Generative Ai For Data Professionals Training Course: Data Synthesis & Analysis

Introduction

Unlock the transformative power of AI with our Generative AI for Data Professionals Training Course. This program is designed to equip you with the essential skills to utilize generative AI models for data synthesis and analysis, enabling you to create innovative solutions and drive data-driven insights. In today's AI-driven world, mastering generative AI is crucial for staying ahead and leveraging cutting-edge technologies. Our generative AI training course provides hands-on experience and expert guidance, empowering you to build powerful AI applications.

This AI for data professionals training delves into the core concepts of generative AI, covering topics such as generative adversarial networks (GANs), variational autoencoders (VAEs), and large language models (LLMs). You'll gain expertise in using industry-standard tools and techniques to utilize generative AI models for data synthesis and analysis that meet the demands of modern data environments. Whether you're a data scientist, analyst, or developer, this generative AI course will empower you to create and deploy advanced AI solutions.

Target Audience:

• Data Scientists
• Data Analysts
• AI Engineers
• Machine Learning Engineers
• Data Developers
• Research Scientists
• Anyone needing generative AI skills

Course Objectives:

• Understand the fundamentals of generative AI models.
• Master the implementation of GANs for data synthesis.
• Utilize VAEs for data generation and anomaly detection.
• Apply large language models (LLMs) for text data analysis and generation.
• Develop generative AI models for various data types (images, text, time series).
• Optimize generative AI models for performance and accuracy.
• Deploy generative AI models in production environments.
• Troubleshoot and debug generative AI pipelines.
• Implement data security and access control in generative AI workflows.
• Integrate generative AI models with existing data platforms.
• Understand how to monitor and maintain generative AI models.
• Explore advanced generative AI techniques and architectures.
• Apply real world use cases for generative AI in data analysis.

Duration

10 Days

Course content

Module 1: Introduction to Generative AI

• Fundamentals of generative AI models.
• Overview of GANs, VAEs, and LLMs.
• Setting up a generative AI development environment.
• Introduction to generative AI tools and frameworks.
• Best practices for generative AI.

Module 2: Generative Adversarial Networks (GANs)

• Implementing GANs for image synthesis and data augmentation.
• Utilizing conditional GANs for controlled data generation.
• Implementing GANs for text and time series data.
• Training and optimizing GAN models.
• Best practices for GAN implementation.

Module 3: Variational Autoencoders (VAEs)

• Implementing VAEs for data generation and latent space exploration.
• Utilizing VAEs for anomaly detection and data reconstruction.
• Implementing conditional VAEs for controlled generation.
• Training and optimizing VAE models.
• Best practices for VAE implementation.

Module 4: Large Language Models (LLMs)

• Utilizing LLMs for text generation and summarization.
• Implementing LLMs for text classification and sentiment analysis.
• Fine-tuning LLMs for specific tasks.
• Implementing LLMs for data augmentation and synthesis.
• Best practices for LLM implementation.

Module 5: Generative AI for Diverse Data Types

• Implementing generative AI for image data.
• Utilizing generative AI for text data.
• Implementing generative AI for time series data.
• Implementing generative AI for tabular data.
• Best practices for diverse data types.

Module 6: Model Optimization and Tuning

• Optimizing generative AI models for performance.
• Implementing hyperparameter tuning and regularization.
• Utilizing model compression and quantization.
• Handling data biases in generative models.
• Best practices for model optimization.

Module 7: Model Deployment and Productionization

• Deploying generative AI models in production environments.
• Utilizing containerization and orchestration tools.
• Implementing model serving and API endpoints.
• Monitoring model performance in production.
• Best practices for model deployment.

Module 8: Troubleshooting and Debugging Generative AI Pipelines

• Debugging generative AI models and pipelines.
• Analyzing model errors and performance issues.
• Utilizing debugging tools and techniques.
• Identifying and resolving model biases.
• Best practices for troubleshooting.

Module 9: Data Security and Access Control

• Implementing data security in generative AI workflows.
• Utilizing authentication and authorization.
• Implementing data encryption and masking.
• Auditing and compliance in generative AI.
• Best practices for data security.

Module 10: Integrating Generative AI with Data Platforms

• Integrating generative AI models with existing data platforms.
• Utilizing data connectors and APIs.
• Implementing real-time generative AI pipelines.
• Best practices for integration.

Module 11: Model Monitoring and Maintenance

• Monitoring generative AI model performance and drift.
• Implementing model retraining and updating.
• Utilizing model monitoring tools and techniques.
• Handling model versioning and rollback.
• Best practices for model maintenance.

Module 12: Advanced Generative AI Techniques

• Implementing diffusion models for high-quality data generation.
• Utilizing generative AI for data privacy and synthetic data generation.
• Implementing generative AI for domain adaptation.
• Advanced techniques for generative AI.
• Best practices for advanced techniques.

Module 13: Generative AI in Cloud Environments

• Deploying generative AI models on cloud platforms.
• Utilizing cloud-based generative AI services.
• Optimizing cloud resources for generative AI.
• Best practices for cloud deployment.

Module 14: Generative AI and Data Governance

• Implementing data governance policies in generative AI.
• Utilizing metadata management tools.
• Implementing data lineage and data dictionary.
• Best practices for data governance.

Module 15: Future Trends in Generative AI for Data Professionals

• Emerging trends in generative AI research and applications.
• Utilizing AI and automation in generative AI workflows.
• Implementing responsible and ethical generative AI practices.
• Best practices for future generative AI.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.