AI-Powered Autonomous Systems Training Course

This instructor-led live training in Thailand (online or onsite) is designed for biologists who want to understand the workings of AlphaFold and leverage its models to guide their experimental studies.

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

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

This live, instructor-guided training session in Thailand (online or onsite) caters to participants from diverse professional backgrounds who aim to utilize Google’s AutoML platform to construct customized chatbots for a wide range of applications.

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

• Grasp the core principles of chatbot engineering.
• Explore the Google Cloud Platform ecosystem and utilize AutoML.
• Curate and prepare data for training chatbot models.
• Train and assess custom chatbot models using AutoML.
• Integrate and deploy chatbots across multiple platforms and channels.
• Track and refine chatbot performance for sustained effectiveness.

This live, instructor-led training in Thailand (available online or onsite) provides an introductory overview of pattern recognition and machine learning. It explores practical applications within statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

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

• Apply core statistical methods to pattern recognition scenarios.
• Utilize key models, including neural networks and kernel methods, for data analysis.
• Implement advanced techniques to solve complex problems.
• Enhance prediction accuracy by integrating various models.

This instructor-led live training, offered in Thailand (either online or onsite), is tailored for intermediate-level developers, data scientists, and AI practitioners seeking to maximize the potential of TensorFlow Lite in Edge AI scenarios.

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

• Comprehend the essential mechanics of TensorFlow Lite and its significance in Edge AI.
• Build and refine AI models through the use of TensorFlow Lite.
• Deploy TensorFlow Lite models onto a variety of edge devices.
• Leverage specialized tools and methods for model conversion and efficiency improvements.
• Create practical Edge AI implementations using TensorFlow Lite.

This instructor-led live training in Thailand (online or on-site) is designed for developers and data scientists aiming to build, deploy, and manage machine learning workflows on Kubernetes.

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

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

This live, instructor-led training, conducted in Thailand (either online or on-site), targets intermediate data analysts aiming to master the use of RapidMiner for value estimation and projection, and for employing analytical tools in time series forecasting.

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

• Implement the CRISP-DM methodology, select optimal machine learning algorithms, and refine model construction and performance outcomes.
• Utilize RapidMiner to estimate and project values, and apply analytical tools for time series forecasting.

This live, instructor-led training in Thailand (online or onsite) is tailored for advanced professionals aiming to explore state-of-the-art XAI techniques for deep learning, with a specific focus on creating interpretable AI systems.

By the end of the program, participants will have the ability to:

• Grasp the difficulties associated with explainability in deep learning.
• Deploy advanced XAI techniques for neural networks.
• Decipher the decisions made by deep learning models.
• Analyze the trade-offs between high performance and transparency.