MinIO Cloud Storage Stack Training Course

This instructor-led live training in Norway (online or onsite) is designed for advanced professionals seeking to deepen their expertise in machine learning models, sharpen their hyperparameter tuning skills, and learn effective model deployment techniques 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 Norway (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 Norway (onsite or remote) targets 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 Norway (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 Norway (onsite or remote) is designed for developers who want to use AWS Lambda to build and deploy services and applications to the cloud, without needing to manage the execution environment (servers, VMs, 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 various event sources.
• Package, deploy, monitor, and troubleshoot Lambda-based applications.

This instructor-led live training in Norway (online or onsite) is designed for intermediate-level data scientists and engineers who wish to utilise 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 Norway (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 provides a cloud-based environment designed for scalable Python development. It delivers high-performance GPUs, extended runtimes, and increased memory capacity to handle demanding AI and data science workloads.

This instructor-led live training, available either online or onsite, targets intermediate-level Python users looking to leverage Google Colab Pro for machine learning, data processing, and collaborative research within a powerful notebook interface.

Upon completing this training, participants will be able to:

• Set up and manage cloud-based Python notebooks using Colab Pro.
• Access GPUs and TPUs to accelerate computational tasks.
• Streamline machine learning workflows by utilizing popular libraries such as TensorFlow, PyTorch, and Scikit-learn.
• Integrate with Google Drive and external data sources to facilitate collaborative projects.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical application.
• Hands-on implementation within a live laboratory environment.

Course Customization Options

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

This instructor-led, live training in Norway (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 live, instructor-led training in Norway (online or onsite) targets intermediate-level data scientists and developers eager to understand and apply deep learning techniques within the Google Colab ecosystem.

By the conclusion 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 Norway (online or onsite) is designed for advanced-level professionals aiming to deepen their understanding of DevOps practices and streamline development processes using AWS Cloud9.

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

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

This instructor-led, live training in Norway (online or onsite) is designed for 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 Norway (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:

• Fundamentals of IoT architecture and functionality
• Understanding the concept of “things”, “sensors”, and the internet, alongside the correlation between business processes and IoT
• Key components of IoT software stacks, including hardware, firmware, middleware, cloud infrastructure, and mobile applications
• Core IoT capabilities such as fleet management, data visualization, SaaS-based facility management and data visualization, alerting systems, sensor and device onboarding, and geo-fencing
• Principles of IoT device communication with cloud services via MQTT
• Linking IoT devices to AWS through MQTT and AWS IoT Core
• Integrating AWS IoT Core with AWS Lambda for computation and DynamoDB for data storage
• Connecting Raspberry Pi devices to AWS IoT Core for basic data exchange
• Practical exercises using Raspberry Pi and AWS IoT Core to construct a smart device
• Visualizing sensor data and enabling communication through a web interface

This instructor-led, live training in Norway (online or onsite) targets intermediate-level data scientists and developers looking to efficiently apply machine learning algorithms within the Google Colab environment.

Upon completing this training, participants will be able to:

• Set up and navigate Google Colab for machine learning projects.
• Understand and apply various machine learning algorithms.
• Utilize libraries such as Scikit-learn for data analysis and prediction.
• Implement supervised and unsupervised learning models.
• Optimize and evaluate machine learning models effectively.