Agentic Conversations (formally mlops.community)

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In this last episode, we covered how Google is thinking about MLOps and how automation plays a key part in their view of MLOps. We started to talk about CI, CD, and the role they play in a pipeline setup for CT. In the next episode, we'll pick up where we left off, starting our discussion of CT and some of the reasons you’d want to set up a pipeline with continuous training in the first place.

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Connect with Cris Sterry on LinkedIn: https://www.linkedin.com/in/chrissterry/

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Yoav is the builder behind Say Less, an AI-powered email summarization tool that was recently featured on the front page of Hacker News and Product Hunt. In this talk, Yoav will walk us through the end-to-end process of building the tool, from the prototype phase to deploying the model as a real-time HTTP endpoint.

Yoav Zimmerman is the engineer/founder behind Model Zoo, a machine learning deployment platform focused on ease-of-use. He has previously worked at Determined AI on large-scale deep learning training infrastructure and at Google on knowledge base construction for features that powered Google Assistant and Search. 

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/
Connect with Cris Sterry on LinkedIn: https://www.linkedin.com/in/chrissterry/
Connect with Yoav on LinkedIn: https://www.linkedin.com/in/yoav-zimmerman-05653252/

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|| Links Referenced in the Show ||

General Info: https://medium.com/@paktek123
Load Balancer Series: https://medium.com/load-balancer-series
Upcoming Open Source: https://medium.com/upcoming-open-source

Some Libraries Neeran maintains: https://github.com/paktek123/elasticsearch-crystal
Some libraries Neeran used to maintain: https://github.com/microsoft/pgtester (and https://medium.com/yammer-engineering/testing-postgresql-scripts-with-rspec-and-pg-tester-c3c6c1679aec)
Some interesting projects Neeran has worked on (architected these): https://devblogs.microsoft.com/cse/2016/05/22/access-azure-blob-storage-from-your-apps-using-s3-api/, https://medium.com/yammer-engineering/logs-on-logs-on-logs-aggregation-at-yammer-2b7073f35606
Some of Benevolent Stuff: https://www.benevolent.com/engineering-blog/deploying-metallb-in-production, https://www.benevolent.com/engineering-blog/spark-on-kubernetes-for-nlp-at-scale (helped with the infra side)

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/
Connect with Cris Sterry on LinkedIn: https://www.linkedin.com/in/chrissterry/

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We trained a Transformer neural net on ambient music to see if a machine can compose with the great masters. Ambient is a soft, flowing, ethereal genre of music that I’ve loved for decades. There are all kinds of ambient, from white noise to tracks that mimic the murmur of soft summer rain in a sprawling forest, but Dan favors ambient that weaves together environmental sounds and dreamy, wavelike melodies into a single, lush tapestry.  

Can machine learning ever hope to craft something so seemingly simple yet intricate? The answer is yes, and it’s getting better and better with each passing year. It won’t be long before artists are co-composing with AI, using software that helps them weave their own masterpieces of sound.

In this talk, we looked at how we did it. Along the way, we’ll listen to some more awesome samples that worked really well and some that didn’t work as well as we hoped. You can download the model to play around with yourself. Dam also shows you an end-to-end machine learning pipeline, with downloadable containers that you can string together with ease to train a masterful music-making machine learning model on your own. 

Dan Jeffries is Chief Technology Evangelist at Pachyderm. He’s also an author, engineer, futurist, and pro blogger, and he’s given talks all over the world on AI and cryptographic platforms. He’s spent more than two decades in IT as a consultant and at open source pioneer Red Hat.

With more than 50K followers on Medium, his articles have held the number one writer's spot on Medium for Artificial Intelligence, Bitcoin, Cryptocurrency, and Economics more than 25 times.  His breakout AI tutorial series "Learning AI If You Suck at Math" along with his explosive pieces on cryptocurrency, "Why Everyone Missed the Most Important Invention of the Last 500 Years” and "Why Everyone Missed the Most Mind-Blowing Feature of Cryptocurrency,” are shared hundreds of times daily all over social media and been read by more than 5 million people worldwide.

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

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/
Connect with Cris Sterry on LinkedIn: https://www.linkedin.com/in/chrissterry/
Connect with Dan on LinkedIn: https://www.linkedin.com/in/danjeffries/

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MLOps and DevOps have a large number of parallels. Many of the techniques, practices, and processes used for traditional software projects can be followed almost exactly in ML projects. However, the day-to-day of an ML project is usually significantly different from a traditional software project. So while the ideas and principles can still apply, it’s important to be aware of the core aims of DevOps when applying them.

Damian is a Cloud Advocate specializing in DevOps and MLOps. After spending a year in Toronto, Canada, he returned to Australia - the land of the dangerous creatures and beautiful beaches - in 2018. Formerly a dev at Octopus Deploy and a Microsoft MVP, he has a background in software development and consulting in a broad range of industries. In Australia, he co-organised the Brisbane.Net User Group and launched the now annual DDD Brisbane conference. He regularly speaks at conferences, User Groups, and other events around the world. Most of the time, you'll find him talking to software engineers, IT pros, and managers to help them get the most out of their DevOps strategies.

|| Links Referenced in the Show ||
MLOps, or DevOps for Machine Learning: https://damianbrady.com.au/2019/10/28/mlops-or-devops-for-machine-learning/
Microsoft Azure Machine Learning: http://ml.azure.com/
MLOps Coffee Sessions #6 Continuous Integration for ML

// Featuring Elle O'Brien: https://www.youtube.com/watch?v=L98VxJDHXMM
MLOps: Isn’t that just DevOps? Ryan Dawson speaks at MLOps Coffee Session: https://www.seldon.io/mlops-isnt-that-just-devops-ryan-dawson-speaks-at-mlops-coffee-session/
DVC - Data Version Control: https://dvc.org/
Pachyderm - Version-controlled data science: https://www.pachyderm.com/
Databricks - Unified Data Analytics: https://databricks.com/

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/
Connect with Damian on LinkedIn: https://www.linkedin.com/in/damianbrady/

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The concept of machine learning products is a new one for the business world. There is a lack of clarity around key elements: Product Roadmaps and Planning, the Machine Learning Lifecycle, Project and Product Management, Release Management, and Maintenance.

In this talk, we covered a framework specific to Machine Learning products. We discussed the improvements businesses can expect to see from a repeatable process. We also covered the concept of monetization and integrating machine learning into the business model. 

Vin is an applied data scientist and teaches companies to monetize machine learning. He is currently working on an ML-based decision support product as well as my strategy consulting practice.

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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] Intro to Vin Vashishta
[01:33] Vin's background
[05:04] Key problems when monetizing Machine Learning
[07:00] How can we fix the key problems in monetizing Machine Learning
[13:24] How can we go about creating that repeatable process?
[16:17] There are all these data scientists who aren't going to school and getting all these diplomas for data wrangling. Right?
[17:12] How can you successfully envision that road mapping from the beginning of the process?
[24:19] How can a Data Scientist be more proactive instead of just getting paid?
[28:53] Have you figured out how to quickly estimate an order of magnitude when ROI questions arise?
[31:48] Have you seen a company that has machine learning as its core product, or have you seen some companies crash and burn?
[34:39] How do you see the tooling ecosystem right now? And how do you see it in a few years?
[38:24] And so how do you balance that when a lot of these tools have a lot of like, bleed and overlap? And so what does that look like?
[42:40] Have you stumbled across organizations wanting to adopt AI without having the foundations, such as data?
[45:28] How can we convince human curators to do machine learning?
[47:23] What are the three biggest challenges you've faced when monetizing the value of ML products? How did you overcome them?
[50:25] How do you deal with people measuring costs and values?

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Machine learning has become an increasingly important means for organizations to extract value from their data. Many companies start off with successful proofs of value but face problems when scaling their capabilities afterward. By generalizing engineering problems and solving them centrally, scaling becomes much more feasible. Model serving platforms generalize the problem of turning a machine learning model into a value-generating application. Combining a serving platform with cultural shifts, such as a shift-left approach, enhances efficiency even further.

Bertjan is a Senior Data Engineer with 15 years of experience in the software industry, specializing in data science and engineering for the last 10 years. He built a variety of data products and machine learning platforms. He has worked on both traditional desktop applications as well as cloud native applications in DevOps teams. He's a craftsman with a passion for delivering value through high-quality software, aligning stakeholders, and coaching junior and mid-level team members.

Axel has a background in data science. While getting his data science master's degree, he did software engineering and data science projects for a wide range of customers. This experience taught him that the main complexity of data science projects lies in the software built around the predictive models. After finishing his degree, he joined BigData Republic. Axel currently helps companies bring their data science capabilities to the next level. His main interest lies in tooling that speeds up the development of machine learning applications.

Join our Slack community: https://go.mlops.community/slack
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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/
Connect with Cris Sterry on LinkedIn: https://www.linkedin.com/in/chrissterry/
Connect with Bertjan on LinkedIn: https://www.linkedin.com/in/bertjanbroeksema/
Connect with Axel on LinkedIn: https://www.linkedin.com/in/axel-goblet-5325327a/

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David & Elle talk about how one of the staples of DevOps, the practice of continuous integration, can work for machine learning. Continuous integration is a tried-and-true method for speeding up development cycles and rapidly releasing software- an area where data science and ML could use some help. Making continuous integration work for ML has been challenging in the past, and we chat about new open-source tools and approaches in the Git ecosystem for leveling up development processes with big models and datasets.

|| Highlights ||
What is continuous integration, and why should ML/data science teams know about it?
Why ML projects tend to fall short of DevOps best practices, like frequent check-ins and testing
How we're dealing with obstacles to get continuous integration working for ML
Also, some fun chat about how data science roles are changing and how MLOps skills fit into the data science toolkit!

The DevOps Handbook: https://amzn.to/2XH7tIT

Join our Slack community: https://go.mlops.community/slack
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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/
Connect with Elle on LinkedIn: https://www.linkedin.com/in/elle-o-brien-2a4586100/

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Airflow is a renowned tool for data engineering. It helps with orchestrating ETL workloads, and it's well-regarded amongst machine learning engineers as well. So, how does Airflow work, and how is it applied to MLOps?

In this episode, Demetrios and David are joined by Simon Darr, a Managing Consultant at Servian, with many years of experience using Airflow, along with Byron Allen, a Senior Consultant at Servian, specializing in ML. The group discusses how Airflow works, its pros and cons for MLOps, and how it is used in practice, along with a short demo.

|| Links Referenced in the Show ||
Maxime Beauchemin on Medium https://medium.com/@maximebeauchemin
The Rise of the Data Engineer: https://www.freecodecamp.org/news/the-rise-of-the-data-engineer-91be18f1e603/
Using Airflow with Kubernetes at Benevolent AI: https://www.benevolent.com/engineering-blog/using-airflow-with-kubernetes-at-benevolentai

|| Sponsored Content ||
Servian is a global data consultancy, providing advisory and delivery for data engineering and ML/AI projects. Accelerate ML is their framework to streamline and maximize the impact of ML workflows on an organization. As a part of that framework, they have a free tool used to help clients understand ML maturity. Check out the framework here, along with the ML maturity assessment.

Accelerate ML framework: https://www.servian.com/accelerate-ml/
ML Maturity Assessment: https://forms.gle/4ZN9htWjSUsSBkfd7

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/
Connect with Simon on LinkedIn: https://www.linkedin.com/in/sdarr/
Connect with Byron on LinkedIn: https://www.linkedin.com/in/byronaallen/

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Most MLOps discussion traditionally focuses on model deployment, containerization, and model serving - but where do the inputs come from, and where do the outputs get used? In this session, we demystify parts of the data science process used to create the all-important target variable and design machine learning experiments.

We discuss some probability and statistical concepts that are useful for MLOps professionals. Knowledge of these concepts may assist practitioners working closely with data scientists or those who aspire to build complex experimentation frameworks.

Danny is a recovering data scientist who has moved over to the dark side of ML engineering in the past 2 years. He has spent multiple years deploying ML models and designing customer experiments in the retail and banking sectors. Danny's passion is to guide businesses and individuals on their AI & machine learning journey. He believes a clear understanding of data strategy and applied machine learning will be a key differentiator in this brave new world. He currently provides personalized mentorship for 400+ aspiring data professionals through the #DataWithDanny community.

Join our Slack community: https://go.mlops.community/slack
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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Cris Sterry on LinkedIn: https://www.linkedin.com/in/chrissterry/
Connect with Danny on LinkedIn: https://www.linkedin.com/in/dannykcma/