Agentic Conversations (formally mlops.community)

MLOps podcast #180 with Sachin Abeywardana, Deep Learning Engineer at Canva AI, Adventures in Building CLIP and Other (Largeish) Language Models sponsored by Prem AI.

// Abstract

Sachin takes us on an adventure, sharing insights on the pitfalls of not understanding the broader product and the importance of incorporating AI and machine learning capabilities. From the use of AI models for grammar correction and code generation to the fascinating Clip model and the challenges of balancing work and family life, this episode promises to be both informative and thought-provoking.

// Bio

Sachin is the father of two beautiful children. He completed his PhD in Bayesian Machine Learning at the University of Sydney in 2015. In 2016, he discovered Deep Learning and hasn't looked back. He currently works as a Senior Machine Learning Engineer at Canva and is mainly focusing on NLP problems.

// MLOps Jobs board

jobs.mlops.community

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links

Sachin Blogs: https://sachinruk.github.io/blog.htmlhttps://sachinruk.github.io/blog/

Graph ML link: http://web.stanford.edu/class/cs224w/

⁠--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Follow us on Twitter: @mlopscommunity

Sign up for the next meetup: https://go.mlops.community/register

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Sachin on LinkedIn: https://www.linkedin.com/in/sachinabeywardana/

Timestamps:

[00:00] Sachin's preferred beverage

[00:26] Takeaways

[02:30] Chat GPT user

[05:58] Understanding of reliable Agents

[08:10] Sachin's background

[12:45] Staying at Deep Learning

[16:17] Recommendation or Lead Scoring

[17:36] Vector database

[19:00] Sachin's blogs

[23:26] The cap people

[26:10] Pursuing a business case

[27:33] Canva

[31:16] Incorporating AI and Machine Learning

[32:17] Sponsor Ad

[38:22] Eliminating unnecessary steps

[39:00] Interacting with the product team

[43:04] Criticisms of the current architecture limitations

[45:58] Insufficient exploration of Transformers

[47:42] Explaining GraphML

[52:35] Fine-tuning ChatGPT2

[57:54] Leading ML Engineers and teams

[59:40] Being practical with Math

[1:05:52] Wrap up

MLOps Coffee Sessions #179 with Shahul Es, All About Evaluating LLM Applications.

// Abstract

Shahul Es, renowned for his expertise in the evaluation space and is the creator of the Ragas Project. Shahul dives deep into the world of evaluation in open source models, sharing insights on debugging, troubleshooting, and the challenges faced when it comes to benchmarks. From the importance of custom data distributions to the role of fine-tuning in enhancing model performance, this episode is packed with valuable information for anyone interested in language models and AI.

// Bio

Shahul is a data science professional with 6+ years of expertise and has worked in data domains from structured, NLP, to Audio processing. He is also a Kaggle GrandMaster and code owner/ ML of the Open-Assistant initiative that released some of the best open-source alternatives to ChatGPT.

// MLOps Jobs board

jobs.mlops.community

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links

All about evaluating Large language models blog: https://explodinggradients.com/all-about-evaluating-large-language-models

Ragas: https://github.com/explodinggradients/ragas

--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Follow us on Twitter: @mlopscommunity

Sign up for the next meetup: https://go.mlops.community/register

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Shahul on LinkedIn: https://www.linkedin.com/in/shahules/

Timestamps:

[00:00] Shahul's preferred coffee

[00:20] Takeaways

[01:46] Please like, share, and subscribe to our MLOps channels!

[02:07] Shahul's definition of Evaluation

[03:27] Evaluation metrics and Benchmarks

[05:46] Gamed leaderboards

[10:13] Best at summarizing long text open-source models

[11:12] Benchmarks

[14:20] Recommending the evaluation process

[17:43] LLMs for other LLMs

[20:40] Debugging failed evaluation models

[24:25] Prompt injection

[27:32] Alignment

[32:45] Open Assist

[35:51] Garbage in, garbage out

[37:00] Ragas

[42:52] Valuable use case besides OpenAI

[45:11] Fine-tuning LLMs

[49:07] Connect with Shahul if you need help with Ragas @Shahules786 on Twitter

[49:58] Wrap up

MLOps Coffee Sessions #178 with Stephen Batifol, Building an ML Platform: Insights, Community, and Advocacy.

// Abstract

Discover how Wolt onboards data scientists onto the platform and builds a thriving internal community of users. Stephen's firsthand experiences shed light on the importance of developer relations and how they contribute to making data scientists' lives easier. From top-notch documentation to getting-started guides and tutorials, the internal platform at Wolt prioritizes the needs of its users.

// Bio

From Android developer to Data Scientist to Machine Learning Engineer, Stephen has a wealth of software engineering experience at Wolt. He believes that machine learning has a lot to learn from software engineering best practices and spends his time making ML deployments simple for other engineers. Stephen is also a founding member and organizer of the MLOps.community Meetups in Berlin.

// MLOps Jobs board

jobs.mlops.community

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links

⁠--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Follow us on Twitter: @mlopscommunity

Sign up for the next meetup: https://go.mlops.community/register

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Stephen on LinkedIn: https://www.linkedin.com/in/stephen-batifol/

Timestamps:

[00:00] Stephen's preferred coffee

[00:32] Takeaways

[01:35] Please like, share, and subscribe to our MLOps channels!

[03:00] Creating his own team!

[04:44] DevRel

[06:32] The door dash of Europe

[11:28] Data platform underneath

[12:55] Cellular core deployment uses open source

[14:21] Alibi

[16:08] Kafka

[16:59] Selling points to data scientists

[20:05] Language models concern data scientists

[22:12] Incorporating LLMs into the business

[23:55] Feedback from data scientists and end users

[27:37] User surveys

[30:11] Evangelizing and giving talks

[35:25] Tech Hub Culture in Berlin

[38:38] Kubernetes lifestyle

[42:55] Interacting with SREs

[45:28] Wrap up

MLOps Coffee Sessions #177 with Mohamed Abusaid and Mara Pometti, Empowering Employees: Education and Literacy for Data and AI in the Workplace sponsored by QuantumBlack.

Am Mohamed, a tech enthusiast, hacker, avid traveler, and foodie all rolled into one individual. Built his first website when he was 9 and fell in love with computers and the internet ever since. Graduated with computer science from university although dabbled in electrical, electronic, and network engineering before that. When he's not reading up on the latest tech conversations and products on Hacker News, Mohamed spends his time traveling to new destinations and exploring their cuisine and culture. Mohamed works with different companies helping them tackle challenges in developing, deploying, and scaling their analytics to reach its potential. Some topics he's enthusiastic about include MLOps, DataOps, GenerativeAI, Product thinking, and building cross-functional teams to deliver user-first products.

MLOps Podcast with Harrison Chase, Aakanksha Chowdhery, Yaron Singer, and Benjamin Harvey, Build and Risks.

Huge shout out and thanks to Databricks for helping us make this all happen!

https://www.langchain.com/

https://www.deepmind.com/

https://www.robustintelligence.com/

https://squared.ai/

https://www.databricks.com/

MLOps Coffee Sessions #176 with Vin Vashishta, Collaboration and Strategy.

// Abstract

From the significance of technical strategists to the crucial role of product managers with a deep understanding of data and AI products, Vin shares invaluable insights on fostering collaboration, driving strategy, and maximizing the potential of data within organizations. Join us as we explore the importance of becoming multipliers in our fields, the power of effective strategy in leveraging data, and the opportunities that lie in the generative AI era.

// Bio

Vin's background is in applied data science. He is the founder of V Squared, one of the oldest and smallest data science consulting companies in the world. They help businesses monetize data and AI. Vin is the author of From Data to Profit. He teaches technical strategy and data, and AI product management.

// MLOps Jobs board

jobs.mlops.community

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links

Website: https://www.datascience.vin/

From Data to Profit: How Businesses Leverage Data to Grow Their Top and Bottom Lines book:https://www.amazon.com/Data-Profit-Businesses-Leverage-Bottom/dp/1394196210/⁠

--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Follow us on Twitter: @mlopscommunity

Sign up for the next meetup: https://go.mlops.community/register

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Vin on LinkedIn: https://www.linkedin.com/in/vineetvashishta/

Timestamps:

[00:00] Vin's preferred coffee

[00:14] Takeaways

[02:02] Please like, share, and subscribe to our MLOps channels!

[02:28] Recent ideas of Vin

[05:09] Understanding the business value of any project [08:30] Generative AI making things faster

[14:29] Strategy in practice

[20:19] Practicality and Credibility of Strategists

[22:42] Coming soon!!! LLMs in Production Conference Panel Part III

[27:48] Becoming a Multiplier

[29:03] The AI Product Manager

[35:12] Successful monetization and integration of technologies

[37:48] Justifying the ROI of LLMs

[44:59] Adding that extra value

[49:52] Read Vin's book linked above!

[50:35] Wrap up

Sign up for our next LLM in production conference: https://go.mlops.community/prodiii

#180 with LLMs in Production Conference part 2 Ux of a LLM User Panel, Misty Free, Dina Yerlan, and Artem Harutyunyan hosted by Innovation Endeavors' Davis Treybig.

MLOps Coffee Sessions #175 with Lamia Youseff, From Virtualization to AI Integration.

// Abstract

Lamia discusses how both Fortune 500 companies and SMBs lack the knowledge and capabilities to identify which use cases in their systems can benefit from AI integration. She emphasizes the importance of helping these companies integrate AI effectively and acquire the necessary capabilities to stay competitive in the market.

// Bio

By way of an introduction, Dr. Lamia Youseff has been working in AI / ML for ~25 years, first in academia (MIT, Stanford, UCSB), then large tech (Google, Microsoft, Apple, and Facebook), and most recently with startups in Generative AI. She is currently the executive director of JazzComputing, a Visiting Research Scientist at Stanford University in Computer Science and AI, and a research affiliate with MIT Computer Science and Artificial Intelligence Lab (CSAIL). Dr. Youseff earned her Ph.D. in computer science by studying computationally intensive workloads (such as AI / ML and HPC / Scientific Codes) and has built/led several AI teams as an executive and leader at large tech companies over the years (Google, Facebook, Microsoft, and Apple). She also earned her Master's in business management, strategy, and leadership from Stanford Graduate School of Business (GSB), where she is a guest lecturer today. Dr. Youseff regularly writes and speaks about AI and Machine Learning evolution at CIO/CTO/CEO summits.

// MLOps Jobs board

jobs.mlops.community

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links

⁠--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Follow us on Twitter: @mlopscommunity

Sign up for the next meetup: https://go.mlops.community/register

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Lamia on LinkedIn: https://www.linkedin.com/in/lyouseff/

Timestamps:

[00:00] Lamia's preferred coffee

[01:12] Takeaways

[03:00] Please like, share, and subscribe to our MLOps channels!

[03:20] Lamia's background

[09:52] Getting into Google Cloud

[13:10] The Google Cloud project

[16:38] The world before Kubernetes

[19:25] Evolution of virtualization

[23:20] Cloud evolution

[28:13] Kubernetes enables the ecosystem

[32:38] Multiple systems for machine learning

[34:40] Standardization for a greater good

[39:50] Complexity and pain points of ML in production

[46:26] JazzComputing

[50:33] Bridging gaps in AI implementation and investment

[51:19] Wrap up

MLOps Coffee Sessions #178 with LLMs in Production Conference part 2 LLM on K8s Panel, Manjot Pahwa, Rahul Parundekar, and

MLOps Coffee Sessions #174 with Michelle Marie Conway, Harnessing MLOps in Finance: Bringing Statistical Models to Life for Positive Impact, co-hosted by Stephen Batifol.

// Abstract

Michelle Marie Conway joins hosts Stephen Batifol and Demetrios to share their insights and experiences in the tech industry. Michelle emphasizes the importance of constant learning and adaptation in the rapidly changing tech industry. They discuss the need to stay up to date with the latest documentation, understand code logic, and be mindful when writing code. Michelle also reflects on their experiences as one of the few women in their university math class and often being the only woman on their team in the workplace. They discuss the need for more girls to pursue STEM subjects in schools and the importance of allies in the workplace. Additionally, Michelle explores the benefits and challenges of AI tools, sharing their experiences with tools like Gen AI and ChatGPT. While AI tools enhance productivity, Michelle also acknowledges the limitations of these tools in more technical tasks and the continued reliance on developer resources. This episode offers valuable insights into the importance of continuous learning, gender diversity in STEM, and the potential of AI tools in the field of MLOps.

// Bio

As an Irish woman who relocated to London after completing her university studies in Dublin, Michelle spent the past 12 years carving out a career in the data and tech industry. With a keen eye for detail and a passion for innovation, she has consistently leveraged my expertise to drive growth and deliver results for the companies she has worked for. As a dynamic and driven professional, Michelle is always looking for new challenges and opportunities to learn and grow, and she's excited to see what the future holds in this exciting and ever-evolving industry.

// MLOps Jobs board

jobs.mlops.community

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links⁠

--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Follow us on Twitter: @mlopscommunity

Sign up for the next meetup: https://go.mlops.community/register

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Stephen on LinkedIn: https://www.linkedin.com/in/stephen-batifol/

Connect with Michelle on LinkedIn: https://www.linkedin.com/in/michelle-conway-40337432

Timestamps:

[00:00] Michelle's preferred coffee

[02:04] Takeaways

[05:18] Please like, share, and subscribe to our MLOps channels!

[06:18] Michelle's journey in tech

[07:49] Engineering best practices

[09:38] Getting comfortable with the hump

[11:22] Clean coding fundamentals

[13:29] Working with the people

[14:09] GCP migration

[18:00] GCP migration length of journey

[18:38] Moving data focus

[19:18] Effectiveness of running 2 systems

[21:00] Dealing with discrepancies

[22:15] Using Nexus

[24:04] Migrating data from Teradata to BigQuery, strict security

[28:48] Hiring new people

[30:56] Securely managing financial data with millions of customers

[32:30] When things go wrong

[35:08] Finding the root cause

[36:28] Dealing with the producers' problems

[40:46] Rapid tech evolution, constant learning

[44:44] Teaching Python, using Gen AI for tasks

[46:34] Dealing with LLMs use cases

[49:15] Dealing with stakeholders and MLOps teams

[51:17] Having a translator

[52:18] Being a woman in the tech industry

[55:11] Encourage more girls in STEM, support women

[56:36] Women in the conversation on tech and female representation

[1:03:49] Wrap up