Building AI Solutions on the Edge Training Course

This course explores the application of AI—specifically Machine Learning and Deep Learning—within the automotive industry. It aims to identify technologies suitable for various in-car scenarios, ranging from basic automation and image recognition to autonomous decision-making.

An examination of the fundamentals of artificial intelligence demonstrates how intelligent technologies are transforming digital strategies, automation, and decision-making processes throughout enterprise operations. This course covers core concepts including the history of AI, frameworks for problem-solving, methods for representing knowledge, reasoning under uncertainty, and various machine learning paradigms, as well as aspects of communication, perception, and autonomous action. It is designed to help executives and architects evaluate opportunities for AI-driven transformation, assess emerging technology trends, and implement practical intelligent solutions to enhance business agility.

This instructor-led, live training in South Korea (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

By the end of this training, participants will be able to:

• Understand the basic principles of AlphaFold.
• Learn how AlphaFold works.
• Learn how to interpret AlphaFold predictions and results.

This course provides programmers and data analysts with the foundational techniques required to build machine learning solutions from the ground up using Python. It covers the core principles of supervised learning (classification and regression), unsupervised learning (clustering and anomaly detection), and advanced neural network architectures. Participants will examine proven methods for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. The program empowers professionals to implement practical ML models, assess algorithm limitations, and complete applied projects aimed at solving real-world problems.

This instructor-led, live training in South Korea (online or on-site) is designed for participants with varying levels of expertise who wish to leverage Google's AutoML platform to build customized chatbots for various applications.

Upon completing this training, participants will be capable of:

• Gaining a solid understanding of chatbot development principles.
• Navigating the Google Cloud Platform to access AutoML.
• Preparing data for training chatbot models.
• Training and evaluating custom chatbot models using AutoML.
• Deploying and integrating chatbots across various platforms and channels.
• Monitoring and optimizing chatbot performance over time.

This instructor-led, live training in South Korea (online or onsite) offers a foundational introduction to the fields of pattern recognition and machine learning. It explores practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

By the end of this training, participants will be able to:

• Apply fundamental statistical methods to pattern recognition.
• Leverage key models such as neural networks and kernel methods for effective data analysis.
• Deploy advanced techniques to solve complex problems.
• Enhance prediction accuracy by integrating various models.

Deep Reinforcement Learning (DRL) integrates the principles of reinforcement learning with deep learning structures, empowering agents to make decisions through environmental interaction. This technology drives numerous modern AI innovations, including autonomous vehicles, robotic control systems, algorithmic trading, and adaptive recommendation engines. DRL enables artificial agents to refine policies, learn strategies, and execute autonomous decisions by leveraging trial-and-error mechanisms and reward-based feedback.

This instructor-led training session, available both online and onsite, is designed for intermediate developers and data scientists looking to master and apply Deep Reinforcement Learning techniques. The goal is to equip participants with the skills necessary to develop intelligent agents capable of making autonomous decisions in complex scenarios.

Upon completing this course, participants will be able to:

• Grasp the theoretical foundations and mathematical core of Reinforcement Learning.
• Code essential RL algorithms, such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents utilizing TensorFlow or PyTorch.
• Deploy DRL solutions for practical use cases, including gaming, robotics, and optimization tasks.
• Utilize contemporary tools to visualize, troubleshoot, and optimize training performance.

Course Format

• Engaging lectures paired with guided discussions.
• Practical exercises and real-world implementation scenarios.
• Live coding sessions and project-based applications.

Customization Options

• For customized course versions (e.g., switching from TensorFlow to PyTorch), please reach out to us to make arrangements.

This instructor-led live training, offered in South Korea (online or onsite), targets intermediate developers, data scientists, and AI practitioners eager to harness TensorFlow Lite for Edge AI solutions.

By the conclusion of this training, participants will be able to:

• Comprehend the fundamentals of TensorFlow Lite and its role in Edge AI.
• Develop and optimize AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on various edge devices.
• Utilize tools and techniques for model conversion and optimization.
• Implement practical Edge AI applications using TensorFlow Lite.

Kubeflow serves as an open-source platform specifically engineered to simplify the construction, training, and deployment of machine learning workloads within a Kubernetes environment.

This instructor-led, live training session (available online or onsite) is tailored for professionals ranging from beginners to those with intermediate experience who aim to establish robust ML workflows leveraging Kubeflow.

By the end of this course, participants will be equipped with the following capabilities:

• To navigate the Kubeflow ecosystem and understand its core components.
• To create reproducible workflows utilizing Kubeflow Pipelines.
• To execute scalable training jobs directly on Kubernetes.
• To efficiently deploy machine learning models using Kubeflow Serving.

Course Format

• Guided presentations complemented by collaborative discussions.
• Hands-on labs featuring real Kubeflow components.
• Practical exercises focused on constructing end-to-end ML workflows.

Customization Options

• Tailored versions of this training can be configured to match your team’s specific technology stack and project needs.

This live, instructor-led training in South Korea, offered either online or on-site, is designed for developers and data scientists seeking to build, deploy, and manage machine learning workflows on Kubernetes.

By the end of this program, participants will be equipped to:

• Install and configure Kubeflow in on-premise and cloud environments.
• Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
• Run complete machine learning pipelines across diverse architectures and cloud setups.
• Use Kubeflow to spawn and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led live training in South Korea (online or onsite) is designed for intermediate-level data analysts who want to learn how to use RapidMiner to estimate and project values, as well as utilize analytical tools for time series forecasting.

By the end of this training, participants will be able to:

• Apply the CRISP-DM methodology, select appropriate machine learning algorithms, and enhance model construction and performance.
• Use RapidMiner to estimate and project values, and utilize analytical tools for time series forecasting.

This instructor-led, live training in South Korea (online or onsite) is aimed at advanced-level professionals who wish to explore state-of-the-art XAI techniques for deep learning models, with a focus on building interpretable AI systems.

By the end of this training, participants will be able to:

• Understand the challenges of explainability in deep learning.
• Implement advanced XAI techniques for neural networks.
• Interpret decisions made by deep learning models.
• Evaluate the trade-offs between performance and transparency.