Speaker Series Topics

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1. Data management in an AI first organization - data as the “new gold”

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Data types and sources, e.g. online data, offline data, public vs. private data sources, IoT and sensors, server log data, hidden data

Data extraction, transformation, and loading (ETL), batch vs. streaming data, and tools, e.g. Informatica, Kafka

Data manipulation, exploration, interpretation and explanatory data science and tools, e.g. SQL, SaS

Data visualization and tools, e.g. Tableau, Looker

 

2. Data science and machine learning to power an AI first organization

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Fundamentals of data science - how to clean data, model, train, test, and refine

Most relevant data science and machine learning models, and when to apply

Deep learning, most relevant networks (RNNs, CNNs, LSTMs, GANs), and applications (image, video, speech and language analytics)

Data science and machine learning languages and tools, incl. Python (with Jupyter, Pytorch), R (with Rstudio), Tensorflow

How to define, evaluate the business value, prioritize data science projects and how to cast into data driven decisioning

Pitfalls and risks, e.g. bias, over-training, lack of data, adversarial attacks, deep fakes

 

3. IT architecture and infrastructure for the AI first organization

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The analytics-first architecture for real-time and batch data processing

Data lakes and single source of truth

Public vs private cloud, migration strategies, and public cloud providers, focus AWS, Azure, GCP

Scalability and future proofness with APIs and microservices

Data collection, every end-point as data source

 

4. New ways of working in an AI first organization

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How to assess the AI maturity of your organization

Practices for AI projects, e.g. Agile, Kanban, DevOps

Tools to support agile working, e.g. Jira

How to run a data science project - from problem statement to implementation and economic impact assessment; roles and skills for data driven projects 

Culture transformation - how to instill an AI first mindset across the organization, how to reach “AI @ scale”

 

5. User experience in an AI first organization

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User experience design and design thinking

User experience design process - from idea to prototype to MVP product and user testing 

User experience design tools , e.g. Abstract, Sketch, InVision, Zeplin

Data driven continuous user experience improvement

 

6. Data privacy, security and ethical aspects for an AI first organization

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Regulatory frameworks in Sri Lanka, South Asia and other regions, e.g. GDPR; data classification and PII (personal identifiable information)

Privacy and security - minimum baseline and beyond; secure data collection, storage, processing, and data sharing; consumer protection and rights, intellectual property

Privacy and security audits, certifications, and organizational training

Security - infrastructure, processes and tools, incl. Security operating centre

Ethical aspects of AI

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7. Skills and lifelong learning for an AI first world 

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The future workplace and required skills 

Talent sourcing, upskilling and new cooperation models 

Self-learning, online resources and certificates, eg AWS, Google, Facebook, IBM, Coursera, deeplearning.ai

Running software and AI projects adjacent to organizations and the power of open source, e.g. Kaggle, Github

 

8. New technologies fuelling the AI first organization and AI business models

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IoT - the internet of everything (data everywhere), edge computing and new new AI architectures (central modeling, de-central / autonomous operation)

Massive connectivity, incl. 5G and use cases.