Agentic Conversations (formally mlops.community)

What are regulations saying about data privacy?

We are already aware of the importance of using Machine Learning to improve businesses, nevertheless to feed Machine Learning, data is a must, and in many cases, this data might even be considered sensitive information. So, does this mean that with new privacy regulations, access to data will be more and more difficult? ML and Data Science have their days counted? Or Will Machine beat privacy?

Don’t forget to subscribe to the Mlops.community slack and if you’re looking for privacy-preserving solutions, show us some love and give a star to the Synthetic data open-source repo (https://github.com/ydataai/ydata-synthetic)

Useful links:

In this episode, we talked to Elizabeth Chabot, Consultant at Deloitte, about When You Say Data Scientist, Do You Mean Data Engineer? Lessons Learned From StartUp Life.

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// Key takeaways:
If you have a data product that you want to function in production, you need MLOps Education to happen about the data product life cycle, noting that ML is just part of the equation. Titles need to be defined to help outside users understand the differences in roles  

// Abstract:
ML and AI may sound sexy to investors, but if you work in the field, you've probably spent late nights reviewing outputs manually, pored over logs, and run root cause analyses until your eyes hurt. If you've created data products at a company where analytics and data science held no meaning before your arrival, you've probably spent many a late night explaining the basics of data collection, why ETL cannot be half-baked, and that when you create a supervised model, it needs to be supervised. Companies hoping to create a data product can have a data scientist show them how ML/AI can further their product, help them scale, or create better recommendations than their competitors. What companies are not always aware of is that once the algorithm is created, the data scientist is usually handicapped until more data hires are made to build the necessary pipelines and frontend to put the algorithm in production. With the number of unique data titles growing each year, how should the first data-evangelist-wrangler-wizard navigate title assignment?  

// Bio:

Elizabeth is a researcher turned data nerd. With a background in social and clinical sciences, Elizabeth is focused on developing data solutions that focus on creating value adds while allowing the user to make more intelligent decisions.

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**AI and ethical dilemmas**

Some of these questions may sound absurd, but they are for sure making people shift from thinking purely about functional AI capabilities but also to look further to the ethics behind creating such powerful solutions.

- MLOps.Community slack

- TEDx talk - Surviving the Robot Revolution  

- Digital Ethics Whitepaper

MLOps community meetup #41! Last Wednesday was an exciting episode that some attendees couldn't help to ask when the next season of their favorite series! The conversation was around Metaflow: Supercharging Data Scientist Productivity with none other than Netflix’s very own Ravi Kiran Chirravuri.
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// Abstract:
Netflix's unique culture affords its data scientists an extraordinary amount of freedom. They are expected to build, deploy, and operate large machine learning workflows autonomously without the need to be significantly experienced with systems or data engineering. Metaflow, our ML framework (now open-source at metaflow.org), provides them with delightful abstractions to manage their project's lifecycle end-to-end, leveraging the strengths of the cloud: elastic compute and high-throughput storage. In this talk, we preface with our experience working alongside data scientists, present our human-centric design principles when building Machine Learning Infrastructure, and showcase how you can adopt these yourself with ease with open-source Metaflow.
// Bio:
Ravi is an individual contributor to the Machine Learning Infrastructure (MLI) team at Netflix. With almost a decade of industry experience, he has been building large-scale systems focusing on performance, simplified user journeys, and intuitive APIs in MLI and previously Search Indexing and Tensorflow at Google.
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Connect with Ravi on LinkedIn:  https://www.linkedin.com/in/seeravikiran/
Timestamps:
[00:00] - Introduction to Ravi Kiran Chirravuri
[02:21] - Ravi's background
[05:19] - Metaflow: Supercharging Data Scientist Productivity  
[05:31] - Why do we have to build Metaflow?
[06:14] - Infographic of a  very simplified view of a machine learning workflow
[07:01] - "An idea is typically meaningless without execution."
[07:38] - Scheduling  
[08:14] - Life is great!  
[08:24] - Life happens, and things are crashing and burning!
[09:04] - What is Metaflow?
[12:01] - How much do data scientist cares
[12:25] - How infrastructure is needed
[13:03] - What Metaflow does
[13:44] - How can you go about using Metaflow for your data science needs?
[14:20] - People love DAG's  
[16:00] - Baseline
[16:16] - Architecture
[17:28] - Syntax
[19:00] - Vertical Scalability
[21:10] - Horizontal Scalability
[22:59] - Failures are a feature
[23:57] - State Transfer and Persistence
[27:05] - Dependencies
[30:57] - Model Ops: Versioning
[33:19] - Monitoring in Notebooks
[35:16] - Decouple Orchestration
[36:48] - AWS Step Functions
[37:16] - Export to AWS Step Functions
[38:10] - From Prototype to Production and Back
[42:07] - What are the prerequisites to use Metaflow?
[43:32] - Where does Metaflow store everything?
[45:10] - Are there any tutorials available?
[45:22] - Have the tutorials been updated?   
[47:27] - How do you deploy Metaflow?
[49:02] - Do you see Metaflow becoming a tool to develop and support auto ML?
[50:34] - What were some of the biggest learnings that you saw people doing that they're not doing on Netflix?
[52:19] - Does Metaflow exist to help data scientists orchestrate everything?
[54:30] - What is your version?

Coffee Sessions #18  with Luigi Patruno of ML in Production, a Centralized Repository of Best Practices

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Summary
Luigi Patruno and ML in production
MLOps workflow: Knowledge sharing and best practices

Objective: learn!

Links:

ML in production: https://mlinproduction.com/

Why you start MLinProduction: https://mlinproduction.com/why-i-started-mlinproduction/

Luigi Patruno: a man whose goal is to help data scientists, ML engineers, and AI product managers build and operate machine learning systems in production.

Luigi shares with us why he started ML in Production - A lot of relevant content, a lot of clickbait with low standards of quality.

He had an Entrepreneurial itch, and the solution was to start a weekly newsletter. From there, he started creating Blog posts and now teamed up with Sam Charrington of TWIML to create courses on SagMaker ML. 

Applied ML

Best practices

Reading Google and Microsoft papers

Analyzing the tools that are out there, ie, Sagemaker, and how to see the world?

Aimed at making you more effective and efficient at your job

Community questions

Taking some time to answer some community questions!

Who do you learn from? Favorite resources?

Self-taught, papers, talks

Construct the systems

Uber michelangelo

----------------- 📝 Rought notes 📝 ----------------

Any companies that stand out to you in terms of MLOps excellence?

Google, Amazon, Stitchfix: they've had to solve hard problems

Serving ads

Personalization at scale

Vertical problems: within their vertices

Motivated by real challenges

DropBox

Great articles

A great machine learning company

Tools

Sagemaker

Has a course on Sagemaker

Nice lessons baked into the system

Dos and don’ts of MLOps

DO LOG!

Monitor

Automate - manual analysis leads to problems

Do it manually first til you feel confident that you can automate it

Tag, version

Store your training, val, and test sets!

What is his process of identifying use cases that are suitable for machine learning as a solution? How do they proceed methodically?

Start with the business goal

The potential number of users that the solution can benefit

The ability to build a predictive model

Performance x impact = score

Rank problems by this

How developed are the datasets?

What part of the ML in Production process do people underestimate the most? What are the low-hanging fruits that many people don’t take advantage of?

Generate actual value without needing to build the most complex model possible

In the industry, performance is only one part of the equation

How has he seen ML in production evolve over the last few years, and where does he think it's headed next?

More and more tools!

Industry-specific tool taking advantage of ML

The problem is that you must have industry knowledge 

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

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Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/

This is the first episode of a podcast series on Machine Learning and Data privacy.  Machine Learning is the key to the new revolution in many industries. Nevertheless, ML does not exist without data and a lot of it, which in many cases results in the use of sensitive information. With new privacy regulations, access to data is today harder and much more difficult but, does that mean that ML and Data Science has its days counted? Will the Machines beat privacy?  

MLOps level 2: CI/CD pipeline automation

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For a rapid and reliable update of the pipelines in production, you need a robust automated CI/CD system. This automated CI/CD system lets your data scientists rapidly explore new ideas around feature engineering, model architecture, and hyperparameters. They can implement these ideas and automatically build, test, and deploy the new pipeline components to the target environment.

Figure 4. CI/CD and automated ML pipeline.  
This MLOps setup includes the following components:  
Source control
Test and build services
Deployment services
Model registry
Feature store
ML metadata store
ML pipeline orchestrator
Characteristics of the stages discussion.  

Figure 5. Stages of the CI/CD automated ML pipeline.  
The pipeline consists of the following stages:
Development and experimentation: You iteratively try out new ML algorithms and new modeling, where the experiment steps are orchestrated. The output of this stage is the source code of the ML pipeline steps that are then pushed to a source repository.  

Pipeline continuous integration: You build source code and run various tests. The outputs of this stage are pipeline components (packages, executables, and artifacts) to be deployed in a later stage.  

Pipeline continuous delivery: You deploy the artifacts produced by the CI stage to the target environment. The output of this stage is a deployed pipeline with the new implementation of the model.  

Automated triggering: The pipeline is automatically executed in production based on a schedule or in response to a trigger. The output of this stage is a trained model that is pushed to the model registry.  

Model continuous delivery: You serve the trained model as a prediction service for the predictions. The output of this stage is a deployed model prediction service.  
Monitoring: You collect statistics on the model performance based on live data. The output of this stage is a trigger to execute the pipeline or to execute a new experiment cycle.  The data analysis step is still a manual process for data scientists before the pipeline starts a new iteration of the experiment. The model analysis step is also a manual process.

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MLOps community meetup #40! Last Wednesday, we talked to Theofilos Papapanagiotou, Data Science Architect at Prosus, about Hands-on Serving Models Using KFserving.

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// Abstract:
We looked at some popular model formats like the SavedModel of Tensorflow, the Model Archiver of PyTorch, pickle&ONNX, to understand how the weights of the NN are saved there, the graph, and the signature concepts.
We discussed the relevant resources of the deployment stack of Istio (the Ingress gateway, the sidecar, and the virtual service) and Knative (the service and revisions), as well as Kubeflow and KFServing. Then we got into the design details of KFServing, its custom resources, the controller and webhooks, the logging, and configuration.
We spent a large part in the monitoring stack, the metrics of the servable (memory footprint, latency, number of requests), as well as the model metrics like the graph, init/restore latencies, the optimizations, and the runtime metrics, which end up in Prometheus. We looked at the inference payload and prediction logging to observe drifts and trigger the retraining of the pipeline.
Finally, a few words about the awesome community and the roadmap of the project on multi-model serving and inference routing graph.
// Bio:
Theo is a recovering Unix Engineer with 20 years of work experience in Telcos, on internet services, video delivery, and cybersecurity. He is also a university student for life; BSc in CS 1999, MSc in Data Coms 2008, and MSc in AI 2017.
Nowadays, he calls himself an ML Engineer, as he expresses his passion for System Engineering and Machine Learning.
His analytical thinking is driven by curiosity and a hacker spirit. He has skills that span a variety of different areas: Statistics, Programming, Databases, Distributed Systems, and Visualization.
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Connect with Theofilos on LinkedIn:  https://linkedin.com/in/theofpa

MLOps community meetup #39! Last week, we talked to Ivan Nardini, Customer Engineer at SAS, about Operationalize Open Source Models with SAS Open Model Manager.  

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// Abstract:
Analytics are Open.  
According to their nature, Open Source technologies allow an agile development of the models, but this results in difficulty putting them in production.  The goal of SAS is to support customers in operationalizing analytics. In this meetup, I present SAS Open Model Manager, a containerized Modelops tool that accelerates deployment processes and, once in production, allows monitoring your models (SAS and Open Source).  

// Bio:
As a member of the Pre-Sales CI & Analytics Support Team, I specialized in ModelOps and Decisioning. I've been involved in operationalizing analytics using different open-source technologies in a variety of industries. My focus is on providing solutions to deploy, monitor, and govern models in production and optimize business decision-making processes. To reach this goal, I work with software technologies (SAS Viya platform, Container, CI/CD tools) and Cloud (AWS).  

//Other Links you can check Ivan on:
https://medium.com/@ivannardini

----------- Connect With Us ✌️-------------  
Join our Slack community:  
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Follow us on Twitter:  @mlopscommunity
Sign up for the next meetup:  
https://go.mlops.community/register

Connect with Demetrios on LinkedIn:  
https://www.linkedin.com/in/dpbrinkm/
Connect with Ivan on LinkedIn:  
https://www.linkedin.com/in/ivan-nardiniDescription

Timestamps:

0:00 - Intro to Ivan Nardini
3:41 - Operationalize Open Source Models with SAS Open Model Manager slide
4:21 - Agenda
5:01 - What is ModelOps, and what is the difference between MLOps and ModelOps?
6:19 - "Do I look like an expert?" Ivan's Background
7:12 - Why ModelOps?
7:20 - Operationalizing Analytics
8:12 - Operationalizing Analytics: SAS
9:08 - Operationalizing Analytics: Customer
11:36 - What's a model for you?
12:07 - Hidden Complexity in ML Systems
12:52 - Hidden Complexity in ML Systems: Business Prospective
14:12 - Hidden Complexity in ML Systems: IT Prospective
17:12 - One of the hardest things is Security?
17:52 - Hidden Complexity in ML Systems: Analytics Prospective
19:20 - Why ModelOps?
20:09 - ModelOps technologies Map
22:29 - Customers ModelOps Maturity over Technology Propensity. MLOps Maturity vs. Technology Propensity
26:23 - Show us your Analytical Models
26:56 - SAS can support you to ship them in production, providing Governance and Decisioning.
27:28 - When you talk to people, is there something that you feel like there is a unified model, but you're focusing on the wrong thing?
29:14 - Have you seen Reproducibility and Governance?
30:47 - Advertising Time
30:55 - Operationalize Open Source Models with SAS Open Model Manager
31:02 - ModelOps with SAS
32:06 - SAS Open Model Manager
33:18 - Demo
33:27 - SAS Model Ops Architecture - Classification Model
35:02 - Model Demo: Credit Scoring Business Application
50:20 - Take Homes
50:24 - Operationalize Analytics  
50:32 - Model Lifecycle Effort Side
51:20 - Business Value Side
51:47 - Typical Analytics Operationalization Graph
52:18 - Analytics Operationalization with ModelOps Graph
53:18 - Is this for everybody?

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//Bio
Satish built compilers, profilers, IDEs, and other dev tools for over a decade. At Microsoft Research, he saw his colleagues solving hard program analysis problems using Machine Learning. That is when he got curious and started learning. His approach to ML is influenced by his software engineering background of building things for production.   He has a keen interest in doing ML in production, which is a lot more than training and tuning the models. The first step is to understand the product and business context, then build an efficient pipeline, train models, and finally monitor its efficacy and impact on the business.  He considers ML as another tool in the software engineering toolbox, albeit a very powerful one.  He is a co-founder of Slang Labs, a Voice Assistant as a Service platform for building in-app voice assistants.  

// Talk Takeaways

ML-driven product features will grow manifold. Organizations take an evolutionary approach to absorb tech innovations. ML will be no exception. How Organizations adopted the cloud can offer useful lessons.
ML/DS folks who invest in an understanding business context and tech environment of the org will make a bigger impact.
Organizations that invest in data infrastructure will be more successful in extracting value from machine learning.  

//Other links you can check Satish on
An Engineer’s Trek into Machine Learning:  
https://scgupta.link/ml-intro-for-developers
Architecture for High-Throughput Low-Latency Big Data Pipeline on Cloud:
https://scgupta.link/big-data-pipeline-architecture
Data pipeline article:
https://scgupta.link/big-data-pipeline-architecture or
https://towardsdatascience.com/scalable-efficient-big-data-analytics-machine-learning-pipeline-architecture-on-cloud-4d59efc092b5
Tips for software engineers based on my experience of getting into ML:
https://scgupta.link/ml-intro-for-developers or https://towardsdatascience.com/software-engineers-trek-into-machine-learning-46b45895d9e0
Twitter:
https://twitter.com/scgupta
Personal Website:
http://scgupta.me
Company Website:
https://slanglabs.in
Voice Assistants info:
https://www.slanglabs.in/voice-assistants

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

Connect with Satish on LinkedIn:

⁠https://www.linkedin.com/in/scgupta

Timestamps:
0:00 - Intro to Satish Chandra Gupta
1:05 - Background of Satish on Machine Learning
3:29 - Satish's background on what he's doing now
5:34 - Why were you interested in the challenges of the workload?
9:53 - As you're looking at the data pipeline, do you see much overlap there?
15:38 - Relationships between engineering pipeline characteristics and how they relate to data.
20:24 - Tips for saving when you're building these pipelines.
24:44 - First point of engagement: Collection
31:26 - Possibilities of Data Architecture
38:03 - Why is it beneficial to save money?
44:22 - Learnings of Satish with his current project, Voice Assistant as a service.