FinOps Training Course

This instructor-led, live training in Serbia (online or onsite) is designed for advanced professionals who want to deepen 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 guided, live training in Serbia (online or in-person) targets intermediate data scientists and medical professionals seeking to apply AI for advanced healthcare applications using Google Colab.

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

• Deploy AI models for healthcare using Google Colab.
• Utilize AI to perform predictive modeling on healthcare datasets.
• Conduct medical image analysis through AI-driven methodologies.
• Investigating ethical implications associated with AI solutions in healthcare.

This instructor-led, live training in Serbia (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 Serbia (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 Serbia (onsite or remote) is designed for developers who want to use AWS Lambda to build and deploy services and applications to the cloud, without worrying about provisioning the execution environment (such as servers, VMs, containers, availability, scalability, and storage).

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 Serbia (available online or onsite) is targeted at intermediate-level data scientists and engineers interested in employing Google Colab and Apache Spark for big data processing and analytics.

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

• Configure a big data environment using Google Colab and Spark.
• Efficiently process and analyze large datasets via Apache Spark.
• Integrate Apache Spark with cloud-based tools.

This instructor-led live training in Serbia (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, delivering high-performance GPUs, extended runtimes, and increased memory capacity to handle intensive AI and data science workloads.

This instructor-led live training, available 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 completion of this training, participants will be able to:

• Set up and manage cloud-hosted Python notebooks using Colab Pro.
• Access GPUs and TPUs to accelerate computational tasks.
• Optimize machine learning workflows using 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 group discussions.
• Extensive exercises and hands-on practice.
• Practical implementation within a live-lab environment.

Course Customization Options

• To arrange customized training for this course, please contact us to discuss your requirements.

This instructor-led live training Serbia (available online or on-site) targets advanced professionals who aim to deepen their grasp 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 Serbia (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 Serbia (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 Serbia (online or onsite) is designed for intermediate-level professionals who want 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 Serbia (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:

• Foundations of IoT architecture and its core functions
• Understanding 'Things', 'Sensors', the Internet, and how they map to business IoT functions
• Key software components of IoT: hardware, firmware, middleware, cloud infrastructure, and mobile applications
• Core IoT functions: fleet management, data visualization, SaaS-based fleet and data visualization tools, alerts/alarms, sensor onboarding, device ('thing') onboarding, and geofencing
• Basics of IoT device-to-cloud communication using MQTT
• Connecting IoT devices to AWS via MQTT (AWS IoT Core)
• Integrating AWS IoT Core with AWS Lambda for computation and DynamoDB for data storage
• Linking Raspberry Pi with AWS IoT Core for basic data communication
• Practical experience using Raspberry Pi and AWS IoT Core to build a smart device
• Visualizing sensor data and establishing communication through a web interface

This instructor-led, live training in Serbia (online or onsite) is aimed at 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.