Agentic AI: Your Data, Your Rules, New Risks!

Agentic AI: Your Data, Your Rules, New Risks!

Video by Open Data Science and AI Conference via YouTube
Agentic AI: Your Data, Your Rules, New Risks!

Explore the evolving landscape of AI agents and their access to your information.

Want to learn more about AI in person? Check out ODSC AI East 2026, coming to Boston this April 28th-30th: https://hubs.li/Q041BP6P0

#DataScience #AI #ArtificialIntelligence #ODSCAI

————————————————————————————————————-

Visit our website and choose the nearest ODSC event to attend and experience all our training and workshops: https://odsc.ai

To watch more videos like this, visit https://aiplus.training

Sign up for the newsletter to stay up to date with the latest trends in data science: https://opendatascience.com/newsletter/

Follow us online!
• Facebook: https://www.facebook.com/OPENDATASCI
• Instagram: https://www.instagram.com/odsc/
• Blog: https://opendatascience.com/
• LinkedIn: https://www.linkedin.com/company/open-data-science/
• X (twitter): https://x.com/_odsc

Source

OpenProject 17.3: Evolving backlogs and sprints

OpenProject 17.3: Evolving backlogs and sprints

Video by OpenProject | Open Source Project Management via YouTube
OpenProject 17.3: Evolving backlogs and sprints

The release brings various features and improvements for you, e.g.

00:00 – Introduction
0:15 – A major update: Evolving backlogs and sprints
1:34 – Action boards released to community
2:06 – Improved project home page
2:24 – Sharing of meeting templates (Basic plan and higher)
2:48 – Option to safely change project identifiers
3:03 – Improved workflow configuration

Find out more about all features and improvements in our release notes: https://www.openproject.org/docs/release-notes/17-3-0/

Source

AI For The Open Source Community

AI For The Open Source Community

Video by Open Source Connect via YouTube
AI For The Open Source Community

🗓 Date: 3rd March
⏰ Time: 8:30 PM IST
🎤 Speaker: Sebastiano Fuccio

Don’t forget to like, share, and subscribe for more talks on AI and emerging technologies!

#OpenSource #ai #cloud #TechTalk #Developers #SebastianoFuccio

Source

Are Kenyan Developers Actually Making Money? (Some Said NO 😳)

Are Kenyan Developers Actually Making Money? (Some Said NO 😳)

Video by OpenSource via YouTube
Are Kenyan Developers Actually Making Money? (Some Said NO 😳)

Are Kenyan developers actually making money?
We went to JKUAT to find out the truth, from street opinions in Nairobi to real developers at a tech event. Some said devs are struggling… others are making serious money
We also ran a 30-minute challenge where teams had to come up with real solutions for small businesses, and the results were crazy
Plus:
A senior developer breaks down AI in tech
Insights from the Angular community in Kenya
Raw, unfiltered opinions from devs on the ground
This isn’t just a vlog… it’s the reality of tech in Kenya 🇰🇪
Be honest… do you think developers in Kenya are actually making money?

CHAPTERS
0:00 Highlights
0:50 Nairobi Street Interviews
2:36 Breakfast
2:52 JKUAT
3:20 Tech Event
3:39 Senior Dev Interview
6:00 Event Ends
6:32 30 Mins Challenge
6:47 Brainstorming
8:21 Teams Presenting Ideas
23:35 Winner
23:40 Interview with Angular Lead

Source

Enterprise Prompt Engineering & LLM Testing via h2oGPTe | Part 12

Enterprise Prompt Engineering & LLM Testing via h2oGPTe | Part 12

Video by H2O.ai via YouTube
Enterprise Prompt Engineering & LLM Testing via h2oGPTe | Part 12

How Enterprise h2oGPTe manages prompt templates, version control, and multilingual AI agent deployment at scale.

Bridging predictive models and end users requires well-engineered, maintainable prompts. h2oGPTe provides a centralized prompt library where teams can create, clone, version, and share templates across the organization. The H2O Super Agent connects natural language prompts directly to predictive scoring APIs—enabling real-world actions like addressing customer churn. Multilingual template support and UI localization allow consistent AI behavior to be deployed across global markets.

Technical Capabilities & Resources

➤ Prompt Templates & Libraries: Create, clone, and share prompt templates from a managed organizational catalog.
🔗 https://docs.h2o.ai/enterprise-h2ogpte/guide/prompts

➤ Prompt Version Control & Iteration: Define system behaviors, iterate on prompt designs, and manage template settings.
🔗 https://docs.h2o.ai/enterprise-h2ogpte/guide/prompts#create-a-prompt-template

➤ Template Sharing Across Teams: Distribute prompt templates for consistent AI behavior organization-wide.
🔗 https://docs.h2o.ai/enterprise-h2ogpte/guide/prompts#share-a-prompt-template

➤ Custom Multilingual Prompts: Configure language-specific templates for consistent, localized global AI deployment.
🔗 https://docs.h2o.ai/enterprise-h2ogpte/guide/prompts#create-a-prompt-template-for-a-specific-language

Source

Advanced MLflow Tracing: Framework Integrations with LangChain, LlamaIndex, LangGraph (Notebook 1.6)

Advanced MLflow Tracing: Framework Integrations with LangChain, LlamaIndex, LangGraph (Notebook 1.6)

Video by MLflow via YouTube
Advanced MLflow Tracing: Framework Integrations with LangChain, LlamaIndex, LangGraph (Notebook 1.6)

In this sixth episode of this series, Jules Damji dives deep into MLflow’s extensive framework integrations. MLflow supports over 30 different open-source agent-building frameworks, allowing you to automatically trace and evaluate complex AI workflows regardless of your chosen architecture.

This tutorial provides a hands-on comparison of three open source agent building frameworks and demonstrates how MLflow provides full visibility into their execution:
🔹 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻: Learn how to use high-level primitives like ChatPromptTemplate and StringOutputParser to build sequential workflows. We demonstrate both simple chains and complex multi-step sequences connected via the pipeline operator.
🔹 𝗟𝗹𝗮𝗺𝗮𝗜𝗻𝗱𝗲𝘅: See how to build a Retrieval-Augmented Generation (RAG) system. We walk through creating an in-memory vector index, generating embeddings with OpenAI, and using a query engine to retrieve document-based answers, all while capturing the entire operation trace in MLflow.
🔹 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵: For more advanced use cases, we explore building stateful, hierarchical agent workflows. We demonstrate a customer service triage system that uses a supervisor node to classify queries and route them to specialized handlers for billing, tech support, or general inquiries.

Key Takeaways:
🔹 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗧𝗿𝗮𝗰𝗶𝗻𝗴: All frameworks integrated with MLflow are automatically traced, capturing inputs, outputs, and intermediate steps without manual instrumentation.8
🔹 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻: Choose LangChain for sequential chains, LlamaIndex for heavy document indexing, and LangGraph for complex, stateful branching or looping workflows.
🔹 𝗩𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆: Use the MLflow UI to inspect timelines, verify embeddings, and debug the internal logic of your AI agents.

Resources:
🔗 Notebook 1.5: https://github.com/dmatrix/mlflow-genai-tutorials/blob/main/06_framework_integrations.ipynb
🎥 Full Series Playlist: https://youtube.com/playlist?list=PLaoPu6xpLk9EI99TuOjSgy-UuDWowJ_mR&si=jdbAbxTCRuxFxfnG

Source