Building AI Solutions on the Edge Training Course

This instructor-led, live training in Argentina (online or onsite) is aimed at data scientists and software engineers who wish to use AdaBoost to build boosting algorithms for machine learning with Python.

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

• Set up the necessary development environment to start building machine learning models with AdaBoost.
• Understand the ensemble learning approach and how to implement adaptive boosting.
• Learn how to build AdaBoost models to boost machine learning algorithms in Python.
• Use hyperparameter tuning to increase the accuracy and performance of AdaBoost models.

This course explores the application of AI—particularly Machine Learning and Deep Learning—within the automotive sector. It equips participants with the knowledge to identify technologies that can be applied across various scenarios in vehicles, ranging from basic automation and image recognition to advanced autonomous decision-making.

This course is designed for managers, solutions architects, innovation officers, CTOs, software architects, and anyone seeking a comprehensive overview of applied artificial intelligence and its near-term development trajectory.

This instructor-led live training in Argentina (online or on-site) targets biologists who wish to understand the functioning of AlphaFold and utilize its models to guide 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 (online or onsite) targets technical professionals with a background in machine learning who seek to optimize models for identifying complex patterns in big data through AutoML frameworks.

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

By the conclusion 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 Argentina (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 Argentina (online or onsite) targets data scientists and analysts aiming to automate, evaluate, and manage predictive models using DataRobot's machine learning capabilities.

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

• Import datasets into DataRobot to analyze, assess, and validate data quality.
• Construct and train models to pinpoint key variables and achieve prediction goals.
• Interpret models to generate actionable insights that support business decision-making.
• Monitor and manage models to ensure optimal prediction performance.

This instructor-led live training in Argentina (online or onsite) is intended for intermediate developers, data scientists, and AI practitioners aiming to utilize TensorFlow Lite for Edge AI applications.

By the conclusion of this training, attendees 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 Argentina (online or onsite) is designed for data scientists, data analysts, and developers who wish to explore AutoML products and features to create and deploy custom ML training models with minimal effort.

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

• Explore the AutoML product line to implement different services for various data types.
• Prepare and label datasets to create custom ML models.
• Train and manage models to produce accurate and fair machine learning models.
• Make predictions using trained models to meet business objectives and needs.

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.

This live, instructor-led training, conducted Argentina (online or on-site), targets developers and data scientists interested in building, deploying, and managing machine learning workflows on Kubernetes.

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

• Install and configure Kubeflow in on-premise and cloud environments.
• Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
• Run complete machine learning pipelines across diverse architectures and cloud environments.
• Use Kubeflow to spawn and manage Jupyter notebooks.
• Create ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led live training (online or onsite) targets developers who intend to use Google’s ML Kit to build machine learning models optimized for mobile device processing.

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

• Set up the necessary development environment to initiate the creation of machine learning features for mobile apps.
• Integrate new machine learning technologies into Android and iOS apps using the ML Kit APIs.
• Enhance and optimize existing applications by employing the ML Kit SDK for on-device processing and deployment.

This instructor-led, live training in Argentina (online or onsite) targets data scientists and software engineers who wish to use Random Forest to build machine learning algorithms for large datasets.

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

• Set up the necessary development environment to start building machine learning models with Random Forest.
• Understand the advantages of Random Forest and how to implement it to resolve classification and regression problems.
• Learn how to handle large datasets and interpret multiple decision trees in Random Forest.
• Evaluate and optimize machine learning model performance by tuning the hyperparameters.

This instructor-led live training in Argentina (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.