Machine Learning Training in Hanoi

This instructor-led, live training in Hanoi (online or onsite) is aimed at beginner-level professionals who wish to understand the concept of pre-trained models and learn how to apply them to solve real-world problems without building models from scratch.

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

• Understand the concept and benefits of pre-trained models.
• Explore various pre-trained model architectures and their use cases.
• Fine-tune a pre-trained model for specific tasks.
• Implement pre-trained models in simple machine learning projects.

This instructor-led live training in Hanoi (online or onsite) is designed for participants of varying expertise levels who aim to leverage Google's AutoML platform to build customized chatbots for various applications.

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

• Understand the fundamentals of chatbot development.
• Navigate the Google Cloud Platform and access AutoML.
• Prepare data for training chatbot models.
• Train and evaluate custom chatbot models using AutoML.
• Deploy and integrate chatbots into various platforms and channels.
• Monitor and optimize chatbot performance over time.

This instructor-led, live training in Hanoi (online or onsite) is aimed at intermediate-level AI developers, machine learning engineers, and system architects who wish to optimize AI models for edge deployment.

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

• Understand the challenges and requirements of deploying AI models on edge devices.
• Apply model compression techniques to reduce the size and complexity of AI models.
• Utilize quantization methods to enhance model efficiency on edge hardware.
• Implement pruning and other optimization techniques to improve model performance.
• Deploy optimized AI models on various edge devices.

This live, instructor-led training in Hanoi (online or onsite) is designed for intermediate developers, data scientists, and technology enthusiasts aiming to acquire practical skills in deploying AI models on edge devices for diverse applications.

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

• Understand the fundamental principles and benefits of Edge AI.
• Set up and configure the edge computing environment.
• Develop, train, and optimize AI models for edge deployment.
• Implement practical AI solutions on edge devices.
• Evaluate and improve the performance of edge-deployed models.
• Address ethical and security considerations in Edge AI applications.

This instructor-led, live training (online or onsite) targets advanced AI engineers and data scientists with intermediate to advanced skills who aim to improve DeepSeek model performance, minimize latency, and efficiently deploy AI solutions using modern MLOps practices.

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

• Optimize DeepSeek models for better efficiency, accuracy, and scalability.
• Adopt best practices for MLOps and model versioning.
• Deploy DeepSeek models on cloud and on-premise infrastructure.
• Monitor, maintain, and scale AI solutions effectively.

MLOps on Kubernetes provides a framework for automating the training, validation, packaging, and deployment of machine learning models through containerized pipelines and GitOps workflows.

This instructor-led live training (available online or onsite) is designed for intermediate-level practitioners looking to build automated, scalable MLOps pipelines on Kubernetes.

Upon completing this training, participants will be able to:

• Design end-to-end CI/CD pipelines tailored for machine learning.
• Implement GitOps workflows for model deployment and versioning.
• Automate the training, testing, and packaging of ML models.
• Integrate monitoring, alerting, and rollback strategies.

Course Format

• Instructor-guided presentations and technical deep dives.
• Hands-on exercises designed to build real-world CI/CD workflows.
• Live-lab practice for deploying ML workloads to Kubernetes.

Course Customization Options

• Organizations may request tailored content aligned with their internal MLOps tools and infrastructure.

Kubeflow is an open-source platform designed to streamline building, training, and deploying machine learning workloads on Kubernetes.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to build reliable ML workflows using Kubeflow.

Upon completion of this training, attendees will gain the skills to:

• Navigate the Kubeflow ecosystem and core components.
• Build reproducible workflows with Kubeflow Pipelines.
• Run scalable training jobs on Kubernetes.
• Serve machine learning models efficiently using Kubeflow Serving.

Format of the Course

• Guided presentations and collaborative discussions.
• Hands-on labs with real Kubeflow components.
• Practical exercises to build end-to-end ML workflows.

Course Customization Options

• Customized versions of this training can be arranged to align with your team’s technology stack and project requirements.

TinyML involves deploying highly optimized machine learning models onto edge devices with limited resources.

This guided, live training session—available online or in-person—is designed for experienced technical professionals looking to design, optimize, and implement complete TinyML workflows.

Upon completion of this course, participants will gain the ability to:

• Gather, preprocess, and manage datasets specifically for TinyML use cases.
• Train and refine models to operate efficiently on low-power microcontrollers.
• Transform models into lightweight formats compatible with edge devices.
• Deploy, test, and monitor TinyML applications across actual hardware setups.

Course Format

• Instructor-led lectures paired with technical discussions.
• Practical laboratory sessions and iterative experimentation.
• Hands-on deployment exercises using microcontroller-based platforms.

Customization Options

• To tailor the training to your specific toolchains, hardware boards, or internal workflows, please reach out to us for arrangements.

This instructor-led live training Hanoi (available online or on-site) is tailored for intermediate-level developers, data scientists, and AI professionals aiming to utilize TensorFlow Lite for Edge AI initiatives.

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

• Comprehend the foundational aspects of TensorFlow Lite and its function in Edge AI.
• Build and optimize AI models utilizing TensorFlow Lite.
• Deploy TensorFlow Lite models onto various edge devices.
• Apply tools and techniques for model conversion and optimization.
• Create functional Edge AI applications using TensorFlow Lite.

This instructor-led live training in Hanoi (online or onsite) is aimed at advanced-level professionals who wish to master the technologies behind autonomous systems.

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

• Design and implement AI models for autonomous decision-making.
• Develop control algorithms for autonomous navigation and obstacle avoidance.
• Ensure safety and reliability in AI-powered autonomous systems.
• Integrate autonomous systems with existing robotics and AI frameworks.

This instructor-led, live training in Hanoi (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

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

• Construct and train convolutional neural networks (CNNs) utilizing TensorFlow.
• Utilize Google Colab for scalable and efficient cloud-based model development.
• Apply image preprocessing techniques for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Employ transfer learning to enhance the performance of CNN models.
• Visualize and interpret the results of image classification models.

TinyML refers to the deployment of machine learning models on low-power, resource-constrained devices operating at the network edge.

This instructor-led, live training (available online or onsite) is designed for advanced-level professionals who want to secure TinyML pipelines and implement privacy-preserving techniques in edge AI applications.

Upon completing this course, participants will be able to:

• Identify security risks specific to on-device TinyML inference.
• Implement privacy-preserving mechanisms for edge AI deployments.
• Harden TinyML models and embedded systems against adversarial threats.
• Apply best practices for secure data handling in constrained environments.

Format of the Course

• Engaging lectures supported by expert-led discussions.
• Practical exercises emphasizing real-world threat scenarios.
• Hands-on implementation using embedded security and TinyML tooling.

Course Customization Options

• Organizations may request a tailored version of this training to align with their specific security and compliance needs.

This instructor-led, live training in Hanoi (online or on-site) is designed for advanced professionals aiming to deepen their knowledge of machine learning models, sharpen their hyperparameter tuning skills, and learn how to effectively deploy models using Google Colab.

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

• Implement advanced machine learning models using popular frameworks like Scikit-learn and TensorFlow.
• Optimize model performance through hyperparameter tuning.
• Deploy machine learning models in real-world applications using Google Colab.
• Collaborate and manage large-scale machine learning projects in Google Colab.

This instructor-led, live training in Hanoi (online or onsite) is designed for intermediate-level professionals who want to apply AI techniques to optimize yield management in semiconductor manufacturing.

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

• Analyze production data to identify factors affecting yield rates.
• Implement AI algorithms to enhance yield management processes.
• Optimize production parameters to reduce defects and improve yields.
• Integrate AI-driven yield management into existing production workflows.

This instructor-led, live training in Hanoi (online or onsite) is intended for intermediate-level business and AI professionals who want to use machine learning in business, forecasting, and AI-driven systems through real case studies and Python-based tools.

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

• Grasp how machine learning aligns with AI and business strategy.
• Apply supervised and unsupervised learning methods to structured business problems.
• Preprocess and transform data for modeling purposes.
• Leverage neural networks for classification and prediction tasks.
• Conduct sales forecasting using statistical and ML-based approaches.
• Implement clustering and association rule mining for customer segmentation and pattern discovery.

This instructor-led live training in Hanoi (offered online or onsite) is designed for intermediate professionals eager to utilize AI-driven predictive maintenance methods in semiconductor manufacturing. The objective is to improve production efficiency and prevent unexpected equipment failures.

By the end of this course, attendees will be able to:

• Apply AI models to predict equipment failures in semiconductor manufacturing contexts.
• Analyze maintenance records to uncover patterns and trends that indicate potential problems.
• Incorporate AI-powered predictive maintenance into existing production workflows.
• Decrease downtime and maintenance costs via proactive equipment management.

This instructor-led, live training in Hanoi (online or onsite) is designed for advanced-level professionals who aim to apply state-of-the-art AI techniques to semiconductor design automation, thereby improving efficiency, accuracy, and innovation in chip design and verification.

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

• Apply advanced AI techniques to optimize semiconductor design processes.
• Integrate machine learning models into EDA tools for enhanced design verification.
• Develop AI-driven solutions for complex design challenges in chip fabrication.
• Leverage neural networks for improving the accuracy and speed of design automation.

This instructor-led, live training in Hanoi (online or onsite) is aimed at intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.

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

• Set up and navigate Google Colab for deep learning projects.
• Understand the fundamentals of neural networks.
• Implement deep learning models using TensorFlow.
• Train and evaluate deep learning models.
• Utilize advanced features of TensorFlow for deep learning.

This instructor-led live training Hanoi (online or onsite) is designed for intermediate professionals seeking to understand and apply AI techniques for optimizing semiconductor fabrication processes.

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

• Grasp AI methodologies tailored for process optimization in chip production.
• Deploy AI models to improve yield and minimize defect rates.
• Examine process data to pinpoint critical parameters for optimization.
• Utilize machine learning techniques to fine-tune semiconductor manufacturing procedures.

This instructor-led, live training in Hanoi (online or onsite) is aimed at intermediate-level participants who wish to automate and manage machine learning workflows, including model training, validation, and deployment using Apache Airflow.

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

• Set up Apache Airflow for machine learning workflow orchestration.
• Automate data preprocessing, model training, and validation tasks.
• Integrate Airflow with machine learning frameworks and tools.
• Deploy machine learning models using automated pipelines.
• Monitor and optimize machine learning workflows in production.

This instructor-led, live training in Hanoi (online or onsite) is designed for intermediate-level data scientists and developers who wish to efficiently apply machine learning algorithms using the Google Colab environment.

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

• Set up and navigate Google Colab for machine learning projects.
• Understand and apply various machine learning algorithms.
• Use libraries like Scikit-learn to analyze and predict data.
• Implement supervised and unsupervised learning models.
• Optimize and evaluate machine learning models effectively.

TinyML involves the deployment of machine learning models onto hardware with extremely limited resources.

This instructor-led live training, available both online and onsite, is designed for advanced practitioners seeking to optimize TinyML models for low-latency, memory-efficient deployments on embedded devices.

Upon completing this training, participants will be able to:

• Utilize quantization, pruning, and compression techniques to minimize model size while maintaining accuracy.
• Benchmark TinyML models to assess latency, memory usage, and energy efficiency.
• Implement optimized inference pipelines on microcontrollers and edge devices.
• Evaluate the trade-offs between performance, accuracy, and hardware limitations.

Course Format

• Instructor-led presentations complemented by technical demonstrations.
• Practical exercises in model optimization and comparative performance testing.
• Hands-on implementation of TinyML pipelines within a controlled lab setting.

Customization Options

• For specialized training tailored to specific hardware platforms or internal workflows, please contact us to customize the program.

This instructor-led, live training in Hanoi (online or onsite) is tailored for advanced professionals seeking to explore state-of-the-art XAI techniques for deep learning, focusing on the construction of interpretable AI systems.

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

• Grasp the challenges associated with explainability in deep learning.
• Apply advanced XAI techniques to neural networks.
• Interpret the decisions generated by deep learning models.
• Analyze the trade-offs between model performance and transparency.

This instructor-led live training in Hanoi (online or onsite) is designed for beginner-level professionals who wish to understand and apply AI technologies within the semiconductor manufacturing industry.

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

• Understand the basic principles of AI and how they apply to semiconductor manufacturing.
• Identify areas within semiconductor manufacturing where AI can be effectively implemented.
• Utilize AI tools and techniques to enhance production efficiency and quality control.
• Implement basic AI models to optimize manufacturing processes.

Docker serves as a containerization platform designed to create reproducible, portable, and scalable environments for machine learning systems.

This instructor-led live training (available online or onsite) targets intermediate to advanced technical professionals seeking to containerize and operationalize complete ML pipelines using Docker.

After completing this training, participants will be able to:

• Containerize machine learning workloads for training, validation, and inference.
• Design and orchestrate end-to-end ML pipelines utilizing Docker and complementary tools.
• Implement versioning, reproducibility, and CI/CD processes for ML components.
• Deploy, monitor, and scale ML services within containerized environments.

Course Format

• Interactive lectures accompanied by practical demonstrations.
• Hands-on exercises centered on constructing real-world ML pipeline components.
• Live-lab implementation focused on end-to-end containerized workflows.

Customization Options

• For tailored training aligned with specific ML infrastructure requirements, please contact us to explore available options.

This instructor-led, live training conducted in Hanoi (online or on-site) targets data scientists and developers aiming to utilize ML.NET models to automatically extract predictive insights from data analysis for enterprise solutions.

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

• Install ML.NET and incorporate it into their development environment.
• Comprehend the machine learning methodologies behind ML.NET's tools and algorithms.
• Build and train models to perform smart predictions using available data.
• Evaluate model effectiveness using ML.NET-specific performance metrics.
• Improve the accuracy of current models based on the ML.NET framework.
• Apply ML.NET machine learning concepts across various other data science initiatives.

This instructor-led, live training in Hanoi (online or onsite) is aimed at intermediate-level data professionals who wish to apply machine learning techniques to data-driven business problems, including sales forecasting and predictive modeling using neural networks.

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

• Grasp the fundamental concepts and categories of machine learning.
• Utilize essential algorithms for classification, regression, clustering, and association analysis.
• Conduct exploratory data analysis and prepare data using Python.
• Apply neural networks to handle nonlinear modeling tasks.
• Deploy predictive analytics for business forecasting, particularly with sales data.
• Assess and enhance model performance through visual and statistical methods.

This instructor-led, live training in Hanoi (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation.

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

• Understand advanced deep learning architectures and techniques for text-to-image generation.
• Implement complex models and optimizations for high-quality image synthesis.
• Optimize performance and scalability for large datasets and complex models.
• Tune hyperparameters for better model performance and generalization.
• Integrate Stable Diffusion with other deep learning frameworks and tools

This instructor-led, live training is available both online and onsite, targeting intermediate to advanced cybersecurity professionals who wish to enhance their skills in AI-driven threat detection and incident response.

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

• Implement advanced AI algorithms for real-time threat detection.
• Customize AI models for specific cybersecurity challenges.
• Develop automation workflows for threat response.
• Secure AI-driven security tools against adversarial attacks.

This instructor-led, live training in Hanoi (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers looking to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

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

• Understand the fundamental principles of TinyML and its benefits for edge AI applications.
• Establish a development environment suitable for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Apply TensorFlow Lite and Edge Impulse to build real-world TinyML solutions.
• Optimize AI models for power efficiency and memory constraints.

This instructor-led, live training in Hanoi (online or onsite) is designed for entry-level cybersecurity professionals seeking to leverage AI to enhance their threat detection and response capabilities.

Upon completing this training, participants will be able to:

• Grasp the application of AI in cybersecurity.
• Deploy AI algorithms for identifying threats.
• Automate incident response using AI tools.
• Incorporate AI into existing cybersecurity infrastructure.

This instructor-led, live training in Hanoi (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 instructor-led, live training in Hanoi (online or onsite) is aimed at intermediate-level data analysts who wish to learn how to use RapidMiner to estimate and project values and utilize analytical tools for time series forecasting.

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

• Learn 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 (online or onsite) is targeted at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for various use cases.

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

• Understand the principles of Stable Diffusion and how it functions for image generation.
• Build and train Stable Diffusion models for image generation tasks.
• Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
• Optimize the performance and stability of Stable Diffusion models.

This instructor-led, live training in Hanoi (available online or onsite) targets beginner-level engineers and data scientists seeking to understand TinyML fundamentals, explore its applications, and deploy AI models on microcontrollers.

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

• Understand the fundamentals of TinyML and its significance.
• Deploy lightweight AI models on microcontrollers and edge devices.
• Optimize and fine-tune machine learning models for low-power consumption.
• Apply TinyML for real-world applications such as gesture recognition, anomaly detection, and audio processing.

This course is designed to equip participants with practical proficiency in applying Machine Learning techniques. Utilizing the Python programming language and its extensive ecosystem of libraries, alongside numerous hands-on examples, the curriculum covers essential Machine Learning components. Participants will learn how to make informed decisions about data modeling, interpret algorithmic outputs, and validate results effectively.

Our objective is to empower you with the confidence to master fundamental Machine Learning tools while steering clear of common pitfalls associated with Data Science applications.

This course empowers developers and data analysts with the essential techniques required to construct machine learning solutions from the ground up using Python. It explores core concepts in supervised learning, including classification and regression, as well as unsupervised methods such as clustering and anomaly detection, alongside advanced neural network structures. Participants will study effective strategies for leveraging scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. The curriculum supports professionals in deploying functional ML models, assessing algorithmic constraints, and executing applied projects designed to address real-world challenges.

Deep Reinforcement Learning (DRL) merges the principles of reinforcement learning with deep learning architectures, empowering agents to make decisions through environmental interaction. This technology drives significant AI advancements, including autonomous vehicles, robotics control, algorithmic trading, and adaptive recommendation systems. DRL enables artificial agents to learn strategies, optimize policies, and make independent decisions via trial-and-error processes grounded in reward-based learning.

This instructor-led live training (available online or onsite) targets intermediate developers and data scientists seeking to master and apply Deep Reinforcement Learning techniques for constructing intelligent agents capable of autonomous decision-making in complex settings.

Upon completing this training, participants will be able to:

• Comprehend the theoretical foundations and mathematical principles underpinning Reinforcement Learning.
• Develop core RL algorithms, such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents utilizing TensorFlow or PyTorch.
• Apply DRL to practical scenarios like gaming, robotics, and decision optimization.
• Diagnose issues, visualize outcomes, and optimize training performance using contemporary tools.

Course Format

• Interactive lectures combined with guided discussions.
• Practical exercises and real-world implementations.
• Live coding demonstrations and project-based applications.

Customization Options

• To request a customized version of this course (for instance, focusing on PyTorch instead of TensorFlow), please contact us to arrange.

Delving into the basics of artificial intelligence demonstrates how smart technology is transforming digital strategy, automation, and decision-making processes throughout enterprise operations. This course covers fundamental concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning approaches, alongside communication, perception, and autonomous actions. It enables executives and architects to assess opportunities for AI-driven transformation, analyze emerging technology trends, and implement practical intelligent solutions to enhance business agility.

This program explores the application of AI, with a specific focus on Machine Learning and Deep Learning, within the automotive industry. It guides participants in identifying technologies suitable for various scenarios inside a vehicle, ranging from basic automation and image recognition to complex autonomous decision-making processes.

This 8-day programme takes participants on a comprehensive journey, building strong foundations in Python engineering and advancing into sophisticated AI system design. Attendees will cultivate disciplined coding habits, gain mastery over statistical and deep learning techniques, and construct generative AI and agent-based systems ready for production. The curriculum emphasizes reliability, evaluation methodologies, safety protocols, and real-world deployment strategies, prioritizing robust implementation over mere experimentation.

An Artificial Neural Network is a computational data model utilized in the development of Artificial Intelligence (AI) systems capable of executing 'intelligent' tasks. Neural Networks are frequently employed in Machine Learning (ML) applications, which represent one specific implementation of AI. Deep Learning constitutes a subset of Machine Learning.

Enhance your data science proficiency through this extensive Machine Learning training program, which explores core algorithms such as Naive Bayes, Decision Trees, Neural Networks, Support Vector Machines, and Clustering techniques. Acquire practical experience grounded in theoretical principles and real-world applications. This course is ideal for data analysts, software engineers, AI enthusiasts, and business professionals looking to implement machine learning solutions. Develop expertise in classification performance metrics, cross-validation, the bias-variance trade-off, and deep learning fundamentals to construct robust predictive models.

This instructor-led, live training in Hanoi (online or onsite) provides an introduction into the field of pattern recognition and machine learning. It touches on 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 core statistical methods to pattern recognition.
• Use key models like neural networks and kernel methods for data analysis.
• Implement advanced techniques for complex problem-solving.
• Improve prediction accuracy by combining different models.

This instructor-led, live training in Hanoi (online or onsite) targets data scientists who aim to utilize TensorFlow for analyzing potential fraud data.

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

• Create a fraud detection model using Python and TensorFlow.
• Build linear regressions and linear regression models to predict fraud.
• Develop an end-to-end AI application for analyzing fraud data.

Machine learning represents a subset of Artificial Intelligence where computers can acquire knowledge without explicit programming.

Deep learning is a specialized area within machine learning that relies on methods such as neural networks to learn data representations and structures.

Python is a high-level programming language renowned for its clean syntax and code readability.

In this instructor-led live training, participants will learn how to implement deep learning models for the telecom sector by building a deep learning credit risk model using Python.

Upon completing this training, participants will be able to:

• Grasp the core concepts of deep learning.
• Explore the applications and use cases of deep learning within telecommunications.
• Utilize Python, Keras, and TensorFlow to develop deep learning models for telecom applications.
• Construct their own customer churn prediction model using Python.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live laboratory environment.

Customization Options

• To request customized training for this course, please contact us to make arrangements.

This hands-on, instructor-led program serves as a seamless follow-up to the Python for Data Analysis course.

It provides an overview of fundamental Machine Learning concepts and demonstrates their practical application in data analysis tasks like prediction, classification, and segmentation.

The curriculum emphasizes understanding the practical workings of Machine Learning through familiar tools such as Python, Pandas, and Jupyter Notebook, eliminating the need for advanced mathematical expertise.

This instructor-led live training in Hanoi (online or onsite) is designed for engineers seeking to evaluate current approaches and tools, enabling them to make intelligent decisions about adopting MLOps within their organizations.

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

• Install and configure various MLOps frameworks and tools.
• Assemble a team with the appropriate skills for constructing and supporting an MLOps system.
• Prepare, validate, and version data for use by ML models.
• Understand the components of an ML Pipeline and the tools needed to build one.
• Experiment with different machine learning frameworks and servers for deploying to production.
• Operationalize the entire Machine Learning process so that it's reproduceable and maintainable.

This instructor-led, live training in Hanoi (online or onsite) is aimed at intermediate-level data analysts, developers, or aspiring data scientists who wish to apply machine learning techniques in Python to extract insights, make predictions, and automate data-driven decisions.

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

• Understand and differentiate key machine learning paradigms.
• Explore data preprocessing techniques and model evaluation metrics.
• Apply machine learning algorithms to solve real-world data problems.
• Use Python libraries and Jupyter notebooks for hands-on development.
• Build models for prediction, classification, recommendation, and clustering.

This instructor-led live training in Hanoi (online or onsite) targets developers and data scientists who want to utilize Tensorflow 2.x to build predictors, classifiers, generative models, neural networks, and similar applications.

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

• Install and configure TensorFlow 2.x.
• Understand the benefits of TensorFlow 2.x over previous versions.
• Build deep learning models.
• Implement an advanced image classifier.
• Deploy a deep learning model to the cloud, mobile and IoT devices.

This course provides a strong conceptual foundation in neural networks, machine learning algorithms, and the principles and applications of deep learning.

Part 1 (40% of the training) emphasizes foundational concepts, helping you select the appropriate technology stack, such as TensorFlow, Caffe, Theano, DeepDrive, or Keras.

Part 2 (20% of the training) introduces Theano, a Python library designed to simplify the creation of deep learning models.

Part 3 (40% of the training) focuses extensively on TensorFlow, Google's open-source software library for deep learning. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

This course is designed for engineers who wish to implement TensorFlow in their deep learning projects.

Upon completion of this course, participants will:

• possess a solid understanding of Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs)
• grasp TensorFlow's architecture and deployment mechanisms
• be capable of managing installation, production environments, architectural tasks, and configurations
• evaluate code quality, perform debugging, and monitor system performance
• implement advanced production-level tasks such as training models, constructing graphs, and logging