Code To Cloud

Join Kevin Evans & Kent Weare as they explore how Microsoft's Logic Apps integrate with AI-powered agents to automate complex business processes, connect legacy systems, and accelerate digital transformation. Perfect for professionals seeking to understand modern cloud-native automation.

 

What You'll Learn

What Logic Apps are and their core value in system integration

The evolution from middleware to cloud-native orchestration

How connectors enable seamless connection to diverse systems — SAP, mainframes, and SaaS solutions

The role of agentic development and AI models in automating decision-making

Practical use cases: Order-to-Cash, HR onboarding, and ServiceNow ticket audits

Deploying MCP servers and configuring connectors dynamically

Why scope narrowing leads to higher accuracy in AI-driven processes

Leveraging knowledge bases to enhance agent capabilities

The integration landscape: Using Foundry with Logic Apps for enterprise connectivity

What You'll Learn

What Logic Apps are and their core value in system integration

The evolution from middleware to cloud-native orchestration

How connectors enable seamless connection to diverse systems — SAP, mainframes, and SaaS solutions

The role of agentic development and AI models in automating decision-making

Practical use cases: Order-to-Cash, HR onboarding, and ServiceNow ticket audits

Deploying MCP servers and configuring connectors dynamically

Why scope narrowing leads to higher accuracy in AI-driven processes

Leveraging knowledge bases to enhance agent capabilities

The integration landscape: Using Foundry with Logic Apps for enterprise connectivity

What You'll Learn

What Logic Apps are and their core value in system integration

The evolution from middleware to cloud-native orchestration

How connectors enable seamless connection to diverse systems — SAP, mainframes, and SaaS solutions

The role of agentic development and AI models in automating decision-making

Practical use cases: Order-to-Cash, HR onboarding, and ServiceNow ticket audits

Deploying MCP servers and configuring connectors dynamically

Why scope narrowing leads to higher accuracy in AI-driven processes

Leveraging knowledge bases to enhance agent capabilities

The integration landscape: Using Foundry with Logic Apps for enterprise connectivity

Most tech careers aren’t linear—and yours doesn’t have to be either. Dr. Jason Bernard shares his unconventional journey from programming video games at age 11 to pioneering AI algorithms that could revolutionize biotech and urban planning.

In this eye-opening episode, Jason reveals how his diverse experiences—from military service to music composition—fuel groundbreaking research in process modeling and data science. You'll discover:

How early programming and limited hardware shaped his approach to elegant, resource-efficient algorithms

The real difference between machine learning, AI, and data analytics—and why understanding this distinction is key to future-proof skills

The fascinating world of modeling biological processes, like gene-expression for drought-resistant crops, using reverse engineering in data science

Why simplicity and human insight remain essential in an era dominated by neural networks and language models The future of data science careers in a rapidly evolving tech landscape and practical advice for new professionals

This episode is perfect for tech enthusiasts, data scientists, and students eager to learn how a curious mind can make an impact across industries—from agriculture to telecommunications—and why adaptability and a deep understanding of algorithms are the secret weapons for success. With over 18 years of research and real-world experience, Jason is an assistant professor at Athabasca University, known for pushing the boundaries of process modeling and optimization. His work on inferring computational models from real data has been called the “holy grail” by leading experts. If you're looking for inspiration on how to navigate a non-traditional tech path or want to understand the core principles behind cutting-edge AI research—this episode will change the way you see your career.

Hit play and unlock your potential today.

 

Host Kevin Evans

What happens when geopolitical tensions spill into your network?

Host: Kevin Evans

Guest: Dom Vogel

In this episode, we break down the real-world cyber threats facing organizations today — from Iranian threat groups targeting US healthcare to the hidden risks lurking in your contractor relationships and free SaaS tools.

Whether you're a solo operator or leading a large enterprise, this conversation delivers practical, no-nonsense strategies to protect what matters most.

🔐 In This Episode: Iranian cyber groups targeting US organizations & their destructive playbook Why geopolitical, cyber, and physical risks can no longer be managed in silos

The leadership drought in cybersecurity — and how to fix it Who should actually own your business technology (hint: not just IT)

Cybersecurity for small businesses: simple wins that scale

The real cost of free tools — what your data is funding Gen Z, remote work, and the future of the cyber workforce

Why procedural maturity matters more than your tech stack

Third-party and contractor risks hiding in plain sight Domain, cloud, and access governance — the unsexy stuff that saves companies

Resources: https://www.codetocloud.io/blog/who-owns-your-business-technology

In this episode, Kevin Evans sits down with AI systems engineer and energy sector expert Wish Bakshi to explore how AI and data analytics are transforming the oil, gas, and power industries. They discuss technical intricacies, industry challenges, and future opportunities in energy infrastructure.

Key Topics

How AI models are used across the "molecules to electrons to bits" in energy

The complexity of oil & gas logistics in North America vs. Europe

Power grid nuances and the role of real-time data and AI

The impact of open source AI models and frameworks like Hugging Face

The evolution of AI’s influence on commodities trading and market prediction

Power & power grid stability — the importance of maintaining the grid’s "heartbeat"

Building and managing AI data centers: scale, infrastructure, and environmental considerations

Frameworks for deploying AI: orchestrator methodology, multi-agent systems

The rise of citizen developers using low-code/no-code AI tools

Future outlook for AI adoption, standardization, and industry disruption

Resources & Links

Hugging Face – community-driven platform for open source AI models

Wednesdays with Wish – Wish Bakshi’s LinkedIn blog & updates

Industry reports and articles on AI in energy sectors (suggested based on discussion points)

In this episode, Kevin Evans and Steve Rogalsky discuss the evolving landscape of education technology (EdTech) and the transformative potential of artificial intelligence (AI) in learning.

They reflect on their experiences at the Prairie Dev Conference, emphasizing the importance of community and networking in professional growth. The conversation delves into the distinction between generative and transformative learning, the ethical implications of AI in education, and the need for personalized learning experiences.

They also explore the barriers to access in education and the potential of AI to democratize learning globally. The episode concludes with thoughts on the future of AI in education and the importance of generosity in fostering transformation. Takeaways Conferences can be a catalyst for career growth. EdTech focuses on solving educational problems with technology. Generative AI can create content but should aim for transformative learning.

AI can personalize education to meet individual learner needs. Ethics and guardrails are crucial in AI applications. Technology can democratize access to education. Javan's paradox suggests that efficiency can lead to higher demand. AI can enhance the role of teachers rather than replace them. The future of work will involve more AI integration.

Generosity in sharing knowledge can transform lives.

Chapters

00:00 Introduction and Conference Reflections

02:46 Transitioning in EdTech and AI Exploration

05:36 Understanding EdTech and Lifelong Learning

08:47 Generative vs. Transformative Learning with AI

11:35 AI in the Classroom: Personalized Learning Experiences

14:42 Use Cases in Psychology and Mathematics

17:22 Governance and Ethics in AI Education

20:13 Future of AI in Education and Job Impact

28:14 The Evolution of Technology and Job Creation

31:56 Access to Education and Lowering Barriers

35:08 The Impact of AI on Work and Creativity

39:13 Vibe Coding and Its Disruptive Potential

45:50 Generosity and Transformation in Technology