AI-Powered Autonomous Systems Training Course

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

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

• Understand the core principles of AlphaFold.
• Learn how the system operates.
• Learn to interpret AlphaFold predictions and outcomes.

This live, instructor-led training, available online or on-site, is tailored for participants with diverse skill levels who intend to use Google's AutoML platform to develop customized chatbots for various applications.

Upon completion of this course, participants will be equipped to:

• Grasp the core principles of chatbot creation.
• Access and navigate the Google Cloud Platform and AutoML services.
• Curate and prepare datasets for training chatbot models.
• Build and assess custom chatbot models using AutoML.
• Deploy and integrate chatbots across multiple platforms and communication channels.
• Track and refine chatbot performance for sustained effectiveness.

This instructor-led, live training in Latvia (delivered 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 conclusion of this training, participants will be able to:

• Apply core statistical methods to pattern recognition.
• Employ key models, including neural networks and kernel methods, for data analysis.
• Implement advanced techniques for complex problem-solving.
• Boost prediction accuracy by integrating various models.

This live, instructor-led training session, available in Latvia format (online or on-site), is designed for intermediate developers, data scientists, and AI professionals aiming to utilize TensorFlow Lite for Edge AI solutions.

By the conclusion of this program, participants will be capable of:

• Comprehending the essentials of TensorFlow Lite and its function in Edge AI.
• Creating and optimizing AI models with TensorFlow Lite.
• Deploying TensorFlow Lite models onto various edge devices.
• Leveraging tools and methods for model conversion and efficiency optimization.
• Implementing real-world Edge AI applications using TensorFlow Lite.

This live, instructor-led session in Latvia, offered online or onsite, is designed for developers and data scientists eager to build, deploy, and manage machine learning workflows on Kubernetes.

By the end of the program, attendees will be capable of:

• Installing and setting up Kubeflow in both on-premise and cloud environments.
• Constructing, deploying, and managing ML workflows that utilize Docker containers and Kubernetes.
• Running full machine learning pipelines across diverse architectures and cloud platforms.
• Using Kubeflow to launch and manage Jupyter notebooks.
• Building ML training, hyperparameter tuning, and serving workloads on multiple platforms.

This instructor-led, live training in Latvia (online or on-site) is designed for intermediate data analysts looking to learn how to use RapidMiner for estimating and projecting values, as well as employing analytical tools for time series forecasting.

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

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

This instructor-led, live training in Latvia (delivered online or onsite) is designed for advanced professionals aiming to explore cutting-edge XAI techniques for deep learning, with a strong focus on building interpretable AI systems.

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

• Grasp the complexities of explainability within deep learning architectures.
• Deploy advanced XAI methods for neural networks.
• Interpret and decode the decisions made by deep learning models.
• Manage the trade-offs between predictive accuracy and transparency.