Succeeding at AI with Agile
Artificial intelligence is a wave that is already here and transforming enterprises, promising a future vastly different from our experiences, environment, and work today. If your agile team isn't already touching backlog items related to AI, it soon will be - or a team better prepared to handle AI work may intercept what will become an increasingly larger share of enterprise workloads.
Agile teams have the imperative to start working with AI technology and tools, incorporate an AI focus into their current and future backlogs, and begin delivering more than just working code: Working models, experiments, and cognitive services that augment and expand the capabilities of their applications and enterprises.
This new world includes new team roles, like Data Scientist; new development pipeline tools: Automated data ingest and scrubbers, data lakes, algorithm libraries, test and training data repositories, model registries, AI labs; and new processes and tasks, such as cognitive service integration, data labeling, model training, testing, and deployment, and algorithm performance evaluation.
This talk describes the tools, infrastructure, roles, and agile process changes required for taking on AI epics so that your team can can get started on its own AI journey.
Outline/Structure of the Talk
I. The AI imperative
A. The wave is here - current adoption trends for cognitive capabilities and services
B. Enterprise readiness
i. AI adoption is not a product purchase - the ubiquitous nature of AI
ii. Defining what is possible given where you are, and how to plan for growth
II. Nuts and bolts of building an intelligent system
A. AI components
i. Cognitive services: Natural language processing, visual recognition, translation, speech-to-text, text-to-speech
ii. Machine learning: Unsupervised vs. supervised learning, model ensembles, model QA
iii. Data management: Data lakes, data cleaning, labeling, test sets, training sets, model registries, deployment patterns
B. Identifying AI opportunities
i. Process decomposition
ii. Data analysis
iii. Interviews/surveys - "day in the life"
C. Cognitive solution approaches
i. Third-party cognitive service integration
ii. Machine learning model pipeline
III. Put the AI in Agile
A. AI-specific agile team resources
i. Data intake management
ii. Data scientist
iii. Enterprise system-and-data coordination
iv. AI QA
v. AI security
B. Practices for discovery, elaboration, and construction
i. AI as a portfolio investment theme
ii. Discovery buckets: Process, data, and artificial intelligence
iii. Constructing field-able experiments
C. "Deliver working AI" through DevOps and development pipeline enhancements
i. AI infrastructure
ii. Devops processes with AI enhancements
iii. Feedback loops and measuring percentages
An understanding of the Agile practices, team roles, processes changes, and devops/pipeline changes need to incorporate AI development efforts into an Agile team structure.
Current or new Agile practitioners ready to incorporate strategies to attack the work of the future.
Prerequisites for Attendees
Basic Agile principles, execution experience is helpful but not required.
schedule Submitted 1 year ago
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