Online or onsite, instructor-led live Predictive AI training courses demonstrate through interactive hands-on practice the fundamentals and advanced topics of Predictive AI.
Predictive AI training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Predictive AI training can be carried out locally on customer premises in Poznan or in NobleProg corporate training centers in Poznan.
This instructor-led, live training in Poznan (online or onsite) is aimed at intermediate-level developers who wish to build AI-powered applications using predictive analytics and generative models.
By the end of this training, participants will be able to:
Understand the fundamentals of predictive AI and generative models.
Utilize AI-powered tools for predictive coding, forecasting, and automation.
Implement LLMs (Large Language Models) and transformers for text and code generation.
Apply time-series forecasting and AI-based recommendations.
Develop and fine-tune AI models for real-world applications.
Evaluate ethical considerations and best practices in AI deployment.
This instructor-led, live training in Poznan (online or onsite) is aimed at beginner-level IT professionals who wish to grasp the fundamentals of Predictive AI.
By the end of this training, participants will be able to:
Understand the core concepts of Predictive AI and its applications.
Collect, clean, and preprocess data for predictive analysis.
Explore and visualize data to uncover insights.
Build basic statistical models to make predictions.
Evaluate the performance of predictive models.
Apply Predictive AI concepts to real-world scenarios.
This instructor-led, live training in Poznan (online or onsite) is aimed at intermediate-level DevOps professionals who wish to integrate predictive AI into their DevOps practices.
By the end of this training, participants will be able to:
Implement predictive analytics models to forecast and solve challenges in the DevOps pipeline.
Utilize AI-driven tools for enhanced monitoring and operations.
Apply machine learning techniques to improve software delivery workflows.
Design AI strategies for proactive issue resolution and optimization.
Navigate the ethical considerations of using AI in DevOps.
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