Course : Spark AdvancedMachine Learning and industrialization of analytical workflows
Practical course - 3d
- 21h00 - Ref. SPN
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 | Learn advanced data analysis with Spark |
| Performing machine learning (ML) processing with Spark |
| Understanding Docker and its usefulness in industrializing analytical workflows |
| Detailing and implementing the analytical cycle with Spark |
| Learn how to industrialize your analysis workflow |
| Discover MLOps |
Intended audience
Professionals who want to use Spark for batch and real-time analytics.
Prerequisites
Connaissances des API Spark, notamment RDD et DataFrame. Connaissances des algorithmes d’apprentissage supervisés et non supervisés. Maîtrise d’un des langages suivants : Scala, Python.
Practical details
Hands-on work
Alternating theory and practical work. 60% exercises for greater depth. Practical feedback.
Course schedule
1 Introduction
- A reminder of the Spark API.
- Docker concepts and their use in data analysis.
- Docker containers.
Hands-on work
Get to grips with the working environment, create Docker containers.
2 The analytical cycle with Spark
- Ingestion of data.
- Exploration.
- Data preparation.
- Learning.
- Industrialization.
Storyboarding workshops
Presentation of case studies and discussion of the different stages of the cycle.
3 Ingestion of data.
- Data loading.
- Batch processing.
- Streaming treatments.
- Data formats: images, binary, structured, Graph...
Hands-on work
Load data from various sources.
4 Data mining
- Descriptive statistics.
- Identify outliers and empty data.
- Identify invalid values and other anomalies.
Hands-on work
Identify anomalies in a dataset.
5 Preparation and feature engineering (data transformation process)
- Data cleansing.
- Pipelines.
- Transformer les valeurs numériques, catégoriques, binaires et texte.
- Création de nouvelles features.
- Réduction de dimensions.
- Vectorisation.
Hands-on work
Prepare data for analysis.
6 ML lifecycle with MLflow
- Life cycle of a machine learning project.
- Introducing the MLflow open-source platform.
- MLflow's main components: Tracking, Models and Projects.
- Parameters, metrics, tags and artifacts.
Hands-on work
Creating and using a machine learning project.
7 Machine learning
- MLlib, Spark's machine learning library and available algorithms.
- Divide a dataset.
- Configure a template and run it.
- Interpretation and validation of learning outcomes.
- Introduction to Spark Streaming.
Hands-on work
Implementing machine learning.
8 Case studies
- Make recommendations.
- Make sales forecasts.
- Semantic analysis.
- Computer vision with Spark and PyTorch.
- Analyse temps réel avec Spark et Kafka.
Case study
Carry out the various case studies proposed.
Customer reviews
4 / 5
Customer reviews are based on end-of-course evaluations. The score is calculated from all evaluations within the past year. Only reviews with a textual comment are displayed.
EDOH G.
28/09/26
3 / 5
I would have liked to have learnt more about the theory and practice of installing and configuring SPARK clusters (standalone, cloud, etc.). There was a lot of copy-and-paste, but also special characters, which meant we spent more time removing them than actually running the code. The virtual machine was completely impractical – it was buggy, difficult to access, prone to crashes and very limited in terms of computing power. I used my own personal computer to follow the
ADRIEN N.
28/09/26
3 / 5
Several topics were covered, but very briefly. The practicals were very much a case of ‘copy and paste’, with no explanation as to why we were using these settings rather than others, particularly in the ML practical.
PIERRE ARTHUR E.
28/09/26
2 / 5
Theory is virtually non-existent: there is no explanation of concepts or practical application. The practical sessions are interesting in themselves, but the course materials are riddled with errors, typos, sections that are out of order or out of date, and have to be debugged on the spot. We waste precious time copying and pasting from PowerPoint, correcting special characters and struggling with the VM. In terms of content, key concepts are missing, notably Spark configuration, which was actua
PARTICIPANTS
Professionals who want to use Spark for batch and real-time analytics.
PREREQUISITES
Connaissances des API Spark, notamment RDD et DataFrame. Connaissances des algorithmes d’apprentissage supervisés et non supervisés. Maîtrise d’un des langages suivants : Scala, Python.
TRAINER QUALIFICATIONS
The experts leading the training are specialists in the covered subjects. They have been approved by our instructional teams for both their professional knowledge and their teaching ability, for each course they teach. They have at least five to ten years of experience in their field and hold (or have held) decision-making positions in companies.
ASSESSMENT TERMS
The trainer evaluates each participant’s academic progress throughout the training using multiple choice, scenarios, hands-on work and more.
Participants also complete a placement test before and after the course to measure the skills they’ve developed.
TERMS AND DEADLINES
Registration must be completed 24 hours before the start of the training.
ACCESSIBILITY FOR PEOPLE WITH DISABILITIES
Do you need special accessibility accommodations? Contact Mrs. Fosse, Disability Manager, at psh-accueil@orsys.fr to review your request and its feasibility.
Professionals who want to use Spark for batch and real-time analytics.
PREREQUISITES
Connaissances des API Spark, notamment RDD et DataFrame. Connaissances des algorithmes d’apprentissage supervisés et non supervisés. Maîtrise d’un des langages suivants : Scala, Python.
TRAINER QUALIFICATIONS
The experts leading the training are specialists in the covered subjects. They have been approved by our instructional teams for both their professional knowledge and their teaching ability, for each course they teach. They have at least five to ten years of experience in their field and hold (or have held) decision-making positions in companies.
ASSESSMENT TERMS
The trainer evaluates each participant’s academic progress throughout the training using multiple choice, scenarios, hands-on work and more.
Participants also complete a placement test before and after the course to measure the skills they’ve developed.
TEACHING AIDS AND TECHNICAL RESOURCES
• The main teaching aids and instructional methods used in the training are audiovisual aids, documentation and course material, hands-on application exercises and corrected exercises for practical training courses, case studies and coverage of real cases for training seminars.
• At the end of each course or seminar, ORSYS provides participants with a course evaluation questionnaire that is analysed by our instructional teams.
• A check-in sheet for each half-day of attendance is provided at the end of the training, along with a course completion certificate if the trainee attended the entire session.
• The main teaching aids and instructional methods used in the training are audiovisual aids, documentation and course material, hands-on application exercises and corrected exercises for practical training courses, case studies and coverage of real cases for training seminars.
• At the end of each course or seminar, ORSYS provides participants with a course evaluation questionnaire that is analysed by our instructional teams.
• A check-in sheet for each half-day of attendance is provided at the end of the training, along with a course completion certificate if the trainee attended the entire session.
TERMS AND DEADLINES
Registration must be completed 24 hours before the start of the training.
ACCESSIBILITY FOR PEOPLE WITH DISABILITIES
Do you need special accessibility accommodations? Contact Mrs. Fosse, Disability Manager, at psh-accueil@orsys.fr to review your request and its feasibility.
This programme is an original creation, developed by the teaching teams at ORSYS Formation. Any reproduction, representation, adaptation or use, in whole or in part, without the prior written authorisation of ORSYS, is strictly prohibited. ORSYS reserves the right to take any action necessary to protect its intellectual property rights.
Dates and locations
Dernières places
Date garantie en présentiel ou à distance
Session garantie
This programme is an original creation, developed by the teaching teams at ORSYS Formation. Any reproduction, representation, adaptation or use, in whole or in part, without the prior written authorisation of ORSYS, is strictly prohibited. ORSYS reserves the right to take any action necessary to protect its intellectual property rights.