Speaker Series
Schedule- details
Speaker Series Topics
​
​
​
1. Data management in an AI first organization - data as the “new gold”
​​
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
​​
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
​​
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
​​
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
​​
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
​​
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
​​
7. Skills and lifelong learning for an AI first world
​​
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
​​
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.