6 Months AI Generalist Program By Outskill – Free Download Course
Learn How To Turn AI Concepts into Reality—Build & Deploy AI Models Like a Pro!
✅ About This Course:
✅ Course Authors: Outskill
✅ Free For Our VIP Members? : Yes
✅ Download Links : Mega & Google Drive
✅ Course Size : 38.73 GB
✅ Updatable? : Yes, all future updates included.
✅ Sales Page : You can check at the bottom of this page.
🏆 Here’s What You Get & Learn With This Course:
AI Engineers bridge the gap between traditional software development and AI-powered applications. They develop and deploy LLMs, automation workflows, and scalable AI-driven solutions.
AI Frameworks & LLMs
Fine-tune and deploy AI models.
Collaboration & Integration
Work across teams for real-world AI applications.
AI Deployment & Scaling
Build, monitor, and scale AI in production.
Security & Observability
Ensure AI solutions are robust and reliable.
What are ‘Agentic Workflows?’
Agentic workflows in AI engineering automate LLM development, deployment, and monitoring, allowing engineers to focus on scalability, efficiency, and innovation.
Function:AI Model Development
Automating model training, fine-tuning, and optimization for efficient AI workflows.
Old Way
Engineers manually preprocess data, fine-tune models, and conduct extensive hyperparameter tuning.
Agentic Workflow
AI automates data processing, retrieval-augmented generation (RAG), and real-time model optimization.
Function: AI Deployment & Scaling
Seamlessly deploying AI models with automated scaling and real-time monitoring.
Old Way
Engineers manually handle CI/CD pipelines, model versioning, and performance monitoring.
Agentic Workflow
AI enables automated deployment using FastAPI, real-time scaling, and monitoring with MLFlow.
Function: AI Systems & Infrastructure
Enhancing reliability and efficiency with AI-powered cloud and database automation.
Old Way
Engineers spend hours debugging API integrations, managing cloud infrastructure, and troubleshooting issues.
Agentic Workflow
AI automates API integration, vector DB management (e.g., Pinecone), and cloud resource optimization.
A Step-by-Step Path to AI Mastery
6-Month Online Deep-Dive
Level 1: Foundations of LLMs & AI Building Blocks
Level 2: Agentic AI Frameworks for AI Solutions
Level 3: Beyond Text LLMs – Exploring Vision Models
Level 4: Building & Deploying AI Solutions
Level 1: Foundations of AI and LLMs
What will you learn?
Evolution of AI, Introduction to Large Language Models (GPT, PaLM, LLaMA), Real-world applications (chatbots, content creation), Development environment setup.
Prompt engineering fundamentals, Task-specific prompts (summarization, classification), Utilizing OpenAI API, Solution architecture with routing.
Model fine-tuning principles, Specialized task training, Dataset preparation, and Fine-tuning practices.
Introduction to embeddings, Vector databases, Semantic search, and Retrieval-Augmented Generation (RAG) workflows.
Tools
Python, Jupyter Notebooks, VS Code, OpenAI, Transformers, LangChain
Level 2: Agentic AI Frameworks for AI Solutions
What will you learn?
LangChain applications, AI agent fundamentals, Task routing, Multi-step query management.
Advanced AI workflows, Utilizing LangGraph, Implementing robust AI solutions, and Designing knowledge assistants.
Evaluation frameworks (TruLens, DeepEval), LLM ReAct for reasoning and action, Explainability techniques, Using LlamaIndex for evaluation.
Basics of Reinforcement Learning, Fine-tuning with RL, Practical RL applications, Case studies in RLHF.
Tools
LangChain, LangFlow, LangGraph, TruLens, DeepEval, LlamaIndex
Level 3: Beyond Text LLMs – Exploring Vision Models
What will you learn?
Introduction to Hugging Face vision models, Image-to-text conversions, Text-to-image conversions, and Leveraging vision models for applications.
Understanding image embeddings, Basics of multimodal embeddings, Introduction to CLIP, Text and image data integration.
Customizing vision models, Task-specific model tuning, Advanced fine-tuning techniques, and Application-specific model optimization.
Overview of diffusion models, Understanding DALL-E, Creative applications of diffusion models, Generating custom art with AI.
Tools
Hugging Face vision models, CLIP, multimodal embedding tools, DALL-E
Level 4: Building & Deploying AI Solutions
What will you learn?
High-level AI application design, Tech stack and solutioning, Scalable design using cloud services, and Architectures for real-world AI solutions.
Low-level design and code organization, FastAPI for AI services, UI/UX integration with Streamlit, Utilizing vector databases.
Deploying AI applications with Docker, Implementing CI/CD pipelines, Real-time application deployment, and Production deployment of AI models.
Using observability tools, Tracking metrics in AI deployments, Managing AI solution performance, and Monitoring deployed AI applications.
Tools
Cloud , Streamlit, MLFlow, observability tools
& More!
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You can find more details about the course according to the sales page.
