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Sandip Das
Sandip Das

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Basic AI & ML Concepts for MLOps Engineers

🚀 Basic AI & ML Concepts for MLOps Engineers

Many engineers misunderstand AI & ML concepts before jumping into MLOps. Let’s clear up those fundamentalsRIGHT NOW!

🤖 What is AI?

Artificial Intelligence (AI) simulates human intelligence in machines to perform tasks like learning, reasoning, and problem-solving.

🔍 What is ML?

Machine Learning (ML) is a subset of AI that enables systems to learn from data and make predictions or decisions without explicit programming.

📊 What is an ML Model?

AnML Model is a mathematical representation trained on data using an algorithm to recognize patterns, make predictions, or decisions without explicit programming.

🔥 ML Model Training Methods:

  1. Supervised Learning - Learns from labeled data (e.g., regression, classification).
  2. Unsupervised Learning - Identifies patterns in unlabeled data (e.g., clustering, dimensionality reduction).
  3. Reinforcement Learning - Trains agents to make sequential decisions by maximizing rewards.
  4. Semi-Supervised Learning - Mixes labeled and unlabeled data to improve accuracy.
  5. Deep Learning (DL) - Uses multi-layered neural networks for complex feature learning.
  6. Online Learning - Continuously updates the model with new data.
  7. Transfer Learning - Adapts knowledge from one task to another.
  8. Ensemble Learning - Combines multiple models to enhance accuracy.

🏗 Foundation Models (FMs)

Foundation Models (FMs) are large-scale AI models trained on massive datasets, making them adaptable to multiple tasks like NLP, image generation, and coding.

Key Characteristics:

Pretrained on massive datasets (text, images, code, videos).

General-purpose capabilities (e.g., GPT-4, Stable Diffusion, Code Llama).

Fine-tuned for custom use cases (e.g., a healthcare chatbot trained on medical literature).

Scalability & API access (AWS, Azure, Google Cloud).


🔥 Famous ML Models

  • DeepSeek R1 - High-performance AI for reasoning & language tasks.
  • Sonet - Efficient LLM for resource-constrained environments.
  • Meta's LLaMA - Open-weight AI for research & deployment.
  • OpenAI's GPT - Powers ChatGPT & generative AI apps.
  • Google's Gemini - Multimodal AI for text, images, & reasoning.
  • BERT - Google's NLP model for search ranking & text classification.
  • Claude (Anthropic) - AI model optimized for safety & accuracy.

🚀 Hugging Face: The Open-Source AI Hub

Hugging Face is an open-source AI platform providing pretrained AI models, datasets, and developer tools for NLP, computer vision, and beyond.

Key Features:

✅ Hosts thousands of open-source AI models.

✅ Provides theTransformers library for NLP.

✅ Supports fine-tuning & deployment via API.

✅ Enables AI research & collaboration.


🧠 LLMs: Large Language Models

Large Language Models (LLMs) are deep learning models trained on vast text datasets to understand and generate human-like text.

How LLMs Work?

🔹Training on massive datasets (books, websites, articles).

🔹Tokenization (breaking text into smaller units).

🔹Self-Attention Mechanism (understanding context in sentences).

🔹Billions of parameters (e.g., GPT-3 has 175B parameters).

LLM Limitations:

Hallucinations - May generate incorrect information.

Bias - Reflects biases in training data.

Computational cost - Requires massive power.

Context limitations - Limited memory in long conversations.


🎨 Generative AI: Content Creation with AI

Generative AI can create text, images, code, music, and videos based on learned data patterns.

How It Works?

  1. Pre-trained on massive datasets.
  2. Uses transformer-based architectures (GPT, Stable Diffusion).
  3. Prompt-based generation (input text → AI generates content).
  4. Fine-tuning for specific domains (e.g., DevOps automation, cybersecurity).

Key Generative AI Models:

  • LLMs - GPT, LLaMA, Falcon (for text & code generation).
  • Image Generators - DALL·E, MidJourney, Stable Diffusion.
  • Audio & Music - OpenAI's Jukebox, Google's MusicLM.
  • Video - RunwayML, Sora.

🔍 RAG: Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) enhances LLM responses by retrieving external data before generating answers.

How RAG Works?

  1. User Query → Model receives a question.
  2. Retrieval Step → Searches external sources (DBs, APIs).
  3. Augmentation Step → Retrieved data is fed into the LLM.
  4. Generation Step → Model generates an improved response.

💡 This technique improves accuracy, context, and reduces hallucinations.


☁️ Amazon Bedrock: GenAI on AWS

Amazon Bedrock is a fully managed AWS service for building scalableGenerative AI applications usingFoundation Models (FMs).

Why Use Amazon Bedrock?

Access to multiple FMs (Claude, LLaMA, Cohere, Stability AI).

Fine-tuning & RAG support (improve accuracy with enterprise data).

Seamless AWS integration (S3, Lambda, SageMaker, DynamoDB, RDS).


🏁 Next Steps: Data Extraction, Validation & Preparation for MLOps

This guide covered AI/ML fundamentals for MLOps Engineers. Next, we’ll dive intoData Extraction, Validation & Preparation for MLOps! 🚀

👉FollowSandip Das for more updates!

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AWS Container Hero | Sr Cloud Solutions Architect | DevOps Engineer: App + Infra | Full Stack JavaScript Developer
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