Training Fundamentals of machine learning with Scala and Apache Spark
Practical information about training
- CATEGORY: Technologies
- SUBCATEGORY: AI
- TRAINING CODE: IT-AI-141
- DURATION: 2 days
- PRICE INFORMATION from: 1950 PLN net
- LANGUAGE OF TRAINING: polish
- FORM OF IMPLEMENTATION: stationary, online
Training description
The training introduces participants to the world of big data processing and machine learning using Scala and Apache Spark. The program focuses on the practical use of MLlib, Spark’s machine learning library, to build scalable analytics solutions. The classes are conducted in the form of workshops, where participants work on real data sets, implementing a variety of ML algorithms.
Participant profile
- Scala programmers looking to expand skills with ML
- Data engineers working with Big Data
- Apache Spark Specialists
- Data analysts looking for scalable solutions
- Big Data systems architects
- Developers interested in distributed computing
- Data scientists in need of big data tools
- ML engineers looking for high-performance computing solutions
Agenda
- Introduction to the ecosystem
- Scala basics for ML
- Apache Spark architecture
- MLlib and its capabilities
- Distributed computing in ML
- Data processing
- RDD and DataFrame operations
- Transformations and actions
- Preparation of data for ML
- Processing optimization
- Implementation of algorithms
- Classification models in MLlib
- Regression algorithms
- Clustering and dimensionality reduction
- Evaluation of models
- Implementing solutions
- Pipeline ML at Spark
- Performance tuning
- Monitoring of models
- Integration with infrastructure
Benefits
Upon completion of the training, the participant will be able to effectively use Apache Spark for machine learning tasks. He will gain the ability to implement scalable ML solutions in a distributed environment. He will learn to optimize data processing and model performance in the context of big data. Will be able to design and implement ML pipelines leveraging Scala and Spark capabilities. Will develop practical distributed computing skills for ML tasks. Will gain the ability to integrate ML solutions with existing big data infrastructure.
Required preparation of participants
- Basic knowledge of object-oriented programming
- Experience in working with relational databases
- Basic knowledge of statistics
- Knowledge of the basics of machine learning
Issues
- Apache Spark architecture
- Scala in the context of ML
- Distributed computing
- RDD and DataFrames
- MLlib pipeline
- Distributed algorithms
- Feature engineering at Spark
- Persistence model
- Performance optimization
- Model deployment
- Monitoring and maintenance
- Spark cluster management
Do you have any questions?
Feel free to contact us.
Monika Fengler
+48 532 081 700
monika.fengler@eitt.pl
31 Ząbkowska Street 03-736 Warsaw
Forms of training delivery
Stationary training
- Training at the customer's premises or at a designated location
- Training room equipped with the necessary equipment
- Training materials in electronic form
- Coffee breaks and lunch
- Direct interaction with the trainer
- Networking in a group
- Workshop exercises in teams
Remote training
- Virtual training environment
- Electronic materials
- Interactive online exercises
- Breakout rooms for group work
- Technical support during the training
- Recordings of the session (optional)
Possibility of funding
The training can be financed with public funds under:
- National Training Fund (KFS)
- Development Services Base (BUR)
- EU projects implemented by PARP
- HR Academy Program (PARP)
- Regional operational programs
If you are interested in funding, our team will help you prepare the required documentation.
HAVE A QUESTION?
Contact us for more information about our training, programs and cooperation. We will be happy to answer all your inquiries!
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Do you have any questions?
Feel free to contact us.
Monika Fengler
+48 532 081 700
monika.fengler@eitt.pl
31 Ząbkowska Street 03-736 Warsaw
FAQ - Frequently Asked Questions
- One-pager invitation with deadlines
- Project kick-off
- Strategic leadership and thinking
- Communication and Cooperation. Conflict management
- Motivating, engaging and difficult decisions in business
- Managing Change and Innovation. Leadership in crisis
- Building the organization of the future
- Best practices workshop - retrospective; creating a coherent program for middle and lower management levels























