Learning Tracks: Working With AI
AI seems to be infiltrating everything we do, and the mechanics behind the scenes are obscure to even the most technical among us. This learning track will break down what concepts and tools you'll need to understand AI, different types of models, and how to use them day to day to impress your boss.
- Most data teams use a special type of code notebook to explore data and build AI models.
- A language-based ML model named GPT-3 took the world by storm.
- Retrieval Augmented Generation (RAG) is an efficient way for ML models to fine tune themselves to your data.
COMING SOON
- For anyone who has seen or used ChatGPT or DALL-E, ML and AI have been advancing quickly over the past few years.
- There are plenty of useful ML models that aren't made by OpenAI that you can use in your day to day.
- Vector databases are specially built data stores for powering AI models.
COMING SOON
- Though all the rage, benchmarks are a one sided and inefficient way to measure AI model performance.
- How various LLMs like Gemini and ChatGPT stack up when it comes to routine tasks like generating LinkedIn posts and emails.
- OpenAI is the most popular provider of generative AI models like GPT-4 and DALL-E.
- Databricks is a tool for running Spark jobs, basically ETL for big data.
PAID
Technically learning tracks help make the world of software simple and digestible, so you can be better at your job. There are more on the way!
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