AI Training

Start Your Journey With Visionbot AI - AI Training for Generating Value

AI is impacting every aspect of business operations soon. Most people who could benefit from AI today don’t usually have the time to learn about it or don’t know where to learn it from.

A recent LinkedIn survey finds that 81% of professionals believe there will be significant impact of AI in their jobs. About 71% want to learn more but don’t know where to start. More than 79% of those surveyed were panning to us AI tools to solve problems at work and take repetitive tasks off their TO DO lists freeing them for more productive and creative work.

There is a sense of excitement, but also a feeling of getting overwhelmed and a fear of being left behind in this shift. Think executives, managers, consultants, or other professionals who aren’t directly working in AI–but can potentially leverage it to improve their work.

AI leaders like Nvidia, Google, Microsoft , Accenture have announced plans to train more than 1.5 million people about GEN AI, ML concepts, OPEN tools, Cloud Skills, Customizing pre-trained GEN AI models for building applications.

Industry Executives increasingly see investing in retraining and upskilling existing workers as an urgent business priority that companies must lead on in areas like text based searches, Prompt Engineering , Content Generation (Product), Customer Experience (Process), Personal Avatars (People).

“AI for All” is a course designed by VisionBot to help business professionals, students and solution architects understand business challenges for AI deployment. The foundational aspect what are the main types of AI and when to use which or what are the specific use cases in their domain, whether it’s supply chain, manufacturing, Construction, Smart cities or cross-functional like HR, Marketing or Sales.

Outcome: Matching the six major patterns of AI to existing functions and problems that have and use this as a mechanism to generate these ideas for AI projects Utilizing the five steps framework of Generating Use cases – Build Business Case, Pilot , Plan for Production use , Deploy & Monitor for selecting and launching AI initiatives.

Introduction to Artificial Intelligence – Foundation Course :

Intro to AI (types & how it works) – Diff between AI / ML / DL Type: Prediction / Clustering / Anomaly / NLP / Object / R.C.A ML(Supervised / Unsupervised / Reinforcement) / Deep Learning concepts & tools (Tensorflow / Pytorch / Spark / MXNet)
AI use-cases (Diff Domains and Functions) – What it can and cannot do !!!
Types of Data required – Modelsets
AI lifecycle - Define problem / Extract data / Develop Model – Target Metrics / Extract metrics / Select Features / Model Fit / Test Model

How to select an AI project? (business & technical criteria)
Frameworks for AI project – Generate / Build / Pilot / Plan / Deploy / Track
How to test an AI idea? (POCs, metrics)
AI tech landscape (frameworks, services, & tools)
Building AI teams ( skills, job functions, outsourcing vs. in-house)
Embedding AI across the organization (from POCs to an embedded capability in production)

Outcome based Generative AI Courses

Beginners: - 2 hours

Introduction to LLM's
Generative AI fundamentals
Introduction to GEN AI Tools

Industry Practitioners : - 12 hours

Encoder / Decoder architecture
Transformer Models
Image captioning models
Prompt design
Leveraging LLM’s: text generation (articles, posts log and short form content)
Regulations & Compliance

Project: PROMPT Engineering Chat API
Kinds of APIs, Models, Token, Rate Limits and Pricing Evaluating Generative AI Outputs

Developers & Influencers - 20 hours

Generative AI studio
Live Lab - One Model Generation
Chatbot based on custom knowledgebase

Project : CONTENT GENERATION AND SUMMARIZATION
Content Generation using Generative AI Abstractive Summarization Text Generation Image Generation – From Text Prompts / Face Image / old Images

Gain expertise in generative AI principles, leveraging its problem-solving capabilities, effective use of Open Tools, harnessing Prompt Engineering for business use-cases, and fine-tuning Large Language Models to achieve desired outputs. Text Classification, Text Summarization, and Text Generation using Generative AI.

Outcomes

Have a solid understanding of Generative AI - history, now, and future and how to use it to solve problems & build services
How to use AI platform’s to leverage Generative AI
How to use APIs and Python SDKs to scale Prompt Engineering
How to use Prompt Engineering effectively for business use-cases
Get working knowledge of how to use Generative AI for key business applications - natural language classification, summarization, and generation
How Large Language Models (LLMs) can be tuned to get desired outputs
How to think about enterprise-level Generative AI solutions
Write basic snippets of codes that call LLM APIs and deliver Prompt Engineering at scale

Be the spark that ignites excellence

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