Program profile · official sources
Machine Learning Engineer
DataCamp offers the Machine Learning Engineer track, an online program designed to prepare aspiring professionals for junior roles in deployment, operations, monitoring, and maintenance. Through interactive courses, you learn to design, train, and deploy end-to-end models using technologies like Python, Docker, and MLflow. The curriculum covers MLOps concepts, CI/CD pipelines, data quality, and model monitoring to address data and concept drift. Access requires a Premium subscription, available for USD 35 per month regular, which provides access to the learning materials but does not guarantee a total completion cost. Upon finishing the program, you earn a Statement of Accomplishment. Prior knowledge of data manipulation and machine learning model training and evaluation using Python is expected.
Commercial status: no commercial relationship is documented for this page. The verification link goes directly to the official provider source.
- Price
- DataCamp Premium — USD 35/month for course content, billed monthly at the regular price. (see provider page)
Premium for Individuals. English public pages with USD explicitly selected; visitor country not stated. Monthly regular access is USD 35 with no monthly promotion shown. Annual regular USD 330 and promotional USD 165 are separate billed totals; no promotion expiry or additional account/geographic restriction is stated. Access price is not a guarantee of track completion. · Official pricing - Duration
- 44 hours
- Format
- Online track with interactive courses, projects and a bonus skill assessment
- Credential
- Statement of Accomplishment
- Prerequisites
- Program description and beginner FAQ expect prior Python data manipulation and machine-learning model training/evaluation. Generic prerequisites panel contradicts this by saying none.
Sources ↗
Course and activity access
Career and Skill tracks: Basic Not included; Premium Included. This program is an official DataCamp Track.. Official source Official source
What’s covered
- Supervised Learning with scikit-learn
- MLOps Concepts
- Introduction to Shell
- Predictive Modeling for Agriculture
- MLOps Deployment and Life Cycling
- Introduction to MLflow
- Predicting Temperature in London
- ETL and ELT in Python
- Introduction to Data Quality with Great Expectations
- Introduction to Data Versioning with DVC
- Monitoring Machine Learning Concepts
- Monitoring Machine Learning in Python
- Introduction to Docker
- CI/CD for Machine Learning
- Machine Learning Engineer
Prerequisites: Program description and beginner FAQ expect prior Python data manipulation and machine-learning model training/evaluation. Generic prerequisites panel contradicts this by saying none.
What are the prerequisites for enrolling in the Machine Learning Engineer track?
While a generic panel on the platform suggests there are no prerequisites, the official program description and the beginner frequently asked questions qualify that claim. Specifically, the program expects learners to have prior knowledge of data manipulation, as well as experience in training and evaluating machine learning models using Python. Because the curriculum dives into advanced topics like model deployment, MLOps, data versioning, and monitoring, you should not treat this as an entry-level course if you lack foundational programming and predictive modeling skills in Python.
How much does it cost to access the Machine Learning Engineer track?
Access to this track requires a DataCamp Premium subscription, as the Basic free plan does not include full access to Career and Skill tracks. A regular individual Premium subscription costs USD 35 per month. Because the program operates on a subscription model, this monthly fee grants you access to the curriculum, but it does not represent a guaranteed total cost to finish the program. The total amount depends on your subscription route and billing periods; annual offers are separate alternatives. DataCamp lists 44 hours for this self-paced track, not a guaranteed elapsed completion time.
Do I receive a professional certification after completing the program?
Upon successfully completing the twelve courses in the track, you will earn a Statement of Accomplishment issued by DataCamp, which you can add to your resume or LinkedIn profile. However, this statement acts as a track completion credential rather than a formal industry certification. While the Premium subscription advertises access to separate industry-leading certifications, completing this specific machine learning engineering track does not automatically grant one. The reviewed track terms leave a separate exam requirement, expiry, and renewal unknown.
What you actually do
- Supervised Learning with scikit-learn — course: Prediction using scikit-learn · Source
- MLOps Concepts — course: Move local models into production · Source
- Introduction to Shell — course: Unix command line and automation · Source
- Predictive Modeling for Agriculture — bonus project: Supervised learning and feature selection · Source
- MLOps Deployment and Life Cycling — course: MLOps framework, lifecycle and deployment · Source
- Introduction to MLflow — course: Tracking, projects, models and model registry · Source
- Predicting Temperature in London — bonus project: Experiment to find the best temperature prediction model · Source
- ETL and ELT in Python — course: Reliable performant data pipelines · Source
- Introduction to Data Quality with Great Expectations — course: Data quality for data science/engineering · Source
- Introduction to Data Versioning with DVC — course: ML data management, pipelines and model evaluation · Source
- Monitoring Machine Learning Concepts — course: Data/concept drift and model degradation · Source
- Monitoring Machine Learning in Python — course: Build a basic monitoring system · Source
- Introduction to Docker — course: Containers and images · Source
- CI/CD for Machine Learning — course: GitHub Actions and Data Version Control · Source
- Machine Learning Engineer — bonus skill assessment · Source
What the credential is
- Credential type
- Statement of Accomplishment · Source
- Credential
- Statement of Accomplishment · Source
- Issuer
- DataCamp · Source
- Sharing
- Add to LinkedIn profile, resume or CV; share on social media and in performance review. · Source
- Credential scope
- Track completion credential; separate industry certification inclusion in Premium does not make this track an exam certification. · Source
Who should skip it
- Absolute beginners with no prior experience in Python data manipulation or machine learning model training and evaluation. · Source
- Learners seeking a formal industry certification exam rather than a Statement of Accomplishment. · Source
- Individuals who prefer purchasing a course for a single flat fee rather than paying a recurring monthly subscription. · Source
Alternatives in the registry
Related by topic or level.
- Databricks Certified Machine Learning Professional — USD 200 (USD 200 for one attempt plus applicable taxes under local law; optional preparation and repeat attempts are separate.); 120 minutes for the exam assessment; not preparation time.
- Databricks Certified Machine Learning Associate — USD 200 (USD 200 for one attempt, normalized from $200 in the official US-hosted guide; taxes, optional preparation and repeat attempts are separate.); 90 minutes for the exam assessment; not preparation time.
- Associate AI Engineer for Data Scientists — DataCamp Premium — USD 35/month for course content, billed monthly at the regular price; 40 hours of listed content.
Change log
- : Initial review of official program, curriculum, credential and subscription-access sources. · Source