Training Machine learning with PredictionIO

The training provides advanced knowledge of building recommendation and prediction engines using Apache PredictionIO. Participants will learn how to create, deploy and manage ML engines in a production environment. The program combines theory with intensive hands-on workshops, covering the full lifecycle of an ML project from concept to deployment.The training focuses on building high-performance web applications using the OpenResty platform, which combines the capabilities of the Nginx server with the Lua programming language. During the hands-on workshop, participants will learn about the OpenResty architecture and how to create their own modules and extensions. The classes are conducted in the form of interactive workshops, where theory is immediately translated into practice through the implementation of real use cases.

• ML engineers building recommender systems
• Developers of applications using ML
• Data Scientists implementing production models
• ML systems architects
• Personalization and recommendation specialists
• Backend developers interested in ML
• Web application developers looking for high-performance server solutions
• System administrators interested in optimizing web infrastructure
• DevOps engineers looking to expand their knowledge with high-performance solutions
• Systems architects designing scalable web infrastructure
• Web application performance specialists
• Lua programmers looking to apply their skills in a web context

1. PredictionIO basics
• Platform architecture
• System components
• Preparation of the environment
• Event Server and Engines
2. Construction of prediction engines
• Implementation of algorithms
• Data processing
• Evaluation of models
• Parameter tuning
3. Deployment and scaling
• Deployment of engines
• Infrastructure management
• Performance monitoring
• High load handling
4. Advanced techniques
• Personalization of recommendations
• Real-time processing
• Integration with external systems
• Performance optimization
5. Introduction to OpenResty
• Platform architecture and components
• Nginx integration with Lua
• HTTP request lifecycle
• OpenResty basic configuration
6. Programming in Lua for OpenResty
• Lua syntax and specifics
• OpenResty modules and libraries
• Handling HTTP requests
• Memory and performance management
7. Create modules and extensions
• OpenResty module structure
• Implementation of own directives
• Integration with external services
• Module performance optimization
8. Advanced applications
• Caching and cache
• Load balancing
• Monitoring and debugging
• Application security

The participant will gain the ability to design and implement advanced recommendation systems using PredictionIO. Will learn to create scalable ML engines tailored to specific business requirements. Will learn techniques for optimizing and monitoring the performance of predictive systems. Will be able to deploy and manage ML engines in a production environment. Will gain knowledge of integrating PredictionIO with existing infrastructure. Will develop the ability to create personalized recommendation systems.The participant will be able to independently design and implement high-performance web applications using the OpenResty platform. Will gain the ability to create custom modules to extend the functionality of the Nginx server. Will learn to optimize the performance of web applications by effectively utilizing the capabilities of OpenResty. Will learn techniques for debugging and monitoring applications in a production environment. Will know how to implement advanced caching and load balancing mechanisms. Will gain practical knowledge of web application security.

• Knowledge of the basics of machine learning
• Programming experience in Scala or Java
• Basic knowledge of distributed systems
• Understanding recommendation algorithms
• Knowledge of HTTP protocol basics and web application architecture
• Basic knowledge of web servers, experience with Nginx preferred
• General knowledge of programming (in any language)
• Basic knowledge of Unix/Linux systems

• PredictionIO architecture
• Event Server
• Predictive motors
• Data processing
• Recommendation algorithms
• Evaluation of models
• Deployment of systems
• Performance monitoring
• Scaling up solutions
• Personalization
• Real-time processing
• Performance optimization
• ———-|————- Subcategory | Software Development Training code | IT-SD-460 Duration | 1 day (8 hours) Price net/person | 2650 PLN.
• OpenResty architecture and components
• Lua programming
• Nginx integration with Lua
• Create modules and extensions
• Performance optimization
• Caching and cache
• Load balancing
• Monitoring and debugging
• Application security
• Best implementation practices
• Scalability of the application
• Integration with external services