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Data Science Guide

Read more about AI, machine learning, data science and business intelligence and the role they play in the data cloud.

Using MLOps to Quickly Deploy and Sustain Machine Learning Pipelines

Machine learning operations (MLOps) is a process used to streamline and standardize the entire ML lifecycle for rapid deployment and maintenance of ML pipelines.

How a Data Ingestion Framework Powers Large Data Set Usage

Learn about the different types of ingestion and how they relate to data integration and its methods, including batch and streaming ingestion.

Types of Regression Models in Machine Learning

We’ll look at what regression analysis is, highlighting seven popular regression models with examples of the real-world business problems they solve.

LLM Inference: From Input Prompts to Human-Like Responses

Explore how large language models (LLMs) use inference to convert input prompts into contextual responses, and discover techniques for creating high-performing AI models.

AI Threat Modeling: A Proactive Approach to Securing AI Systems

AI threat modeling helps securing AI systems against attacks, data poisoning, and model stealing. Learn to address security, privacy, and ethical risks.

AI Data Analytics: Insights at Scale

Discover how AI enhances data analytics by improving predictive insights, automating data processing, and enabling large-scale analysis.

Using Multimodal Models to Build More Capable AI Systems

Learn how multimodal models (LMMs) enhance AI by integrating images, audio, and video, and explore their benefits and real-world applications across industries.

AI in DevOps: Optimizing Software Development and Delivery

In this article, we’ll explain how DevOps teams can use AI, and we’ll share a five-step process for introducing AI into your DevOps workflows.

Scala vs. Python for Data Science

In this article we explore the similarities and differences between Scala and Python, and learn how data scientists can combine the two effectively.