Machine Learning Training in Tours

This instructor-led, live training in Tours (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 Tours (online or onsite) targets participants with varying levels of expertise who aim to utilize Google's AutoML platform to develop customized chatbots for diverse applications.

Upon completion of this training, participants will be able to:

• Grasp the fundamentals of chatbot development.
• Navigate the Google Cloud Platform and access AutoML.
• Prepare data for training chatbot models.
• Train and assess 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 Tours (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 instructor-led, live training in Tours (online or onsite) is designed for intermediate-level developers, data scientists, and technology enthusiasts who wish to acquire practical skills in deploying AI models on edge devices for various applications.

Upon completion of this training, participants will be capable of:

• Grasping the principles of Edge AI and its associated advantages.
• Establishing and configuring the edge computing environment.
• Creating, training, and optimizing AI models for edge deployment.
• Implementing functional AI solutions on edge devices.
• Assessing and enhancing the performance of models deployed at the edge.
• Tackling ethical and security issues inherent in Edge AI applications.

This instructor-led, live training in Tours (online or onsite) targets advanced AI engineers and data scientists with intermediate-to-advanced expertise who aim to boost DeepSeek model performance, reduce latency, and efficiently deploy AI solutions using contemporary MLOps practices.

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

• Optimizing DeepSeek models for efficiency, accuracy, and scalability.
• Applying best practices for MLOps and model versioning.
• Deploying DeepSeek models across cloud and on-premise infrastructure.
• Effectively monitoring, maintaining, and scaling AI solutions.

MLOps on Kubernetes serves as 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, targets intermediate-level practitioners aiming to build automated, scalable MLOps pipelines on Kubernetes.

Upon completing this training, participants will be able to:

• Design end-to-end CI/CD pipelines 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 that build real-world CI/CD workflows.
• Live-lab practice 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:

• Explore the Kubeflow ecosystem and its core components.
• Develop reproducible workflows using Kubeflow Pipelines.
• Execute scalable training jobs within a Kubernetes environment.
• Deploy machine learning models efficiently via Kubeflow Serving.

Format of the Course also allows for the evaluation of participants.

• 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 the deployment of optimized machine learning models onto edge devices with limited resources.

This instructor-led live training, available online or onsite, is designed for advanced technical professionals looking to design, optimize, and deploy full-scale TinyML pipelines.

Upon completing this training, participants will be able to:

• Gather, preprocess, and manage datasets specifically for TinyML applications.
• Train and optimize models for power-efficient microcontrollers.
• Transform models into lightweight formats compatible with edge devices.
• Deploy, test, and monitor TinyML applications on actual hardware.

Course Format

• Instructor-led lectures combined with technical discussions.
• Practical labs and iterative experimentation sessions.
• Hands-on deployment exercises on microcontroller platforms.

Customization Options

• To tailor the training to your specific toolchains, hardware boards, or internal workflows, please contact us to arrange.

This instructor-led, live training in Tours (online or onsite) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications.

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

• Understand 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.

This instructor-led, live training in Tours (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 Tours (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:

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

TinyML represents a methodology for deploying machine learning models on low-power, resource-limited devices at the network edge.

This instructor-led, live training (available online or onsite) is designed for advanced professionals aiming to secure TinyML pipelines and integrate privacy-preserving techniques into edge AI applications.

Upon completing this course, participants will be able to:

• Recognize security risks specific to on-device TinyML inference.
• Deploy privacy-preserving mechanisms for edge AI implementations.
• Secure TinyML models and embedded systems against adversarial threats.
• Apply best practices for secure data management in constrained environments.

Course Format

• Interactive lectures complemented by expert-led discussions.
• Practical exercises focused on real-world threat scenarios.
• Hands-on implementation using embedded security and TinyML tools.

Course Customization Options

• Organizations can request a customized version of this training to meet their specific security and compliance requirements.

This instructor-led, live training in Tours (online or onsite) is designed for advanced-level professionals who want to expand their knowledge of machine learning models, improve their hyperparameter tuning skills, and learn how to effectively deploy models using Google Colab.

By the end 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 Tours (online or onsite) is aimed at intermediate-level professionals who wish 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 Tours (online or onsite) targets intermediate-level business and AI professionals seeking to implement machine learning in business contexts, forecasting, and AI-driven systems through real case studies and Python-based tools.

Upon completing this training, participants will be able to:

• Comprehend the role of machine learning within AI and business strategy.
• Apply supervised and unsupervised learning techniques to structured business problems.
• Preprocess and transform data for modeling purposes.
• Utilize neural networks for classification and prediction tasks.
• Execute sales forecasting using statistical and ML-based methods.
• Implement clustering and association rule mining for customer segmentation and pattern discovery.

This instructor-led live training, held Tours (online or onsite), is designed for intermediate professionals aiming to apply AI-driven predictive maintenance techniques in semiconductor manufacturing to improve production efficiency and reduce unexpected equipment failures.

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

• Implement AI models for predicting equipment failures in semiconductor manufacturing.
• Analyze maintenance data to identify patterns and trends indicative of potential issues.
• Integrate AI-driven predictive maintenance into existing manufacturing workflows.
• Reduce downtime and maintenance costs through proactive equipment management.

This instructor-led live training in Tours (available online or onsite) is designed for advanced professionals who aim to apply cutting-edge AI techniques to semiconductor design automation, thereby improving efficiency, accuracy, and innovation in chip design and verification.

By the end 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 Tours (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 in Tours (online or on-site) targets intermediate-level professionals who wish to understand and apply AI techniques for optimizing semiconductor fabrication processes.

Upon completing this training, participants will be able to:

• Grasp AI methodologies used for process optimization in chip fabrication.
• Deploy AI models to improve yield and minimize defects.
• Examine process data to pinpoint critical parameters for optimization.
• Utilize machine learning techniques to fine-tune semiconductor manufacturing processes.

This instructor-led live training, held in Tours (online or onsite), is designed for 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 Tours (online or onsite) is designed for intermediate-level data scientists and developers who wish to apply machine learning algorithms efficiently 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 on hardware with strict resource limitations.

This instructor-led live training, available online or onsite, targets advanced practitioners seeking to optimize TinyML models for low-latency and memory-efficient deployment on embedded devices.

Upon completion of this training, participants will be capable of:

• Utilizing quantization, pruning, and compression techniques to minimize model size while maintaining accuracy.
• Benchmarking TinyML models for latency, memory usage, and energy efficiency.
• Deploying optimized inference pipelines on microcontrollers and edge devices.
• Assessing the trade-offs between performance, accuracy, and hardware constraints.

Course Format

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

Customization Options

• For training tailored to specific hardware platforms or internal workflows, please reach out to customize the program.

This instructor-led live training in Tours (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.

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

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

• Grasp the fundamental principles of AI and their application in semiconductor manufacturing.
• Identify areas within semiconductor manufacturing where AI can be effectively implemented.
• Use 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 construct reproducible, portable, and scalable environments for machine learning systems.

This instructor-led training session, available either online or onsite, targets technical professionals with intermediate to advanced skills who aim to containerize and operationalize complete ML pipelines using Docker.

After completing this training, participants will be equipped to:

• Containerize workloads for ML training, validation, and inference.
• Design and orchestrate end-to-end ML pipelines utilizing Docker and complementary tools.
• Implement versioning, ensure reproducibility, and integrate CI/CD practices 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.

Course Customization Options

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

This instructor-led, live training in Tours (online or onsite) is aimed at data scientists and developers who wish to use ML.NET machine learning models to automatically derive projections from executed data analysis for enterprise applications.

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

• Install ML.NET and integrate it into the application development environment.
• Understand the machine learning principles behind ML.NET tools and algorithms.
• Build and train machine learning models to perform predictions with the provided data smartly.
• Evaluate the performance of a machine learning model using the ML.NET metrics.
• Optimize the accuracy of the existing machine learning models based on the ML.NET framework.
• Apply the machine learning concepts of ML.NET to other data science applications.

This guided, live training in Tours (online or on-site) is designed for intermediate data professionals seeking to apply machine learning techniques to business issues, including sales forecasting and predictive modeling with neural networks.

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

• Understand the core concepts and types of machine learning.
• Apply key algorithms for classification, regression, clustering, and association analysis.
• Perform exploratory data analysis and data preparation using Python.
• Use neural networks for nonlinear modeling tasks.
• Implement predictive analytics for business forecasting, including sales data.
• Evaluate and optimize model performance using visual and statistical techniques.

This instructor-led, live training in Tours (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 in Tours (online or onsite) is designed for intermediate to advanced cybersecurity professionals seeking to enhance their skills in AI-driven threat detection and incident response.

Upon completion of this training, participants will be able to:

• Deploy advanced AI algorithms for real-time threat detection.
• Customize AI models to address specific cybersecurity challenges.
• Create automation workflows for threat response.
• Protect AI-driven security tools from adversarial attacks.

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

Upon completion of this training, participants will be able to:

• Grasp the fundamentals of TinyML and its advantages for edge AI applications.
• Configure a development environment tailored for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Utilize TensorFlow Lite and Edge Impulse to build real-world TinyML solutions.
• Optimize AI models to meet power efficiency and memory limitations.

This instructor-led, live training in Tours (online or onsite) is aimed at beginner-level cybersecurity professionals who wish to learn how to leverage AI for improved threat detection and response capabilities.

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

• Understand AI applications in cybersecurity.
• Implement AI algorithms for threat detection.
• Automate incident response with AI tools.
• Integrate AI into existing cybersecurity infrastructure.

This instructor-led, live training in Tours (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 Tours (online or onsite) is designed for 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 in (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.

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

• Understand the principles of Stable Diffusion and how it works 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 Tours (online or onsite) is designed for engineers and data scientists at the beginner level who want to grasp the fundamentals of TinyML, investigate its practical uses, and implement AI models on microcontrollers.

Upon completing this training, participants will be capable of:

• Grasping the core concepts of TinyML and why they matter.
• Deploying lightweight AI models onto microcontrollers and edge devices.
• Optimizing and refining machine learning models to minimize power usage.
• Utilizing TinyML for real-world scenarios including gesture recognition, anomaly detection, and audio processing.

This course aims to equip participants with general proficiency in applying Machine Learning methods in real-world scenarios. By leveraging the Python programming language and its extensive ecosystem of libraries, and supported by a wide range of practical examples, the course demonstrates how to utilize the essential components of Machine Learning. Participants will learn to make informed data modeling decisions, interpret algorithm outputs, and validate results effectively.

Our objective is to empower you with the confidence to understand and apply the core tools of the Machine Learning toolkit, while helping you steer clear of common pitfalls associated with Data Science applications.

This course empowers programmers and data analysts with the essential techniques needed to construct machine learning solutions entirely from scratch using Python. It explores the core principles of supervised learning (including classification and regression) and unsupervised learning (such as clustering and anomaly detection), alongside advanced neural network architectures. Participants will examine proven methods for leveraging scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. The curriculum supports professionals in implementing practical ML models, assessing algorithmic limitations, and completing applied projects designed for real-world problem-solving.

Deep Reinforcement Learning (DRL) merges reinforcement learning concepts with deep learning models, empowering agents to make decisions by interacting with their surroundings. This technology drives many modern AI innovations, including self-driving cars, robotic control systems, algorithmic trading, and adaptive recommendation engines. DRL enables artificial agents to learn strategies, refine policies, and make autonomous choices through trial and error, guided by reward-based feedback.

This instructor-led training, available online or onsite, targets intermediate-level developers and data scientists eager to master and apply Deep Reinforcement Learning techniques. Participants will learn to build intelligent agents capable of making autonomous decisions in complex environments.

Upon completion of this training, participants will be able to:

• Grasp the theoretical foundations and mathematical principles underlying Reinforcement Learning.
• Implement core RL algorithms, including Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Apply DRL to practical scenarios such as gaming, robotics, and decision optimization.
• Troubleshoot, visualize, and enhance training performance using contemporary tools.

Format of the Course also allows for the evaluation of participants.

• Interactive lectures accompanied by guided discussions.
• Hands-on exercises and practical implementation tasks.
• Live coding demonstrations and project-based applications.

Course Customization Options

• To request a customized version of this course (for example, utilizing PyTorch instead of TensorFlow), please contact us to make arrangements.

A deep dive into the fundamentals of artificial intelligence demonstrates how intelligent technologies are transforming digital strategies, automating processes, and enhancing decision-making across enterprise operations. This course explores core concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and various machine learning paradigms, alongside communication, perception, and autonomous action. It equips executives and architects with the insights needed to evaluate AI-driven transformation opportunities, assess emerging technology trends, and implement practical intelligent solutions to accelerate business agility.

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

This eight-day programme guides participants through a comprehensive journey, starting from solid Python engineering principles and progressing to advanced AI system architecture. Learners cultivate disciplined coding habits, gain mastery over statistical and deep learning techniques, and construct production-ready generative AI and agent-based solutions. The curriculum prioritises reliability, evaluation, safety, and real-world deployment, moving beyond mere experimentation.

An Artificial Neural Network is a computational data model utilized in creating Artificial Intelligence (AI) systems capable of executing "intelligent" tasks. Neural Networks are frequently employed in Machine Learning (ML) applications, which constitute one implementation of AI. Deep Learning represents a specialized subset of ML.

Enhance your data science skills with this in-depth Machine Learning training program. The course covers essential algorithms such as Naive Bayes, Decision Trees, Neural Networks, Support Vector Machines, and Clustering techniques. Participants will acquire practical experience grounded in theoretical principles, utilizing real-world case studies. This course is particularly beneficial for data analysts, software engineers, AI aficionados, and business professionals aiming to implement machine learning solutions. You will master key concepts including classification performance metrics, cross-validation, the bias-variance trade-off, and deep learning foundations to construct reliable predictive models.

This instructor-led, live training in Tours (online or on-site) 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 Tours (online or onsite) targets data scientists who want to use TensorFlow to analyze potential fraud data.

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

• Create a fraud detection model in 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 that enables computers to learn from data without being explicitly programmed for every specific task.

Deep learning is a specialized branch of machine learning that employs methods based on learning data representations and structures, such as neural networks.

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

In this instructor-led live training, participants will discover how to implement deep learning models for the telecommunications sector using Python by guiding them through the development of a deep learning credit risk model.

Upon completing this training, participants will be able to:

• Comprehend the fundamental concepts of deep learning.
• Identify the applications and uses of deep learning within the telecom industry.
• Utilize Python, Keras, and TensorFlow to construct deep learning models for telecom.
• Develop their own deep learning customer churn prediction model using Python.

Course Format

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

Course Customization Options

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

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

Participants are introduced to the fundamental concepts of Machine Learning and learn how to apply them directly to data analysis tasks, including prediction, classification, and segmentation.

The course emphasizes practical understanding, utilizing familiar tools like Python, Pandas, and Jupyter Notebook, without necessitating an advanced mathematical background.

This instructor-led, live training in Tours (online or onsite) is designed for engineers who wish to evaluate the current approaches and tools available, helping them make informed decisions about the future adoption of MLOps within their organizations.

Upon completing this training, participants will be able to:

• Install and configure various MLOps frameworks and tools.
• Assemble the right kind of team with the right 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 reproducible and maintainable.

This instructor-led, live training in Tours (online or onsite) targets 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 Tours (online or onsite) is designed for developers and data scientists who aim to utilize TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and other 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 foundational knowledge on neural networks, machine learning algorithms, and the principles and applications of deep learning.

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

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

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

Audience

This course is designed for engineers looking to utilize TensorFlow for their deep learning projects.

Upon completion of this course, participants will:

• possess a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• comprehend TensorFlow’s architecture and deployment mechanisms
• be capable of managing installation, production environments, and architectural configurations
• be able to evaluate code quality, perform debugging, and monitor performance
• be proficient in implementing advanced production tasks such as training models, constructing graphs, and logging