What we’re about
Welcome to Data Science Dojo's Meetup group. Our goal is to help connect other like-minded business professionals who are interested in teaching, learning, and sharing their knowledge and understanding of data science to a larger community.
We encourage all members of this group to be pro-active in leading discussions on topics related to data science like machine learning, artificial intelligence, predictive analytics, big data, and IoT, as well as programming languages such as R, Hadoop, and Python.
Stay tuned to our Meetup calendar for future community events and be sure to follow us on Twitter at @DataScienceDojo. Also, be sure to visit our data science bootcamp for more information about our training.
Upcoming events (3)
See all- Part 2: How Do You Evaluate Agents? | Evaluating AI Agents with Arize AILink visible for attendees
Evaluating large language models (LLMs) can be a daunting task, and when it comes to agentic systems, the complexity increases exponentially. In this second part of our community series with Arize AI, we will explore why traditional LLM evaluation metrics fall short when applied to agents and introduce modern LLM evaluation techniques that are built for this new paradigm.
From code-based evaluations to LLM-driven assessments, human feedback, and benchmarking your metrics, this session will equip you with the necessary tools and practices to assess agent behavior effectively. You will also get hands-on experience with Arize Phoenix and learn how to run your own LLM evaluations using both ground truth data and LLMs.
What We Will Cover:
- Why standard metrics like BLEU, ROUGE, or even hallucination detection aren’t sufficient for evaluating agents.
- Core evaluation methods for agents: LLM evaluations using code-based evaluations, LLM-driven assessments, human feedback and labeling, and ground truth comparisons.
- How to write high-quality LLM evaluations that align with real-world tasks and expected outcomes.
- Building and benchmarking LLM evaluations using ground truth data to validate their effectiveness.
- Best practices for capturing telemetry and instrumenting evaluations at scale.
- How OpenInference standards (where applicable) can improve interoperability and consistency across systems.
- Hands-on Exercise: Judge a sample agent run using both code-based and LLM-based evaluations with Arize Phoenix.
Ready for Part 3 of the series? Find it here!
- Part 3: Can Agents Evaluate Themselves? | Evaluating AI Agents with Arize AILink visible for attendees
AI agent evaluation is evolving — it’s no longer just about what the AI agent outputs, but how it got there. In this Part 3 webinar of the community series with Arize AI, we will dive into advanced AI agent evaluation techniques, including path-based reasoning, convergence analysis, and even using agents to evaluate other agents.
Explore how to measure the efficiency and structure of agent reasoning paths, assess collaboration in multi-agent systems, and evaluate the quality of planning in complex setups like hierarchical or crew-based frameworks. You will also get a look at emerging techniques like self-evaluation, peer review, and agent-as-judge models — where agents critique and improve each other in real time.
What We Will Cover:
- Understand how to evaluate not just what an AI agent does, but how it arrived at its output.
- Measure convergence and reasoning paths to assess execution quality and efficiency.
- Learn how to evaluate collaboration and role effectiveness in multi-agent systems.
- Explore methods for assessing planning quality in hierarchical and crew-based agents.
- Dive into agents-as-judges: Enable self-evaluation and peer review mechanisms and build critique tools and internal feedback loops to improve agent performance.
- Discuss real-world applications of these techniques in large-scale, agentic AI systems.
- Interactive Element: Watch a live example of an agent acting as a judge — or participate in a multi-agent AI agent evaluation demo using Arize Phoenix.
Missed the earlier parts? Catch up on Part 1 and Part 2 of the series!
- Large Language Models Bootcamp Information SessionLink visible for attendees
Join us for an exclusive Information Session where we break down everything you need to know about our 5-day Large Language Models Bootcamp (available in-person & online).
➡ Why Attend the Info Session:
✅ Gain in-depth understanding of the structure, agenda, and hands-on curriculum.
✅ Get your questions answered in the live interactive Q/A session.
✅ Learn how our hands-on project will get you building LLM applications in just 5 days.
✅ Learn about the renowned instructors and industry-leading partners who are a part of our bootcamp faculty.➡ Who Should Attend?
AI enthusiasts, data professionals, and product leaders looking to gain hands-on experience and leverage LLMs for innovation and growth.We look forward to meeting you!
For further details: Link to Website | Link to Advisor Call
