You can train a model. Now make it survive production.
Machine Learning and Data Science, GuildTrek’s deep track for engineers who want to model real data and put it into production. Six to nine months, statistics-first, MLOps all the way to the end. Cohorts are forming now; join the early-access list and pick yours.
Cohort forming · limited seats
Fees shared on enquiry · nothing due to apply
What you walk away with
32
Weeks, foundations to MLOps
3
Levels of depth
15+
Models you train and tune
100%
Real datasets, no toy sets
1
Deployed, monitored ML system
Sound familiar?
If any of these is you, keep reading.
If any of these sound like you, this deep track was built for exactly your gap.
“Your models work in the notebook and nowhere else.”
You build the MLOps muscle that gets them served and monitored.
“You call fit and predict, but the statistics are a black box.”
You learn the probability and inference underneath, from scratch.
“Your accuracy looked great, then collapsed on real data.”
You learn to spot leakage, imbalance, and drift before they burn you.
“You have done a few tutorials but never shipped a model.”
Every model here is trained, tuned, deployed, and watched.
“You want data science but do not know what to actually master.”
One deep path: statistics, then modeling, then production, in order.
Why this is different
Plenty of data-science courses stop at the notebook. This one does not.
The difference is depth: the statistics under every model, and the MLOps that gets it into production.
Another data-science course
GuildTrek
A 40-hour video course you half-finish
Models you train and defend on real datasets
fit, predict, done
The statistics and evaluation under every model
Toy datasets that always behave
Messy real data, with leakage and drift to fight
A notebook that dies on your laptop
A deployed, monitored system with drift detection
A PDF certificate
A public GitHub portfolio of models that ship
In 6–9 months, you go from
- "I have done a few ML courses"
- "I can run scikit-learn"
- "I finished a Kaggle tutorial"
“I trained, tuned, evaluated, deployed, and monitored real models on real data, and I can prove it.”
Proof, not a PDF
Models you train, tune, and deploy on real data.
Every model here is trained and tuned on real datasets, then evaluated honestly and put into production, not left to die in a notebook.
The curriculum
What you'll cover.
A structured, level-by-level path. The full topic-by-topic detail comes on enrolment.
1Level 1 · Foundations
Weeks 1–10- Python data stack (NumPy, Pandas, visualization)
- Statistics & probability — deeper than most intros
- Data wrangling & exploratory analysis
- The ML mindset & end-to-end lifecycle
2Level 2 · Applied ML & Modeling
Weeks 11–22- Supervised & unsupervised learning in depth
- XGBoost / LightGBM / CatBoost, SVM, clustering, PCA
- Model evaluation, selection & hyperparameter tuning
- Feature engineering, time-series & intro deep learning
3Level 3 · Production ML & MLOps
Weeks 23–32- Deployment, serving & CI/CD for ML
- Monitoring, drift & feature stores
- Explainability (SHAP), fairness & governance
- Cloud ML & scalable pipelines
4Optional · Industry Certification Prep
- Aligned to Azure DP-100, AWS ML, Google Cloud & Databricks
Where it leads
What you'll be ready for.
Cohort forming · limited seats
Stop half-finishing tutorials. Start shipping models.
Join the early-access list for the next Machine Learning and Data Science cohort and pick the batch that fits you.