Data Engineering: AI Game Changer
Data engineering is the unsung hero fueling the rapid growth and consumption of artificial intelligence. It transforms AI's potential into reality, driving digital innovation and reshaping the world. In this discussion, we delve into the world of data engineering, exploring how it unlocks and enables democratized use of Artificial Intelligence. Prepare for a deep dive into the mysteries of AI, through the lens of both the data and engineering that makes it possible.
Vinoo Ganesh is a recognized authority in software and data engineering. Starting his journey at Palantir Technologies, Vinoo has held leadership roles at Veraset and Citadel, contributing significantly to data services and investment platforms. He is an advisor to startups like Databand.ai and Horangi, and the architect of the LinkedIn Learning course 'Hands-On Introduction: Data Engineering', where he imparts actionable knowledge on data engineering principles and tools.
Episode Links
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Training Courses by Vinoo: https://go.raybo.org/TFIT2801
Vinoo Ganesh Entrepreneur Profile: https://go.raybo.org/TFIT2802
AI and Competitive Edge Discussion: https://go.raybo.org/TFIT2803
Unstructured Data Solutions: https://go.raybo.org/TFIT2804
Snowflake Data Cloud Overview: https://go.raybo.org/TFIT2805
Anthropic AI Home: https://go.raybo.org/TFIT2806
DIY Superconductivity Race: https://go.raybo.org/TFIT2807
Apache Airflow Tools: https://go.raybo.org/TFIT2808
New Work Management Platform: https://go.raybo.org/TFIT2809
Social Platform Saturn's Fund: https://go.raybo.org/TFIT2810
Conversations with Claude AI: https://go.raybo.org/TFIT2811
ChatGPT Overview: https://go.raybo.org/TFIT2812
Democratizing ChatGPT: https://go.raybo.org/TFIT2813
Pinecone Vector Search Tool: https://go.raybo.org/TFIT2814
Apache Cassandra Documentation: https://go.raybo.org/TFIT2815
AI-native Embedding Database: https://go.raybo.org/TFIT2816
Chroma AI Tools: https://go.raybo.org/TFIT2817
Index
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00:00 Intro
02:37 Data as the ultimate disinfectant
04:51 Transition from philosophy to computer engineering
06:46 Impact of philosophy and finance on AI
07:58 Assessing companies' readiness for AI
09:28 AI readiness mirrors data strategy
10:48 Long-term AI implementation vs. quick
12:05 The power and challenges of generative AI
12:42 Trust in generative AI
14:26 The creative potential of generative AI
13:50 Introduction to Generative AI
14:40 Advancements in Model Size
15:51 Challenges of Context Window
17:05 Applications of Generative AI
18:56 Limitations of Current AI Models
20:04 AI Capabilities in Art
21:56 Infrastructure Challenges in Data Eng.
23:52 Definition of Data Engineering
25:41 Paths to Becoming a Data Engineer
26:57 Unstructured Data Engineering
28:20 Rise of Generative AI Models
29:10 Advice for Getting into Data Engineering
31:10 Specializations in the Data Field
32:36 Importance of Data Engineering in Orgs
33:27 Need for Expertise in Managing Data
34:39 Operationalizing Dark Data
35:03 Changing Role of Data Engineers with AI
36:11 Work to Contextualize AI Models
37:25 The Future of Data Engineering
38:53 Small Companies' Opportunities with AI
42:08 Competing with established companies
43:21 Opportunities and tools for differentiation
44:46 Defining a data strategy
46:05 Opportunities for smaller companies
47:15 Differentiation through unique data
47:58 Building around AI capabilities
49:09 Lack of standards in the AI space
50:38 Future role of data engineers
51:36 Leveraging the open-source ecosystem
52:46 Rapid pace of AI advancements
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