Azure Databricks Training Course – implementing Machine Learning solutions
Key information
Upcoming sessions
Would you like to schedule this training on a specific date? Contact us by email or via the contact form.
Description of the training: Azure Databricks Training Course – implementing Machine Learning solutions
Azure Databricks is the unified analytics platform built on Apache Spark, co-developed by Databricks and Microsoft. It combines Data Engineering, Data Science, Machine Learning and SQL Analytics in a collaborative Lakehouse environment with Delta Lake and MLflow.
What is the difference between Azure Databricks and Azure Synapse Analytics?Synapse is Microsoft’s integrated data platform (SQL, Spark, pipelines) for enterprise DW and BI scenarios. Databricks targets advanced Data Science/ML workloads with optimised Spark and MLflow. The two can coexist. Our MFE-IT training helps you position Databricks within your data architecture.
How do you deploy an ML model with Azure Databricks and MLflow?Typical workflow: notebook experimentation → MLflow tracking (parameters, metrics, artefacts) → registration in the Model Registry → stage transitions (Staging/Production) → deployment via endpoint (Databricks Serving, Azure ML or container). MFE-IT details the full chain.
How long does it take to learn Azure Databricks for ML?Our Azure Databricks Machine Learning training runs over 1 day (6 hours) in a fully tailored format. It targets data engineers and data scientists wanting to industrialise their ML models on the platform.
{"@context":"https://schema.org","@type":"FAQPage","mainEntity":[{"@type":"Question","name":"What is Azure Databricks?","acceptedAnswer":{"@type":"Answer","text":"Azure Databricks is the unified analytics platform built on Apache Spark, co-developed by Databricks and Microsoft. It combines Data Engineering, Data Science, Machine Learning and SQL Analytics in a collaborative Lakehouse environment with Delta Lake and MLflow."}},{"@type":"Question","name":"What is the difference between Azure Databricks and Azure Synapse Analytics?","acceptedAnswer":{"@type":"Answer","text":"Synapse is Microsoft’s integrated data platform (SQL, Spark, pipelines) for enterprise DW and BI scenarios. Databricks targets advanced Data Science/ML workloads with optimised Spark and MLflow. The two can coexist. Our MFE-IT training helps you position Databricks within your data architecture."}},{"@type":"Question","name":"How do you deploy an ML model with Azure Databricks and MLflow?","acceptedAnswer":{"@type":"Answer","text":"Typical workflow: notebook experimentation → MLflow tracking (parameters, metrics, artefacts) → registration in the Model Registry → stage transitions (Staging/Production) → deployment via endpoint (Databricks Serving, Azure ML or container). MFE-IT details the full chain."}},{"@type":"Question","name":"How long does it take to learn Azure Databricks for ML?","acceptedAnswer":{"@type":"Answer","text":"Our Azure Databricks Machine Learning training runs over 1 day (6 hours) in a fully tailored format. It targets data engineers and data scientists wanting to industrialise their ML models on the platform."}}]} .elementor-accordion .elementor-tab-title .elementor-accordion-icon{margin-right:10px}.elementor-accordion .elementor-tab-title .elementor-accordion-icon + a,.elementor-accordion .elementor-tab-title .elementor-accordion-title{padding-left:4px}Good to know
Our sessions are guaranteed from a single registrant, with no risk of postponement (except in cases of force majeure). A prior interview takes place between the participant and a company representative to fully take into account the participant’s profile (level, needs, professional context, challenges, etc.). Evaluation: during the training, the trainer assesses participants’ learning progress through quizzes, role-plays and practical exercises. Participants receive a certificate of achievement at the end of the training. This training is part of our Cloud Computing training catalogue.
Objectives of the training: Azure Databricks Training Course – implementing Machine Learning solutions
- By the end of the training, the participant will be able to:
- Understand the fundamentals of the Azure Databricks platform for data analysis and Machine Learning.
- Use Apache Spark in Azure Databricks to process, analyse and visualise data at scale.
- Prepare and train high-performing Machine Learning models in a collaborative environment.
- Manage the ML model lifecycle with tools such as MLflow.
- Optimise and automate model training using techniques like hyperparameter tuning and AutoML.
- Explore advanced approaches such as Deep Learning and model production deployment.
Prerequisites of the training: Azure Databricks Training Course – implementing Machine Learning solutions
Command of Python and experience with ML libraries (e.g. Scikit-Learn, PyTorch, TensorFlow). Basic Machine Learning knowledge (building and evaluating models). Initial experience with data workflows or cloud environments is a plus. Because every participant is unique, a personalised interview with our expert lets us design a course perfectly aligned with their goals, level and professional challenges.
Target audience of the training: Azure Databricks Training Course – implementing Machine Learning solutions
Data Scientists, Machine Learning and AI engineers wishing to use Azure Databricks to implement ML solutions at scale. Data analysts and cloud engineers involved in Big Data processing projects. Developers and IT professionals wanting to upskill on ML integration in Azure.
Detailed programme of the training: Azure Databricks Training Course – implementing Machine Learning solutions
Introduction to Azure Databricks
- Overview of the Azure Databricks platform and its use cases. Key concepts, workspace and data governance (Unity Catalog, Purview).
Apache Spark for Machine Learning
- Creating and managing Spark clusters. Running Spark code in notebooks and transforming datasets.
Training Machine Learning models
- Data preparation, feature selection and training predictive models. Model performance evaluation.
ML lifecycle management with MLflow
- Using MLflow to track experiments, register and serve models.
Model optimisation
- Hyperparameter tuning with Hyperopt or similar tools. Analysing and scaling trials to improve performance.
Using AutoML
- Automating model creation via AutoML (UI and API).
Deep Learning in Databricks
- Deep Learning concepts and training models with PyTorch or other frameworks. Distributing training to speed up processes.
Putting ML solutions into production
- Automating data pipelines, deployment strategies, version management and model lifecycle.
Course highlights
- Focuses on implementing Machine Learning solutions with Azure Databricks
- Covers data preparation, model training and MLflow
- Builds on hands-on labs on the Databricks platform
- Prepares you to put ML solutions into production
FAQ
What is Azure Databricks?
Azure Databricks is the unified analytics platform built on Apache Spark, co-developed by Databricks and Microsoft. It combines Data Engineering, Data Science, Machine Learning and SQL Analytics in a collaborative Lakehouse environment with Delta Lake and MLflow.
What is the difference between Azure Databricks and Azure Synapse Analytics?
Synapse is Microsoft’s integrated data platform (SQL, Spark, pipelines) for enterprise DW and BI scenarios. Databricks targets advanced Data Science/ML workloads with optimised Spark and MLflow. The two can coexist. Our MFE-IT training helps you position Databricks within your data architecture.
How do you deploy an ML model with Azure Databricks and MLflow?
Typical workflow: notebook experimentation → MLflow tracking (parameters, metrics, artefacts) → registration in the Model Registry → stage transitions (Staging/Production) → deployment via endpoint (Databricks Serving, Azure ML or container). MFE-IT details the full chain.
How long does it take to learn Azure Databricks for ML?
Our Azure Databricks Machine Learning training runs over 1 day (6 hours) in a fully tailored format. It targets data engineers and data scientists wanting to industrialise their ML models on the platform.