Understanding AI

Generative AI relies on deep learning models to create new content.

Machine learning (ML) trains a computer to find patterns and make predictions.

Deep learning processes data using an algorithm inspired by the human brain.

A foundation model is a type of ML model pretrained to perform a range of tasks.

Large language models (LLMs) understand and generate human language.

AI infrastructure helps data scientists and developers work efficiently.

MLOps integrates ML models into software development processes.

AIOps use AI to enhance or partially replace a range of IT operations processes and tasks.

Different industries benefit from different applications of AI/ML.

Across the healthcare market, AI can serve life sciences, providers, and payers.

Service providers use AI to overcome network complexity, operational inefficiencies, and market competition.

AI can improve customer service and operational efficiency in banking.

Edge AI lets devices make smarter decisions faster.

LLMOps manages and automates the lifecycle of LLMs.

An AI platform is a collection of technologies to develop, train, and run machine learning models.

InstructLab simplifies the process of customizing LLMs with private data.

Retrieval-augmented generation (RAG) links external resources to an LLM for a more accurate output.

Intelligent applications use AI to augment a human workflow.

Fine-tuning and RAG both aim to improve LLMs, but use different methods. RAG avoids altering the model, while fine-tuning requires adjusting its parameters.

Granite is a series of LLMs created by IBM for enterprise applications that support gen AI use cases like language and code.

Agentic AI is a software system designed to interact with data and tools in a way that requires minimal human intervention.

Compare predictive AI and gen AI to find out which best fits your AI use cases.

LLMs and small language models (SLMs) are both types of AI systems trained to interpret human language, including programming languages.

AI inference is when an AI model provides an answer based on data.

vLLM is a library of open source code that helps large language models (LLMs) perform calculations more efficiently and at scale.

LoRA and QLoRA both help fine-tune LLMs more efficiently, but manipulate the model and utilize storage differently.

Parameter-efficient fine-tuning (PEFT) is a set of techniques that adjusts only a portion of parameters within an LLM to save resources.

AI at the enterprise comes with its own challenges and benefits on a larger scale.

MaaS is an approach to delivering AI models as shared resources, allowing users within an organization to access them on demand.

Learn how each technology works, their unique strengths, and how they can collaborate for smarter solutions.

AI security defends AI applications against malicious attacks that weaken workloads and manipulate data.

Model Context Protocol (MCP) is an open source protocol that enables 2-way connection and standardized communication between AI applications and external services.

llm-d is an open source framework to speed up LLM inference at scale.

Distributed inference lets AI models process workloads more efficiently by dividing the labor of inference across a group of interconnected devices.