Towards AI

AUGUST 24, 2023

Common mistakes and misconceptions about learning AI/ML Markus Spiske on Unsplash A common misconception of beginners is that they can learn AI/ML from a few tutorials that implement the latest algorithms, so I thought I would share some notes and advice on learning AI. Trying to code ML algorithms from scratch.

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Neural Network Software Engineer Algorithm Machine Learning 98

Pickl AI

JANUARY 29, 2024

As we navigate this landscape, the interconnected world of Data Science, Machine Learning, and AI defines the era of 2024, emphasising the importance of these fields in shaping the future. ’ As we navigate the expansive tech landscape of 2024, understanding the nuances between Data Science vs Machine Learning vs ai.

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Data Science ML Machine Learning Artificial Intelligence 52

Towards AI

MAY 30, 2024

Sonam Gupta, PhD, Developer Advocate, Experienced Data Scientist, PhD Data Science and Podcast host “This textbook not only explores the critical aspects of LLMs, including their history and evolution, but it also equips AI Engineers of the Future with the tools and techniques that will set them apart from their peers.

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Prompt Engineer Prompt Engineering LLM NLP 108

ODSC - Open Data Science

FEBRUARY 13, 2025

He also demonstrated workflow automation using Koo.ai, highlighting how AI-driven knowledge extraction can enhance research dissemination. The workshop provided practical insights for AI engineers and content creators looking to streamline content production with intelligent automation.

Large Language Models 52

Large Language Models AI AI Automation 52

TheSequence

JANUARY 19, 2024

You probably don’t need ML engineers In the last two years, the technical sophistication needed to build with AI has dropped dramatically. At the same time, the capabilities of AI models have grown. ML engineers used to be crucial to AI projects because you needed to train custom models from scratch.

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Prompt Engineer Prompt Engineering Generative AI Large Language Models 59

ODSC - Open Data Science

FEBRUARY 25, 2025

Topics Include: Agentic AI DesignPatterns LLMs & RAG forAgents Agent Architectures &Chaining Evaluating AI Agent Performance Building with LangChain and LlamaIndex Real-World Applications of Autonomous Agents Who Should Attend: Data Scientists, Developers, AI Architects, and ML Engineers seeking to build cutting-edge autonomous systems.

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Data Scientist Machine Learning Large Language Models ML Engineer 52

ODSC - Open Data Science

NOVEMBER 25, 2024

In-person on Wednesday, Nick Becker, Product Leader in GPU-accelerated Data Science at NVIDIA discussed the next phase of accelerated computing; Chip Huyen, Storyteller at Tep Studio discussed AI engineering; and Dr. Ali Arsanjani, the Director of Applied AI Engineering at Google Cloud discussed infusing and scaling generative AI into businesses.

Data Science 52

Data Science AI Engineer Machine Learning Prompt Engineering 52

Pickl AI

JULY 14, 2023

However, symbolic AI faced limitations in handling uncertainty and dealing with large-scale data. Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming. Artificial Intelligence and the Future of Humans 1.

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Neural Network Software Engineer Artificial Intelligence Artificial Intelligence 52

ODSC - Open Data Science

FEBRUARY 21, 2025

Learn how to create benchmarks, catch hallucinations, select meaningful metrics, and monitor AI agent failure modes, turning evaluation into a key driver for success in your AI applications. This session will equip business leaders with actionable insights for driving AI transformation in their organizations.

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Data Scientist Software Engineer LLM Neural Network 52

Bugra Akyildiz

MARCH 24, 2024

In the post, they talk about advantages and diadvantages of Metaflow: Advantages User-friendly API: Metaflow offers a human-readable API that simplifies the process of building and managing ML workflows. Generates neighbors using auxiliary model and measures change in likelihood. More details and these approaches are outlined in the paper.

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Machine Learning Algorithm Data Scientist LLM 52

AWS Machine Learning Blog

DECEMBER 2, 2024

As LLMs continue to expand, AI engineers face increasing challenges in deploying and scaling these models efficiently for inference. About the Authors Lokeshwaran Ravi is a Senior Deep Learning Compiler Engineer at AWS, specializing in ML optimization, model acceleration, and AI security.

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Large Language Models Auto-complete Machine Learning LLM 102