🎯 Mark Freeman II

Gable

• Joined as employee-one to lead GTM at Gable to help us reach our first customers.

• Reported directly to the CEO and led the development and execution the go-to-market
strategy which resulted in a successful launch, 4000+ leads, and 300+ meetings with
senior data engineers and leaders, ranging from scaleups to Fortune 500 companies—
this was integral for our ability to raise a Series A. (https://bit.ly/4eDwbPE)

• Co-authored the O’Reilly book "Data Contracts: Developing Production Grade Pipelines at Scale,"
contributing all the technical chapters, and positioning Gable as thought leaders of an
emerging category within the data market. (https://bit.ly/3XIu4mF)

• Served as a technical expert by presenting at conferences on data engineering topics and providing thought leadership via blogs.

• Built Gable’s marketing and sales stack with an emphasis on data capture, automation,
and analytics that enabled automated qualification, conversion into meetings, and nurture
campaigns for leads—while establishing the underlying data model.

• Served as a sales engineer, mapping technical requirements to Gable’s product roadmap,
building champions among directors plus data leaders, creating data strategy decks for
clients, and closing multiple accounts.

• Served as a data scientist, providing insights and visualizations for Gable’s executive team
for presenting sales metrics to the board and potential investors.

Tech Lead, GTM Engineering

On the Mark Data

I help brands connect to data professionals via content strategy, GTM strategy, and influencer marketing. Learn more at onthemarkdata.com.

• Built a brand with an audience of 40k+ data professionals via social media content, technical blogs, and video-- which has harnessed over 14MM impressions.

• Established the weekly Scaling DataOps newsletter, with 7k+ subscribers, where I interview data leaders and executives on data best practices in scaling data infrastructure.

• Built a revenue stream around sponsored content from top data brands such as Nvidia, SuperDataScience, Scale AI, and much more.

Founder

O'Reilly

Currently writing a book on data contracts.

⭐️ "This book has the potential to become the definitive guide for data contracts! I can't wait to dive into the rest of it and unlock even more valuable insights!"

⭐️ "The initial read is clear and provide a good potted history of the data problem and how it has organically morphed due to so many factors. I can't wait for the rest of the book"

⭐️ "This book has the potential to become the bible of data contracts especially as the world moves towards intelligent applications. Data is essential to applications that use AI, and having a good contract is paramount towards establishing trust between systems and having a seamless experience. Looking forward to the full publication!"

Author - Data Contracts: Developing Production Grade Pipelines at Scale

LinkedIn

⭐️ "Took a few hours out of my Saturday to dive into data engineering with DBT, and had the opportunity to pair program with Mark... Great work on the new course, Mark. I sure learned a lot, and I'm confident others will benefit from it as well. As someone who's relatively new to data engineering, this short course was an eye-opener. It expanded my understanding of the scalability and possibilities within the field."

⭐️ "I completed Mark's course over the weekend and thought it was a GREAT introduction to using DBT. For people looking for a low-stakes, easy-to-follow introduction to DBT as a tool for data engineering, I think you would be pressed to find something more effective."

⭐️ "I would like to thank Mark... for creating such a comprehensive, engaging & pure hands on course on dbt. He explained the concepts clearly & demonstrated them with practical examples and exercises."

⭐️ "I was excited when I saw this course on your page, i know this is going to be the course I needed to start my DBT journey and you did not disappoint, Thank you"

LinkedIn Learning Instructor, Data Engineering

Humu

• Build ETL pipelines to bring third-party data sourced from APIs to combine with our product data, creating rich data assets to drive product feature creation and customer analytics.

• Built buy-in, developed the transition plan, and led the migration of the orchestration of data transformations from manually created scheduled queries to data build tool (dbt)-- which enabled version control of our semantic layer, data quality checks, and data discoverability of data assets within our data warehouse.

• Led the development of an internal data quality engine to increase test coverage of core data pipelines and implement data observability.

Senior Data Engineer

Driving forward the organization’s data strategy by:
• 1) Conducting user interviews to understand the organization’s data pain points.
• 2) Identifying the need for data marts to increase data access.
• 3) Scoping overhaul of our data pipelines and warehouse.
• 4) Sourcing data ingestion vendors.
• 5) Fostering leadership buy-in via building minimum viable products.

Senior Data Scientist (Data Engineering)

• Led the development of the Data Science team’s custom R package and managed the internal distribution and upgrading.

• Implemented CI/CD utilizing Docker and CircleCI to ensure changes to the Data Science codebase passes tests and prevent breaks to our internal packages.

• Assisted in doubling the Data Science team via interviewing, onboarding, and technical mentorship for upskilling colleagues.

Senior Data Scientist

• Developed and put into production an NLP pipeline via Python that takes raw text data, classifies respective texts' themes, and ranks and assigns text to relevant insights within customer reports (https://bit.ly/3yAeBqu).

• Designed and put into production via Python an algorithm to classify user activity—output became org’s main product health metric and was validated with both statistical significance and UX research interviews (https://bit.ly/3yt5QhY).

• Support the VP of Product in strategic product decisions by refining business questions, conducting log analytics via SQL and R, and creating reports shared org-wide.

Data Scientist

IndexoRx

• Made it to the final round of interviews for Y Combinator, where we presented our pitch deck to their investment team and described the business opportunity within the pharmacy space in reducing the burden of DIR fees.

• Developed an MVP of our data product that utilized analytics and machine learning to rank users at risk for lowered medication adherence and forecast DIR fees.

• Conducted deep market research on pharmacy business models, pharmacy supply chains, and PBM reimbursement processes.

Founder, Chief Data Officer

Bay Area Community Health Advisory Council

• Lead organization’s evaluation efforts through developing a standardized survey utilizing CSAT and NPS scores, transitioning administration from paper to Google Forms, and providing analysis and data visualizations of services data—resulting in a 24% increase in survey response rate and 2 reports used by org’s Executive Director for fundraising

Member / Evaluation & Analytics Committee Chair

Verana Health

• Utilize deep understanding of medical claims and EHR data, in conjunction with strong SQL and PySpark skills, to work with data that contains over 63 million patients, 274 million patient encounters, and over 10 billion data points amongst 55 separate EHRs

• Developed and deployed into production an algorithm via Apache Spark that classified insurance types for over 250 million records, utilizing unstructured EHR data, that improved both accuracy from 40% to 84% and reduced null values from 16% to <1%

• Support sales team in 21 potential contracts by determining viability of our data assets through defining research and business questions, developing SQL and PySpark code to build cohorts, providing insights on quality issues within data, and creating data reports to communicate findings to non-technical staff

• Conduct an algorithm assessment on a key variable and identified errors impacting 1.3 billion records, as well as provide recommendations to the algorithms team to rectify the error—assessment was utilized to help certify new data architecture

Data Scientist (Data Quality)

Stanford University School of Medicine

• Developed R script for department’s ETL process, that transforms data into cleaned variables and removed duplicate observations based on defined logic—reducing data cleaning time of analysts by 10 hours per project

• Completed an exploratory analysis through conducting 48 multiple regression models to understand relationship between biometric data and self-reported wellbeing data, thus identifying 3 viable hypotheses to further investigate and ensuring the lab diverts resources to impactful studies 

• Updated methods of a previous propensity score match analysis, lowering the SDM for all variables from 0.17-0.21 to <0.10, resulting in the analysis now meeting required match assumptions and improving the manuscript strength for publishing: matching accounts for observed confounder bias

Data Analyst

Collective Health

• Developed and operationalized a Python script, utilizing pandas, that identifies discrepancies between insurance enrollment data and network partner data— reduced manual work process time from 98 to 16.3 hours a year (time saved grows with number of added clients)

• Developed and operationalized a Python script, utilizing pandas, that takes 3 sources of data and creates custom reports for respective insurance partners’ specification— reduced manual work process time from 52 to 4.3 hours a year

• Utilize SQL to pull necessary data to resolve insurance eligibility issues and other related operations work

Operations Associate

onthemarkdata.com | Senior data engineer helping brands with GTM and content strategy.

I'm a community health advocate turned data engineer interested in the intersection of social impact, business, and technology. My life’s mission is to improve the well-being of as many people as possible through data—especially among those marginalized.
 
As the founder of On the Mark Data, I use this platform to help brands connect to data professionals through captivating content.
 
I received my M.S. from the Stanford School of Medicine and I'm also certified in Entrepreneurship and Innovation from the Stanford Graduate School of Business. In addition, I have worked within numerous startups where I have put machine learning models into production, integrated data analytics into products, and led migrations to improve data infrastructure.
 
The culmination of my technical skills, content success, and experience within startups would make me an asset whether you are looking to create a successful content strategy, engage in influencer marketing campaigns, or develop a go-to-market strategy for your startup.

You can learn more about opportunities to collaborate, my media appearances, or my content at onthemarkdata.com.

• Joined as employee one to lead GTM at Gable to help us reach our first customers.
• Worked with the CEO to establish our GTM strategy, content strategy, and messaging for technical audiences.
• Building internal data products to drive our GTM strategy and sales.

It didn't feel real until I had the physical copy of the book in my hands, but I'm officially a published author!

This is just such a full circle moment for me as I was reading these O'Reilly animal books when I was dreaming of making a career in data.

If you told me nearly 7 years ago that I would have my name on one I wouldn't believe you!

This represents about two years of life of nights and weekends on top of helping build a startup (Gable) and my own company (On the Mark Data).

Writing a book and building a company was hard... but I would easily do it all over again.

Hundreds of user interviews and discovery calls informed us what would be meaningful in the book.

While the book provided us clear synthesis of what we were seeing in industry.

This created a symbiotic relationship where we would build, write, get feedback, and repeat.

A huge shoutout to the O'Reilly team and my co-authors Chad and Ben!

An even bigger shoutout to everyone who supported us, as it wouldn't have been possible without you all.

Data Engineer | Tech Lead @ Gable.ai | O’Reilly Author: Data Contracts | LinkedIn [in]structor (30k+ Learners) | Founder @ On the Mark Data

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Here's my tech stack for the dev environments I create for LinkedIn Learning courses (all open source) that you can use for yourself:

🐋 Dev Environment:
- Docker Compose - Containers and configs
- VSCode Dev Containers - IDE in container
- GitHub CodeSpaces - Browser cloud compute

📦 Databases:
- Postgres - Transactional Database
- Minio - Data Lake
- DuckDB - Analytical Database

⚙️ Ingestion + Orchestration + Logs:
- Python Scripts - Simplicity over a tool
- Data Build Tool - SQL queries on DuckDB
- Alembic - Python-based database migrations
- Psycopg - Interact with postgres via Python

📋 CI/CD:
GitHub Actions - Simple for students
 
📊 Data:
Data[.]gov - Public real-world datasets

👩🏽‍💻 Coding Surface:
Jupyter Notebooks - Quick and iterative
VS Code - Update and implement scripts

This setup is extremely powerful as you have a full data platform that sets up in minutes, it's filled with real-world data, you can query it right away, and you can see the logs. Plus, since we are using GitHub codespaces, it's essentially free to run in the browser with just a couple clicks!

Innovation isn't about creating completely brand new technologies and practices. Instead, it can be applying a proven method to a new domain.

Under this framing, data engineering teams can learn a lot from clinical research methodologies.

My stats professor in grad school used to joke around that someone messed up on research design if he had to use advanced statistics.

Why? You can avoid jumping through so many technical hoops if you control for people and process!

Here's how I see it applying to data systems:

A - clinical) How do we ensure fidelity (ie research staff follow protocols correctly) across the steps of data capture for the study?

A - data) How do we ensure the business processes for capturing and processing data stays consistent across staff (eg data entry)?

B - clinical) How do we store patient data, and what is the least amount of information we need to reduce risk for a HIPAA violation?

B - data) What data values are considered PII, how do we retain and delete such records, and what regulations do we need to handle?

C - clinical) What are we trying to compare (eg treatment vs control), and thus what measurements and demographics do we need to capture to properly assess this?

C - data) What telemetry do we need on a product to answer questions about usage of a product or service?

Nine times out of ten, the problem you are solving for already has a solution in a different context!

Stop looking at problems at a surface level and instead understand the "pattern" of the problem, and the find similar patterns in spaces where the problem has both been solved and well documented. It's why interdisciplinary teams are the most innovative!

This is also exactly how we landed on "shift left data" in our data contract book by the way! We saw the same problem pattern in DevOps and how they used "shift left" to address it.

"What do you mean my ROI would only be 1.5x? I need it to be a 4x for it to make sense..."

That brutal honesty was one of the best lessons I've learned in getting adoption and budget for data infrastructure projects.

I spent days making a proposal for a c-suite exec I was trying to sell to. I identified their core problems, was getting multiple meetings, and we were talking about numbers... and that's where I messed up.

My mistake was assuming this was their only problem they were considering for purchasing, and not part of a portfolio of problems they were strategically trying to solve.

I wasn't "competing" against a problem I knew I could clearly solve and generate ROI on. Instead, I was competing with the opportunity cost of not pursuing otherr solutions with various multiples of ROI for different problems.

That meeting completely changed how I think about value to execs. It's funny how obvious it sounds looking back, but I'm hard headed and needed to learn by experience.

Fast forward to today, and I feel like I have had enough reps to dive into the business technicals of what value actually means and map it to my experiences.

This week I'm reading the book Valuation, which was essentially the playbook used by McKinsey consultants. Specifically, I want to take my understanding of what is valuable to a business to next level with economic theory and technical business fundamentals.

I think more technical people should study this as well! Move away from parroting the abstract phrase "we need to focus on driving business value" and instead provide a clear plan to what that actually means and why a leader shoukd choose to invest in your initiative over others.

Allowing others to explore AI >>> Gatekeeping AI

I still remember the first time I saw a neural network work in real-time (shoutout the MarI/O Youtube video by SethBling) and just the magic I felt.

That was the moment I realized I needed to make a career in data and the moment I got over my fear of coding.

So many non-technical, but highly skilled in their domain, professionals are now experiencing exactly what I felt years ago.

Why try and police that?

Yeah, that vibe coded app is absolute crap and one agentic action away from deleting its own databases... but that person shipped something.

That's already more progress than the technical naysayers shouting from the sideline.

Greg Coquillo

Product Leader @AWS | Startup Investor | 2X Linkedin Top Voice for AI, Data Science, Tech, and Innovation | Quantum Computing & Web 3.0 | I build software that scales AI/ML Network infrastructure

Product leader with a passion for solving business use cases such as Growth, Go-to-Market, and Price Optimization. I owned P&L for commodity-based products, and leveraged AI to increase the average customer contract values across business industries. As a Product Leader at Amazon, I own a roadmap that drives the adoption of AI products through the Media Automation charter, which supports Private Brands’ YOY growth. I work with Data Scientists, Software Engineers and Business Intelligence Engineers to leverage Machine Learning and Big Data Analytics to design predictive models that enhance business decision-making and performance to accelerate growth, reduce Time to Market of products, monitor cross-geo compliance and import requirements, and ensure customer safety.

I am always curious and eager to learn how new technology affects our daily lives, and I love to support communities on their quest to adopt Artificial Intelligence across industries. Feel free to contact me if you have Data Science projects that you’d like to be showcased on this platform for visibility.

🌐I am a Global Business Leader with 12 years of experience and skills including Product Management, P&L management, Global Networking, EBITDA Optimization, Pricing Management, Margin Management, Product Management, Program Management, Operations and Supply Chain Management, Lean Manufacturing, Six Sigma, and Continuous Improvement.

🧘🏽‍♂️Soft Skills include: Communication, Collaboration, Lateral Thinking, Adaptability, Inspiration, Innovation, Empathy, Listening, Diplomacy, Organization, Responsibility, Leadership...

🎬🎥🎞 I really enjoy watching movies, and if I could redo it all over, I’d be a Sci-Fi script writer so I can be bounded by the limits of imagination.

(Opinions are my own)

Product Management, Media Automation

Owner of the AI vision and roadmap for Private Brand’s Compliance Division. Driving adoption of new technology, allowing my customers to scale their processes and increase productivity. Core responsibilities include:

-Building cross-functional teams consisting of Data Scientists, Software Engineers, Data Engineers, Product Managers and Program Managers, to plan and implement sophisticated AI projects
-Manage the New Initiative Intake Process
-Gather Business requirement artifacts
-Communicate roadmap definition and prioritization
-Coordinate User Acceptance Testing
-Manage Launch Announcements
-Prepare and communicate annual planning artifacts

Building Agentic AI systems beyond connecting APIs or LLMs is complicated, but not impossible.

This architecture lays the foundation for how AI agents think, communicate, and improve, covering everything from testing and observability to deployment and memory management.

Here’s a breakdown of the key layers and components that make up a scalable Agentic AI Architecture :

1.🔸Decomposition
Break down complex systems by domain (e.g., Coding Agent, Data Agent), by cognitive capability (Reasoning, Planning, Execution), or by agent role (Planner, Executor, Memory Manager, Communicator).

2.🔸Communication
Enable message passing between agents using inter-agent protocols or A2A (Agent-to-Agent) orchestration.
Support both single-agent and multi-agent setups for small or distributed workflows.

3.🔸Deployment
Deploy agents in containerized or serverless environments using Docker or Modal.
Support orchestrators like CrewAI or AutoGen for collective intelligence in multi-agent workflows.

4.🔸Data & Discovery
Integrate knowledge bases (like vector databases for RAG), memory stores (FAISS, Redis, Pinecone), and APIs for dynamic data access.
Context is passed using Model Context Protocol (MCP) for structured and real-time reasoning.

5.🔸Testing & Observability
Validate workflows end-to-end, test reasoning logic, and evaluate performance under real conditions.
Monitor using Weights & Biases, LangFuse, and track metrics like latency and task success rate.

6.🔸UI & Style
Provide intuitive feedback loops through visualization layers, dashboards, and self-reflective modes.
Enable collaborative, proactive, and goal-driven reasoning among multiple agents.

7.🔸Security
Protect access with token-based authorization and data encryption.
Include Trust Layers for human-in-the-loop validation and Policy Enforcement for safe execution.

8.🔸Cross-Cutting Concerns
Handle configuration, secrets, and environment management.
Support flexible frameworks like LangChain, AutoGen, or CrewAI for runtime execution and modular design.

Agentic AI is the future of automation - where AI doesn’t just assist but collaborates and learns.

Save this post to understand the architecture that powers the next generation of AI systems

#AgenticAI

AI agents are great at working alone. However, they can also be effective when operating within networks of collaboration.

The real power of AI lies in how these agents communicate, coordinate, and complement each other’s abilities.

Orchestration patterns define this collaboration which turns isolated agents into intelligent, goal-driven systems capable of solving complex tasks together.

Here are 6 orchestration patterns that power the next generation of AI systems :

1.🔸Sequential Orchestration
Agents work one after another - each passing its result to the next.
Perfect for structured, step-by-step tasks like data pipelines or analysis chains.

2.🔸Concurrent Orchestration
Multiple agents work in parallel, handling different parts of a problem simultaneously.
This boosts speed, scalability, and overall task efficiency.

3.🔸Group Chat Orchestration
Agents communicate in a shared conversation, refining ideas together.
Useful for brainstorming, collaborative decision-making, or generating consensus.

4.🔸Maker–Checker Loops
One agent creates or proposes; another verifies, corrects, or approves.
Ideal for maintaining accuracy, compliance, and quality assurance in workflows.

5.🔸Handoff Orchestration
Agents transfer control dynamically based on context or expertise.
Best suited for adaptive workflows like support systems or multi-step automation.

6.🔸Magnet Orchestration
A central “manager” agent dynamically assigns tasks to the right specialists.
Enables fluid, context-aware collaboration across multiple intelligent agents.

AI Agents are evolving fast and orchestration is what makes them powerful.

Learning how these patterns can help you build smarter, more collaborative AI systems can be a huge advantage.

#AIAgent

AI Agent Orchestration Patterns

📚LLMs in the Enterprise are finally getting the playbook they deserve… LLMs in Enterprise by Ahmed Menshawy and Mahmoud Fahmy provides practical guides for building real AI systems that operate at scale.

Most teams talk about LLMs in theory. This book focuses on execution. It bridges foundational concepts with the hands-on design patterns that matter when you are integrating models into production environments.

Here are the insights that stood out👇

1.🔸Enterprise LLM integration is a data architecture problem
The book breaks down how to design pipelines, tune retrieval, and structure data so models operate with consistency and low latency in real workloads.

2.🔸Scaling LLMs requires pattern-level thinking
They go deep on architectural patterns that reduce complexity, improve efficiency, and streamline deployment. This includes RAG frameworks, fine-tuning strategies, segmentation techniques, and evaluation patterns that teams often overlook.

3.🔸Performance is not just about bigger models
The authors show how to optimize model behavior with advanced inferencing engines, contextual model customization, and monitoring systems that keep applications predictable.

4.🔸Real enterprise value comes from operational rigor
Security, fairness, transparency, and accountability are not afterthoughts. They are part of the design process, especially when LLMs touch business workflows and customer data.

5.🔸AI teams win by mastering both concepts and impact
The flow of the book reflects the real enterprise lifecycle: Concept → Customization → Impact
A clear, structured way to think about turning LLM capabilities into business outcomes.

If you are building production AI systems, leading an LLM program, or preparing for the next wave of enterprise adoption, you should definitely get a copy of this book.

Enterprise AI is evolving fast. Understanding these design patterns early puts at a career advantage which allows you to shape the next generation of intelligent applications.

#LLM

Agentic AI vs Traditional AI: The Shift from Supervision to Autonomy

For years, Traditional AI has been like a skilled worker. It has been efficient, but dependent on human supervision.

Agentic AI, however, takes a different approach. It is a decision-maker that thinks, plans, and adapts without constant direction.

Let’s dive into what truly separates them 👇

Agentic AI: Smarter. Adaptive. Independent.

- Dynamic Learning: Perceives its environment, plans actions, and evaluates outcomes continuously.

🔸Goal-Oriented Thinking: Sets goals, creates sub-goals, and adjusts strategies like a human problem solver.

🔸Reasoning & Reflection: Learns from mistakes, refines decisions, and improves performance over time.

🔸Collaborative Agents: Multiple agents share context, delegate tasks, and synchronize for collective intelligence.

Traditional AI: Powerful but Limited.

🔹Static Learning Models: Train once, deploy, and manually retrain, with no real-time adaptability.

🔹Human-Dependent Execution: Waits for prompts or tasks, performs inference, and stops.

🔹Narrow Focus: Optimized for specific use cases without flexibility.

🔹No Self-Improvement: Requires human supervision for updates and tuning.

In Short:

- Traditional AI executes.

- Agentic AI thinks, plans, and evolves.

It’s the difference between automation and autonomy, a shift that’s redefining how AI systems operate, learn, and collaborate in the real world.

What other diffenreces have you noticed?

#AI

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🎯 Mark Freeman II

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The Battle

🎯 Mark Freeman II

Greg Coquillo

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Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}🎯 Mark Freeman II's LinkedIn Performance

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Performance Score🎯 Mark Freeman II's LinkedIn Performance