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Technical managers who wish to improve their knowledge and abilities in generative AI should take the Generative AI for Technical Managers course. The intended audience is mentioned below:
• Technical Managers and Leaders
• Technology Executives
• Product Managers
• Innovation Leaders
• Tech Entrepreneurs
• Data Scientists and Engineers
• Business Analysts
• Educators and Trainers
• Any Tech Enthusiast
While there are no strict prerequisites for the Generative AI for Technical Managers course, background or IT industry experience can provide a solid foundation for comprehending the course material. Here are some suggested prerequisites:
• Basic Technical Understanding.
• Familiarity with Python and other programming languages and concepts.
• Fundamental understanding of algorithms and data structures.
• Knowledge of the fundamentals of machine learning.
• Fundamental comprehension of algebra and calculus, as well as basic mathematics.
• Comprehension of statistical principles and how to use them to analyze data.
• Basics of AI and Machine Learning.
• Comprehension of organizational dynamics and how technology helps businesses achieve their objectives.
• Learning Mindset.
Having these prerequisites can be advantageous; the course is designed to cater to individuals with varied backgrounds and levels of experience.
Upon completion of the course, the professionals will be competent in the following areas:
• Exhibit a thorough grasp of generative artificial intelligence, the technology that underpins it, and the range of sectors in which it is used.
• Utilise strategic thinking to help you decide which Generative AI applications and adoptions are best for your organization.
• Demonstrate your ability to lead and manage teams working on generative AI projects while considering both the technical and non-technical factors.
• Analyze how generative AI might affect revenue models, competitive positioning in the market, and corporate goals.
• Recognize and follow ethical issues in AI development to ensure that generative AI technologies are used fairly and responsibly.
• Foster understanding and collaboration among non-technical stakeholders by communicating intricate technical concepts linked to generative AI.
• Encourage a culture of creativity by using generative AI to spark original answers to problems in business and technology.
• Determine and manage any dangers that might arise from generative AI, creating plans to reduce such risks and ensure that industry rules are followed.
• Plan, carry out, and supervise Generative AI projects successfully, ensuring they align with organizational objectives and deadlines.
Virtual Instructor-Led Training
- Instructor-led Online Training
- Experienced Subject Matter Experts
- Approved and Quality Ensured training Material
- 24*7 leaner assistance and support
Customized According To Team's Requirements
- Blended Learning Delivery Model (Self-Paced E-Learning And/Or Instructor-Led Options)
- Course, Category, And All-Access Pricing
- Enterprise-Class Learning Management System (LMS)
- Enhanced Reporting For Individuals And Teams
- 24x7 Teaching Assistance And Support
• What is Generative AI
• Key Concepts in Generative AI
• Generative Models and Discriminative Models
• Types of Generative Models (e.g., Variational Autoencoders, Generative Adversarial Networks)
• Training and Inference in Generative Models
• Discuss a few Industry use cases of Generative AI Applications
• Applications of Generative AI and Deep Learning
• Image and Video Generation
• Music and Audio Generation
• Text Generation
• Lab: Hands-on lab on Text Generation using Large Language models
• What are Large Language Models?
• Importance and Applications of Large Language Models
• Overview of LLMs in the Context of Natural Language Processing
• Understanding the Architecture of Large Language Models
• Transformer Architecture
• Self-Attention Mechanism
• Pre-training and Fine-tuning of LLMs
• Training and Data Requirements for Large Language Models
• Training Corpus and Data Collection
• Pre-processing and Tokenization
• Training Process and Computational Resources
- What is Prompt Engineering?
- Importance of Prompt Engineering in Modern Organizations
- Role of Managers in Prompt Engineering and Management
- Understanding the Prompt Generation Process
- Design and optimize prompts
- Apply advanced prompt engineering techniques
- Review and apply the latest and most advanced prompt engineering techniques.
- Understanding of Multi-modal LLM and different methods in Multi-modal LLMs
- Tree-of-thought and chain-of-thought methods
- Generative AI Product Development
- Building AI First Products
- Understanding the complexity and challenges
- Design Exploration and Ideation
- Simulation and Testing
- Generative AI Project Lifecycle
- Evaluation metrics for generative AI models
- Qualitative and quantitative assessment of generative AI outputs
- User feedback and engagement analysis
- Continual improvement and iteration techniques
- Data Protection, Privacy and Security
- Things to consider for protecting Data
- Data Lifecycle Management
- Compliances & Regulations
- Aspects to consider for Data Security
- Data Privacy Considerations
- Generative AI Deployment
- Model deployment strategies: on-premises, cloud-based, and edge deployment
- Integration with existing systems and workflows
- Testing and performance optimization
- Monitoring and maintenance of generative AI models
- Responsible AI Considerations
- Ethical implications of generative AI
- Fairness, transparency, and accountability in AI projects
- Regulatory frameworks and guidelines for generative AI
- Building responsible and ethical generative AI systems
- Understanding the roles and responsibilities of analysts, engineers, and scientists in generative AI projects
- Effective communication and collaboration strategies
- Project scoping and requirement gathering
- Overcoming challenges and mitigating risks in project implementation
Adversarial Robustness Researcher
AI Compliance Officer
Thanks to its time-saving and productivity-boosting capabilities, generative AI completely changes the software development business. Developers can concentrate on more complicated tasks, while generative AI handles repetitive tasks like building UIs, testing, and documentation.
Learners' assessments will be conducted through weekly tasks, group projects, and a final test. The evaluation standards will encompass script accuracy, productivity, and quality.
This course will make you a pro in Generative AI in 02 days.
By enrolling and completing the Generative AI for Technical Managers (GAITM) course, you'll learn about the possibilities, constraints, and capabilities of generative AI. This course will allow you to explore practical applications and acquire knowledge of typical use cases. At Vinsys, you'll get an opportunity to work directly with generative AI projects to apply what you've learned and obtain an understanding of the technology's implications for society and business.
Vinsys' exceptional support system and approved training programs make it a great destination to receive training. In addition to tools that support them in optimizing value for the customer, excellent support is always accessible. Professionals at Vinsys are skilled in recognizing the value of technology and integrating it into the overarching business plan. While Vinsys has a broad perspective, it also pays close attention to minutiae to ensure everything fulfills and surpasses standards.
The course covers reinforcement learning, machine learning, natural language processing, neural networks, convolutional neural networks, recurrent neural networks, generative models, reinforcement learning, deep learning, and supervised and unsupervised learning. This technology can completely change how people and computers collaborate on creative projects.
Generative AI improves decision-making by facilitating scenario simulations, enhancing data processing, and offering insightful information. Decision-makers can examine several approaches, evaluate possible consequences, and make well-informed decisions with the aid of generative AI, which generates various realistic solutions. Rather than creating AI and ML code from scratch, developers can use generative AI to utilize, modify, and train algorithms to build new code and solutions. This can save time and effort.