Edge Computing Infrastructure Training Course

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at advanced-level professionals who wish to enhance their knowledge of machine learning models, improve their skills in hyperparameter tuning, and learn how to deploy models effectively 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 Uzbekistan (online or onsite) is aimed at intermediate-level data scientists and healthcare professionals who wish to leverage AI for advanced healthcare applications using Google Colab.

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

• Implement AI models for healthcare using Google Colab.
• Use AI for predictive modeling in healthcare data.
• Analyze medical images with AI-driven techniques.
• Explore ethical considerations in AI-based healthcare solutions.

This instructor-led, live training in Uzbekistan (onsite or remote) is aimed at engineers who wish to deploy and manage IoT devices on AWS.

By the end of this training, participants will be able to build an IoT platform that includes the deployment and management of a backend, gateway, and devices on top of AWS.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at developers who wish to install, configure, and manage AWS IoT Greengrass capabilities to create applications for various devices.

By the end of this training, participants will be able to use AWS IoT Greengrass to build, deploy, manage, secure, and monitor applications on intelligent devices.

This instructor-led, live training in Uzbekistan (onsite or remote) is aimed at developers who wish to use AWS Lambda to build and deploy services and applications to the cloud, without needing to worry about provisioning the execution environment (servers, VMs and containers, availability, scalability, storage, etc.).

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

• Configure AWS Lambda to execute a function.
• Understand FaaS (Functions as a Service) and the advantages of serverless development.
• Build, upload and execute AWS Lambda functions.
• Integrate Lambda functions with different event sources.
• Package, deploy, monitor and troubleshoot Lambda based applications.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at intermediate-level data scientists and engineers who wish to use Google Colab and Apache Spark for big data processing and analytics.

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

• Set up a big data environment using Google Colab and Spark.
• Process and analyze large datasets efficiently with Apache Spark.
• Visualize big data in a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at beginner-level data scientists and IT professionals who wish to learn the basics of data science using Google Colab.

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

• Set up and navigate Google Colab.
• Write and execute basic Python code.
• Import and handle datasets.
• Create visualizations using Python libraries.

Google Colab Pro is a cloud-based environment designed for scalable Python development. It offers high-performance GPUs, extended runtimes, and increased memory, making it ideal for demanding AI and data science projects.

This instructor-led, live training (available online or onsite) is targeted at intermediate-level Python users who want to leverage Google Colab Pro for machine learning, data processing, and collaborative research within a powerful notebook interface.

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

• Set up and manage cloud-based Python notebooks using Colab Pro.
• Utilize GPUs and TPUs for faster computation.
• Streamline machine learning workflows with popular libraries such as TensorFlow, PyTorch, and Scikit-learn.
• Integrate with Google Drive and external data sources for collaborative projects.

Format of the Course

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

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Uzbekistan (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.

This instructor-led, live training in Uzbekistan (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 Uzbekistan (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of DevOps practices and streamline development processes using AWS Cloud9.

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

• Set up and configure AWS Cloud9 for DevOps workflows.
• Implement continuous integration and continuous delivery (CI/CD) pipelines.
• Automate testing, monitoring, and deployment processes using AWS Cloud9.
• Integrate AWS services such as Lambda, EC2, and S3 into DevOps workflows.
• Utilize source control systems like GitHub or GitLab within AWS Cloud9.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at intermediate-level professionals who wish to learn how to effectively build, deploy, and maintain serverless applications on AWS Cloud9 and AWS Lambda.

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

• Understand the fundamentals of serverless architecture.
• Set up AWS Cloud9 for serverless application development.
• Develop, test, and deploy serverless applications using AWS Lambda.
• Integrate AWS Lambda with other AWS services such as API Gateway and S3.
• Optimize serverless applications for performance and cost efficiency.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at beginner-level data scientists who wish to learn how to create meaningful and visually appealing data visualizations.

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

• Set up and navigate Google Colab for data visualization.
• Create various types of plots using Matplotlib.
• Utilize Seaborn for advanced visualization techniques.
• Customize plots for better presentation and clarity.
• Interpret and present data effectively using visual tools.

Summary:

• Introduction to the fundamental architecture and functions of the Internet of Things (IoT)
• Understanding the components such as "Things," "Sensors," and their integration with the internet, along with how they map to business functions in IoT
• Overview of essential IoT software components, including hardware, firmware, middleware, cloud services, and mobile applications
• Key IoT functionalities like fleet management, data visualization, Software-as-a-Service (SaaS) based fleet management and data visualization, alerts and alarms, sensor onboarding, device onboarding, and geo-fencing
• Basics of IoT device communication with the cloud using the MQTT protocol
• Connecting IoT devices to AWS using MQTT through AWS IoT Core
• Integrating AWS IoT Core with AWS Lambda for computation and data storage
• Connecting a Raspberry PI to AWS IoT Core and demonstrating simple data communication
• Handling alerts and events in the IoT ecosystem
• Sensor calibration techniques

Summary:

• Introduction to the fundamental architecture and functions of IoT (Internet of Things)
• Understanding the roles of "things," sensors, the internet, and how they align with business functions in IoT
• Overview of all essential IoT software components, including hardware, firmware, middleware, cloud services, and mobile applications
• Key IoT functionalities such as fleet management, data visualization, SaaS-based fleet management and data visualization, alerts and alarms, sensor onboarding, device onboarding, and geo-fencing
• Basics of IoT device communication with the cloud using MQTT (Message Queuing Telemetry Transport)
• Connecting IoT devices to AWS (Amazon Web Services) using MQTT through AWS IoT Core
• Linking AWS IoT Core with AWS Lambda functions for computation and data storage utilizing DynamoDB
• Integrating Raspberry PI with AWS IoT Core for simple data communication
• Practical hands-on experience with Raspberry PI and AWS IoT Core to create a smart device
• Data visualization and communication with a web interface for sensor data