WeAreDevelopers LIVE • May 26, 2021

Machine Learning for Software Developers (and Knitters)

Kris Howard

Can an AI reverse-engineer a knitting pattern? Step into the Software 2.0 paradigm and learn how to build AWS computer vision pipelines without a data science degree.

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#1 about 3 min

Approachable machine learning concepts for software developers

Bypassing complex mathematical theory allows developers of any background to conceptualize and integrate basic machine learning capabilities.

#2 about 7 min

Commercial applications of modern artificial intelligence and machine learning

Analyzing advanced implementations in biology and delivery operations illustrates how predictive algorithms solve uniquely complex logistics challenges.

#3 about 4 min

Transitioning from deterministic programming to probabilistic machine learning frameworks

Adapting to probability-based outputs rather than discrete logic transforms how traditional engineers must structure conditional branching.

#4 about 3 min

Exploring the tiered architecture of modern machine learning stacks

Breaking down the cloud artificial intelligence stack enables practitioners to match deployment options directly to their technical experience levels.

#5 about 6 min

Translating physical object reverse engineering into image classification problems

Approaching physical structure identification as an image classification task creates a pathway for digitizing real-world textures.

#6 about 6 min

Automating data collection and managing crowdsourced training image sets

Establishing automated cloud storage pipelines mitigates the overwhelming file cleanup tasks generated by crowdsourcing raw multi-media data.

#7 about 4 min

Curating accurate training labels via managed workflows and public workforces

Designing restrictive categorization workflows for human annotators minimizes the systemic inaccuracies caused by subjective visual interpretations.

#8 about 4 min

Executing custom image classification training jobs in managed environments

Executing structured validation cycles within comprehensive cloud environments simplifies the notoriously difficult procedure of deploying scalable inference endpoints.

#9 about 6 min

Improving model inference accuracy via data augmentation and parameter tuning

Introducing irrelevant visual clutter and optimizing hyperparameters resolves the severe false positive rates commonly found in early vision models.

#10 about 11 min

Evaluating tradeoffs between fully managed artificial intelligence and custom pipelines

Evaluating pre-packaged vision classification services against flexible scientific platforms highlights the crucial balance between administrative control and developmental speed.

Matching moments

4:44 min

Introduction to prototyping and building practical AI applications

Krzysztof Cieślak

Krzysztof Cieślak · WWC 2024

1:20 min

Uncovering the hidden technical debt in machine learning

Nico Axtmann · WWC 2022

4:15 min

Introduction to artificial intelligence driven development

Natalie Pistunovich · LIVE

15:08 min

Audience questions on practical machine learning operational strategies

Lina Weichbrodt · LIVE

3:07 min

Designing an automated machine learning deployment blueprint

Elisabeth Günther

Elisabeth Günther · WWC 2024

3:28 min

Implementing continuous delivery architecture for machine learning

Dubravko Dolic +1 · WWC 2021

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