines, monitoring drift and performance, and retraining when needed.
Working Example: Deploying a fraud detection model into a payment system API, with live performance monitoring and scheduled retraining.
Collaborate with Stakeholders (10 - 15%)
You work with data scientists, product managers, and software teams to ensure models align with business needs and integrate smoothly into applications or platforms. You'll also need to effectively discuss outcomes and correlate them to business action.
Working Example: Working with the product team to scope a ranking feature and coordinating with backend developers for implementation.
Design, Train & Test Models (30 - 40%)
You'll spend a significant chunk of time building and refining predictive models, as well as designing transparent features that will be incorporated. This includes selecting algorithms, training them on data, tuning hyperparameters, and evaluating performance.
Working Example: Using historical customer data to train a churn prediction model, experimenting with random forests and XGBoost to optimise accuracy.
Prepare and Engineer Data (25 - 30%)
Before models can be trained, they need clean, structured input. You will clean, merge, and transform data, you will then need to craft business agreed features that improve model performance, and support the expected outcomes aligned to the business need.
Working Example: Extracting time-based features from user interaction logs and cohorting them to help define optimised contact strategies.
Who Do ML Engineers Work With?
ML Engineers bridge the gap between data science and production systems. They collaborate with technical and business teams to turn models into practical solutions:
Data Scientists - To take experimental models and prepare them for deployment
Data Engineers - To access and process clean, high-volume data for training
Product Managers - To align outputs with user and business needs
Software Engineers - To integrate models into applications, APIs, or backend systems
Foundational Skills
Core Programming
Clean, scalable code is at the heart of every ML pipeline and model. A foundation in Python and object-oriented design allows engineers to move from notebooks to production environments; keeping things structured and readable help in the long run.
Why It Matters?
These are the core skills you’ll need to become job ready, and we've provided some recommended resources to help get you prepared
Model Training
Without proper training and evaluation, even the most advanced models will mislead, and ultimately not get used. A solid understanding of model validation ensures results are robust and not just lucky guesses, fostering greater business trust and alignment.
Where to Start
Why It Matters?
Automate with Python
Where to Start
Hands on ML
Building reusable functions for data preprocessing
Structuring ML codebases for team collaboration
Automating model training via scripts
Integrating APIs into ML models
Debugging performance in deployed models
Real World Use Cases
Pro Tip
Write your code as if someone else will have to interpret and maintain your code tomorrow, even if it’s just you next week.
OOP with Pytthon
Python for Data Science
Real World Use Cases
Pro Tip
Evaluating models for business alignment
Splitting datasets to avoid data leakage
Comparing model versions with confidence
Using grid/random search for parameter tuning
Diagnosing underfitting vs overfitting
Always know, correlate and reconcile to your baseline, don’t celebrate a model that barely beats random and doesn't improve a business.
Intro to Machine Learning
Supervised learning
Data Structures
Optimising your data structure and algorithms reduce memory usage, increase speed, and help with scaling ML systems to production. You don’t need to be a Computer Science graduate, but thinking like one helps to future proof your models.
Why It Matters?
Versioning
If you can’t reproduce your model’s output, you can’t trust it. Having robust version control ensures your code, data, and experiments stay aligned over time. This concept is critical for scaling Machine Learning in real situations.
Why It Matters?
Choosing the right data structure for operations
Reducing training time in large-scale datasets
Improving search and recommendation algorithms
Managing feature stores effectively
Writing efficient preprocessing pipelines
Real World Use Cases
Pro Tip
If your model is slow or memory-intensive, it’s probably a data structure problem, learning to debug lays a strong foundation for success.
Real World Use Cases
Pro Tip
Tracking experiments with MLflow
Versioning datasets using DVC
Comparing model runs and rollbacks
Keeping pipelines aligned with model versions
Creating auditable ML workflows
Treat your Machine Learning experiments like software, version everything. Having a reliable baseline means you always have a trustworthy position.
Where to Start
Grokking Algorithms
Where to Start
Storytelling with Data
Python Data Structures
Data Structures
Machine Learning Mastery
Version Control with Git
Advanced Skills
MLOps
Building a model is only 20% of the job, getting it into production, monitoring it, and updating it is the other 80%. MLOps bridges the gap between experimentation and reliable, scalable deployment; understanding the concepts can speed up the process.
Why It Matters?
These are the aspirationl skills you’ll need to excel as a Data Scientist
Distributed Computing
Real-world Machine Lerning often requires processing millions of records. Tools like Spark or Dask allow you to scale pipelines beyond what fits in memory; a must for handling production-grade data. You gain redundancy by leveraging cloud computation.
Where to Start
Why It Matters?
Practical MLOps
Where to Start
Designing Data Apps
Deploying models as REST APIs using Flask
Automating retraining with CI/CD pipelines
Using MLflow for tracking and model registry
Monitoring drift and performance in production
Scaling models with Docker & Kubernetes
Real World Use Cases
Pro Tip
Start small, even a simple CI/CD pipeline can save hours of manual deployment time down the road, enabling outcomes to drive business change.
Deploy with FastAPI
Real World Use Cases
Pro Tip
Preprocessing massive datasets with PySpark
Running distributed model training jobs
Handling streaming data with Kafka Streaming
Reducing batch job runtimes for faster results
Integrating data lakes into training pipelines
Don’t scale just to scale, focus on optimising your code locally first. This will give you the insight necessary to scale successfully and effectively.
Big Data Fundamentals
Deep Learning
Deep learning powers cutting-edge AI across vision, NLP, and beyond. Understanding architectures like CNNs, RNNs, and Transformers gives you the edge to solve more complex, high-value problems. This can add real value for businesses.
Why It Matters?
Responsible AI
As AI becomes more embedded in decisions, trust and transparency are non-negotiable. Being able to explain source references, model structure or logic decisions ensures your models can be audited, defended, and improved when tested.
Why It Matters?
Building computer vision pipelines with CNNs
Sentiment analysis using Transformers like BERT
Time-series forecasting with LSTM/RNNs
Creating predictive engines with neural networks
Fine-tuning pre-trained models for rapid results
Real World Use Cases
Pro Tip
You don't always need to build from scratch, fine-tuning or repurposing trusted models can help deliver outcomes at scale and speed.
Real World Use Cases
Pro Tip
Visualising feature importance with SHAP
Detecting bias in training data
Explaining model outputs to stakeholders
Documenting fairness metrics
Ensuring compliance in regulated industries
If a stakeholder doesn’t trust your model, it won’t get used. Complex models that can explained simply will build more trust, and ultimately get used.
Where to Start
Deep Learning with Python
Where to Start
Data Ethics
Deep Learning
Responsible AI
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