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WeAreDevelopers LIVE • Feb 1, 2022

MLOps and AI Driven Development

Natalie Pistunovich

AI code generation is turning developers into reviewers, shifting the real engineering challenge to MLOps. Discover why Go is the perfect language for building secure, AI-driven cloud infrastructure.

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#1 about 5 min

Introduction to artificial intelligence driven development

Setting the stage for combining DevOps and machine learning operations with practical tooling recommendations.

#2 about 6 min

Brief history of artificial intelligence research and funding

How the field evolved from symbolic logic to neural networks driven by explosive data availability.

#3 about 4 min

Evolution of large language models and natural language processing

Tracking the rapid development of pre-trained transformer engines boasting zero and few-shot learning capacity.

#4 about 5 min

Parameter growth in modern massive artificial intelligence models

Comparing trillion-parameter transformer architectures to human brain cerebral cortex functional capacities.

#5 about 10 min

Translating natural language to executable software with Codex

Demonstrating how highlighting text can automatically generate unit tests, bash commands, and functioning frontend structures.

#6 about 2 min

Mastering prompt engineering for effective machine interactions

Providing proper context parameters prevents unexpected inference patterns within token constraint boundaries.

#7 about 5 min

Leveraging the Go programming language for scalable infrastructure

How structural concurrency and cross-compilation binaries simplify deploying machine-generated operations systems natively.

#8 about 5 min

Embracing automation and no-code tooling in software engineering

Offloading boilerplate code and documentation burdens gives engineers breathing room to focus on complex algorithmic challenges.

#9 about 4 min

Maintaining and monitoring machine learning models in production

Deploying supporting infrastructure for data collection and model iteration ensures consistent deployment behaviors.

#10 about 10 min

Audience questions on model security and continuous fuzzing

Addressing concerns around AI-generated vulnerability detection, data provenance tracking, and pipeline component isolation setups.

Matching moments

2:08 min

Essential engineering roles in the generative AI space

Mary Grygleski Mary Grygleski · LIVE

3:01 min

Balancing artificial intelligence tools with foundational software engineering skills

Tim Ruscica · Coffee With Developers

4:19 min

Introduction to DevOps for AI and MLOps

Aarno Aukia · LIVE

1:22 min

Overcoming initial skepticism of AI code generation

Jeff Blankenburg Jeff Blankenburg · WWC Europe 2026

5:01 min

Leveraging large language models for code optimization and development

Stephan Gillich Stephan Gillich +3 · WWC 2024

2:20 min

Integrating generative AI into software development workflows

Chris Wysopal Chris Wysopal · WWC 2024

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