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WeAreDevelopers LIVE • Nov 30, 2020

Leverage Cloud Computing Benefits with Serverless Multi-Cloud ML

Linda Mohamed

Raw data once caused an AI to classify a human as a juggling ball. Avoid this by chaining AWS, Azure, and GCP into a serverless machine learning pipeline.

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#1 about 6 min

Understanding foundational data science and artificial intelligence concepts

Differentiating computer science, data science, and machine learning lays the groundwork for practical artificial intelligence implementation.

#2 about 3 min

Explaining the standard machine learning development cycle

Fetching, preparing, training, and evaluating data forms the core iterative loop of machine learning model generation.

#3 about 4 min

Evaluating existing pre-trained models and mathematical calculations

Existing open-source machine learning solutions and theoretical math often fail to address practical real-world variables.

#4 about 3 min

Manual data pre-processing using custom vision platforms

Training a custom vision model manually reveals the limitations and time constraints of non-automated data labeling.

#5 about 2 min

Exploring data collection challenges from user-generated platforms

Scraping visual data from social platforms highlights the need for robust data acquisition workflows.

#6 about 4 min

Automating feature engineering to handle messy data

Uncleaned social media inputs cause model recall metrics to drop, proving the necessity of comprehensive data cleaning.

#7 about 3 min

Connecting multi-cloud services for automated data preparation

Utilizing an event-driven flow across public cloud platforms scales machine learning data cleaning processes efficiently.

#8 about 3 min

Orchestrating serverless workflows with cloud messaging services

Cloud messaging queues and serverless functions format data automatically for cross-platform model training.

#9 about 3 min

Comparing cloud provider model training and evaluation accuracy

Contrasting model evaluation across different cloud vendors demonstrates minor performance differences in image recognition.

#10 about 4 min

Deploying machine learning models locally and remotely with containers

Packaging exported machine learning models into Docker configurations allows for seamless multi-cloud deployment.

#11 about 3 min

Defining tech democratization across serverless cloud environments

Leveraging multiple managed cloud infrastructure platforms empowers software engineers to bypass deep domain expertise requirements.

#12 about 4 min

Reviewing architectural prototype lessons and future deployment pipelines

Understanding that manual implementation prototypes lack maintainability emphasizes the requirement for automated continuous delivery pipelines.

Matching moments

5:05 min

Exploring hybrid models, monoliths, and serverless computing

Michael Eisenbart · LIVE

2:18 min

Exploring the tiered architecture of modern machine learning stacks

Kris Howard · LIVE

3:43 min

Evaluating cost effectiveness and summarizing serverless machine learning strategies

Marek Suppa · LIVE

2:41 min

Transitioning artificial intelligence infrastructure into scalable commodity cloud services

juarezjunior juarezjunior · WWC 2024

3:08 min

Comparing containers, cloud architectures, and serverless computing

Dominik Kress · LIVE

3:31 min

Practical learnings from deploying containerized AI solutions

Sebastian Rhode Sebastian Rhode · WWC 2024

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