Events

• Identifying Pain Points: Focus on solving real challenges with measurable outcomes.
• Build or Buy?: Choose the right approach based on resources.
• AI for SMBs: Accessible, scalable, and affordable solutions for small businesses.
• Actionable Steps: Start small, iterate, and collaborate with trusted AI partners.

AI drives efficiency, innovation, and competitiveness for businesses of all sizes.

• AI speeding up document verification and fraud detection, with real-world examples from clients.
• Automation improving claims processing and real-time fraud detection.
• Addressing challenges like low-resource languages and infrastructure gaps in Africa.

Her session inspired attendees to use AI to drive inclusive, impactful solutions in the FinTech industry.

• Overview of AWS Services in Gen AI
• High Impact Gen AI Use Cases in FinTech
• Gen AI Use Case Considerations

In this event, the following topics are discussed:

• Embedding AI into the Organization Strategy
• Data Governance Enhances AI implementations
• Challenges faced when implementing AI
• Practically implementing AI solutions
• Creating an Organization AI Culture

By integrating proprietary data, businesses are able to fine-tune these models for next-level customization, providing tailored solutions that create unique strategic advantages.

In this discussion, Greg covers:

• The transformative potential of GPT models
• How integrating proprietary data enhances model customization
• The strategic advantages of tailored solutions

In this interview, she covers:

• The major benefits of enterprise ML platforms
• How businesses can maximize the value of ML platforms
• The importance of multi-skilled teams in ML development
• Tips to accelerate delivery while retaining quality
• How data teams can demonstrate the ROI of enterprise ML platforms

Merelda was invited by the Mineworks Investment Company (MIC) to discuss the Machine Learning, Analytics, and Data (MAD) Landscape. As an AI expert, she addressed:

• The global AI landscape and its relevance to South Africa
• Challenges facing South African corporates
• Emerging global and local trends
• Monetising data and AI
• Investment opportunities

In this presentation, you’ll learn:

• 3 Key Challenges of Building Enterprise ML Platforms
• 3 Key Learnings from Building Enterprise ML Platforms with Our Customers
• 6 Steps on How to Build Enterprise ML Platforms Today

In this presentation, they covered:

• The need for efficient receipt information extraction
• Utilising OCR and image processing techniques
• Insights into creating a practical Proof of Concept (PoC) for enhanced receipt tracking
• How image processing methods can simplify personal finance organization by extracting and categorizing information from receipts

In this presentation, Ruan covered:

• Clever techniques using Python to generate synthetic data
• Maintaining the integrity of the expected data distribution
• Insights on how synthetic data can significantly shorten the lifecycle in developing data products

In this presentation, she covered:

• Identifying opportunities to leverage AI
• Starting an AI project in your startup
• Readiness for embracing AI technology

In this presentation, she covered:

• The readiness for AI adoption
• Common challenges and advice for startups
• Building a data and AI roadmap as a journey
• Case studies illustrating AI maturity
• A practical framework to build a custom AI strategy

In this presentation, he covered:

• The global AI/ML landscape and its impact on business
• Challenges in achieving rapid time-to-value
• Defining “value” in AI/ML investments
• Strategies for generating business impact, competitive advantage, and enhanced decision-making
• Overcoming business, process, technology, and data challenges

Additionally, we’ve published a blog post on this topic.

In this presentation, she covered:

• The necessity of MLOps for a broad range of companies
• Principles over technology and conventions over configurations
• A reasonable MLOps stack to ship reliable ML faster
• Practical steps and decision frameworks for MLOps implementation
• Demonstrations of collaboration and endpoint deployment on GCP

In this presentation, she covered:

• Challenges enterprises face in scaling AI projects from proofs of concept to full deployment
• Common deployment failures
• Introduction to MLOps principles
• Practical steps for setting up successful AI deployments