MLOps - Deploying ML solutions

Course description:

The MLOps course provides a comprehensive introduction to the process of implementing and managing machine learning solutions in production environments. It covers key steps and best practices in developing, training, evaluating and maintaining ML models, with a focus on the use of specific tools such as MLflow, Apache Airflow and Kubeflow.

Prerequisites:

Basic Python programming skills.
Fundamental understanding of machine learning techniques.
Experience with ML libraries in Python (scikit-learn, TensorFlow, PyTorch).
Basics of cloud systems and containers (Docker, Kubernetes).
Familiarity with the concepts of DevOps and CI/CD.

Who is it for:

Data scientists and data analytics specialists who want to implement and maintain ML models in production environments.
Software engineers and developers interested in building and managing infrastructure for machine learning solutions.
Research and development teams in AI and machine learning working on complex and scalable projects.
Artificial intelligence professionals who want to deepen their knowledge of best practices and tools for implementing and managing ML models.
Product managers and team leaders who want to understand the processes and challenges associated with implementing machine learning solutions within their organizations.

What will you learn:

The concept of MLOps and its role in the field of artificial intelligence and machine learning.
The stages of the machine learning model development lifecycle, from data management and model training to validation and deployment.
The process of data management, including data collection, cleaning and versioning, as well as splitting into training, validation and testing sets.
Techniques used to train models, including selection of appropriate algorithms, cross-validation, optimization of hyperparameters and ensuring reproductibility of experiments.
Use of the MLflow platform for experiment tracking, model management and integration with other services.
Orchestrate workflows with Apache Airflow to automate processes for training and evaluating machine learning models.
Different ways to implement machine learning models, including the use of cloud services and on-premises solutions, as well as container and microservices concepts.
Using the Kubeflow platform to develop, deploy and manage machine learning models in the Kubernetes environment.
Monitoring the performance of machine learning models, identifying data drift and approaches to updating models.
The importance of ethical and security aspects in the development and use of machine learning models such as data privacy, algorithm bias, and accountability in decision making.

Course module structure

• Introduction to MLOps: history, applications and importance in the field of AI and machine learning.
• ML Model Development Lifecycle: from data and training to validation and deployment.
• Data management: data collection, cleaning and versioning; division into training, validation and testing sets.
• Model training: Algorithm selection, cross-validation, hyperparameter optimization and experiment reproductibility.
• Introduction to MLflow: tracking experiments, managing models, and integrating with other services.
• Orchestrating workflows with Apache Airflow: automating the processes of training and evaluating ML models.
• Deploying ML models: Deployment modalities, cloud services and on-premises solutions; containers and microservices.
• Kubeflow: The platform for developing, deploying and managing ML models in Kubernetes.
• Model monitoring and maintenance: performance monitoring, data drift and model updates.
• Ethical and security aspects: data privacy, algorithm bias and responsibility in the development and use of ML models.

Certification:

Certificate of completion.

€ 840

DURATION: 2 days
SKILL LEVEL: Associate
LECTURES: 10 lessons

Course description:

Prerequisites:

Basic Python programming skills.
Fundamental understanding of machine learning techniques.
Experience with ML libraries in Python (scikit-learn, TensorFlow, PyTorch).
Basics of cloud systems and containers (Docker, Kubernetes).
Familiarity with the concepts of DevOps and CI/CD.

Who is it for:

Data scientists and data analytics specialists who want to implement and maintain ML models in production environments.
Software engineers and developers interested in building and managing infrastructure for machine learning solutions.
Research and development teams in AI and machine learning working on complex and scalable projects.
Artificial intelligence professionals who want to deepen their knowledge of best practices and tools for implementing and managing ML models.
Product managers and team leaders who want to understand the processes and challenges associated with implementing machine learning solutions within their organizations.

What will you learn:

The concept of MLOps and its role in the field of artificial intelligence and machine learning.
The stages of the machine learning model development lifecycle, from data management and model training to validation and deployment.
The process of data management, including data collection, cleaning and versioning, as well as splitting into training, validation and testing sets.
Techniques used to train models, including selection of appropriate algorithms, cross-validation, optimization of hyperparameters and ensuring reproductibility of experiments.
Use of the MLflow platform for experiment tracking, model management and integration with other services.
Orchestrate workflows with Apache Airflow to automate processes for training and evaluating machine learning models.
Different ways to implement machine learning models, including the use of cloud services and on-premises solutions, as well as container and microservices concepts.
Using the Kubeflow platform to develop, deploy and manage machine learning models in the Kubernetes environment.
Monitoring the performance of machine learning models, identifying data drift and approaches to updating models.
The importance of ethical and security aspects in the development and use of machine learning models such as data privacy, algorithm bias, and accountability in decision making.