Organizations are increasingly looking to integrate machine learning into their processes, but the complexity of environments and the lack of skills on suitable platforms often slow adoption. Without a structured approach, ML projects frequently remain at the experimental stage without ever reaching production. The DP-3014 training course teaches you how to implement end-to-end machine learning solutions with Azure Databricks, from data preparation through model training and evaluation to deployment. That is precisely the purpose of this Azure Databricks machine learning training.
During this intensive one-day course, you learn to leverage Azure Databricks’ machine learning capabilities to build, train and deploy predictive models. You work with MLflow for experiment tracking, Spark MLlib for distributed training and Feature Stores for feature management. Delivered by MCT-certified trainers at ITTA in Geneva and Lausanne, this training uses official Microsoft cloud labs to prepare you for the DP-3014 Applied Skill assessment and to put robust ML solutions into production within your organization.
Module 1: Explore Azure Databricks
Module 2: Use Apache Spark in Azure Databricks
Module 3: Train a machine learning model in Azure Databricks
Module 4: Use MLflow in Azure Databricks
Module 5: Tune hyperparameters in Azure Databricks
Module 6: Use AutoML in Azure Databricks
Module 7: Train deep learning models in Azure Databricks
Module 8: Manage machine learning in production with Azure Databricks
Azure Databricks has become the reference platform for large-scale machine learning within the Azure ecosystem. The combination of Apache Spark for distributed processing, MLflow for experiment tracking, and the Feature Store for variable management creates a comprehensive environment covering the entire ML lifecycle. For Swiss companies, Azure Databricks offers the scalability needed to train models on large data volumes while benefiting from the security and compliance of the Azure cloud. The DP-3014 training enables you to master these tools and move from experimentation to structured, reproducible ML model production.
The training covers the complete machine learning lifecycle in Azure Databricks. The first module focuses on data preparation for ML. You learn to clean, transform, and enrich your datasets with PySpark, handle missing values, and perform the feature engineering needed to build high-performing models. The second module focuses on model training.
You use Spark MLlib to train classification, regression, and clustering algorithms on distributed datasets. The third module introduces MLflow for experiment tracking. You learn to record parameters, metrics, and artifacts from each run to objectively compare your models. The fourth module covers the Databricks Feature Store and centralized management of predictive variables. The final module covers model deployment in production with the Model Registry and serving endpoints. Each module includes practical exercises on official Microsoft cloud labs.
The DP-3014 training is designed for data scientists, ML engineers, and data engineers who want to implement machine learning solutions on Azure Databricks. Prior experience with Python and fundamental machine learning concepts is recommended. You should understand basic concepts such as the difference between classification and regression, overfitting, and training and test sets. Familiarity with Azure Databricks is an advantage but not mandatory. Professionals who have completed the DP-3011 training on data analytics with Azure Databricks are particularly well prepared for this training.
This training prepares you for the Microsoft DP-3014 Applied Skill assessment. The practical exam places you in a real Azure Databricks environment where you must demonstrate your ability to prepare data, train a model, track experiments with MLflow, and deploy a functional ML solution. The assessed tasks correspond to concrete professional scenarios, ensuring that your validation attests to directly applicable skills. The Applied Skill is recorded on your Microsoft Learn profile and strengthens your credibility with employers in the machine learning field.
As a Microsoft Learning Partner, ITTA delivers the DP-3014 training with official MOC educational materials and dedicated Microsoft cloud labs. Our MCT-certified trainers combine machine learning technical expertise with experience on the Azure Databricks platform in professional environments. Sessions take place in Geneva and Lausanne in in-person or virtual classroom format, with small groups that promote interaction and personalized follow-up. You benefit from extended access to practice environments to deepen exercises after the training and consolidate your ML skills on Azure Databricks.
Is Azure Databricks knowledge required for the DP-3014 training?
Familiarity with the Databricks environment is an advantage. If you are new to the platform, the DP-3011 training is an excellent prerequisite to familiarize yourself with the Azure Databricks ecosystem.
What is the difference between DP-3014 and DP-604?
DP-604 covers machine learning in Microsoft Fabric while DP-3014 focuses on Azure Databricks. Both trainings address ML but on different platforms with distinct tools and architectures.
Does the training cover deep learning?
The training focuses on classical machine learning with Spark MLlib and PyTorch with TorchDistributor for distributed training. Dedicated advanced frameworks are covered in specialized trainings.
What is MLflow and why is it important?
MLflow is an open-source platform natively integrated into Azure Databricks that enables experiment tracking, model versioning, and deployment. It is an essential tool for industrializing machine learning projects.
Can the skills acquired be used with cloud platforms other than Azure?
Yes, Databricks is available on Azure, AWS, and Google Cloud. Skills in Spark MLlib, MLflow, and the Feature Store are transferable across different cloud environments.
Is the training available in virtual classroom format?
Yes, ITTA offers the DP-3014 training in person in Geneva and Lausanne as well as in a virtual classroom with full access to cloud labs and the same MCT trainer.
Does this training allow you to deploy models in production?
Yes, the final module specifically covers model deployment with the Model Registry and Databricks serving endpoints, enabling you to move from experimentation to production.
Monday to Friday
8:30 AM to 6:00 PM
Tel. 058 307 73 00
ITTA
Route des jeunes 35
1227 Carouge, Suisse
Monday to Friday, from 8:30 am to 06:00 pm.
