Own the Unknown™ with Kristen Kehrer

Welcome to the second month of Further's Own the Unknown™ LinkedIn newsletter, which means it is time to introduce you to a new thought leader. Twice monthly, we'll share some of the knowledge we've gained from following, reading, and interviewing some of the most insightful and influential thought leaders on LinkedIn.

This month, we will be interviewing Kristen Kehrer . In this edition, we'll be focusing on her book, Machine Learning Upgrade: A Data Scientist's Guide to MLOps, LLMs, and ML Infrastructure, coauthored with Caleb Kaiser , which was released in August. She also has a LinkedIn Learning course, AI and the Future of Work: Workflows and Modern Tools for Tech Leaders. 

In addition to her thought leadership, she serves as Head of Decision Science and AI at MoneyGram International , Kristen has been delivering innovative and actionable machine learning solutions in industry since 2010 in the utilities, healthcare, and eCommerce. She became a #8 global LinkedIn Top Voice - Data Science & Analytics in 2018 with 98k followers in data science. Creator of Data Moves Me, LLC and previously Faculty/SME at Emeritus Institute of Management. 

Keith McCormick will be interviewing Kristen on October 16th, and we look forward to asking her how ML workflows have evolved in recent years in response to increased scale and the rise of LLMs.

Modern Machine Learning Systems

In the book, some of the forces that gave rise to the MLOps specialty are identified. "Modern machine learning systems are much more akin to products than pipelines. They have many interconnected components and are applied to a much wider range of problems. On a basic technical level, they introduce several difficult challenges:

• Data size and variety
• Complex algorithms
• Continuous model development
• Scalability
• Collaboration
• Ethical considerations and governance.

The Emergence of LLMs Bringing Increased Complexity and Scale

LLMs have dominated the public discussion of AI over the last many months, but as Kristin and Caleb point out, they bring increased complexity with them. "LLMs can understand nuanced language and context, enabling more sophisticated and context-aware AI systems, such as chat agents. However, chat agents require frequent inference (i.e., the process of running inference with a trained AI model to make a prediction or solve a task.) How can we handle 1,000 concurrent users, each requiring inference every few seconds, with a 17-billion parameter model?" It's worth noting that some consider anything below 30 billion parameters to meet the definition of a "small language model."

The authors also mention the rise of Multimodal AI, "integrating LLMs with other deep learning models, such as convolutional neural networks (CNNs)." Many thought leaders, including Further's Cal Al-Dhubaib, believe Multimodal AI will be an essential trend in the coming months.

The Rise of the AI Engineer Role

These forces are changing, or perhaps more accurately adding to, the skill sets needed on a data science team. "One of the trends we're seeing is the rise of the 'AI Engineer' role. There are a growing number of job titles in the data science world, and sometimes, it can be challenging to understand where one role might start and another begins. An AI engineer typically develops traditional ML models, or in the case of LLMs, they may be fine-tuning, building a RAG system, or building LLM-powered applications. AI engineers will have experience with the tools associated with LLMs, like LangChain and vector databases."

Challenges to Data Scientists and their Organizations

Keith will be sure to ask Kristen about some of the insights from her course. All of us in data science roles would agree with her assessment that keeping up with changes professionally has become challenging. From the standpoint of organizations, the challenges are many. They include assessing ROI, especially in the early stages, data privacy and security, AI ethics, and effective model monitoring. These areas are a particular focus at Further, so we can't wait to get Kristen's insights firsthand.

Upcoming interviews and events

If you haven't done so, follow Further here on LinkedIn. That's the best way to get the latest news. And click on "attend" here so that you won't miss the interview with Kristen. You'll also be able to watch the recording in your LinkedIn feed.

Coming up soon the Further team has several events. Cal Al-Dhubaib will be speaking at OSDC West. Three members of the team, Nicolas Decavel-Bueff , Kristy Hollingshead , and Keith McCormick will be speaking at TDWI Orlando. Juliana Novic will serve on a panel at the upcoming Responsible AI Institute event on October 16th, Accelerating Responsible AI: Proven Strategies from Regulated Industries.

Two major events are coming up. Make sure to attend Cal's presentation as part of our Further bimonthly webinar series. Sign up for his discussion of Winning Gen AI experiences: gofurther.com/solutions/ai/building-trust-in-ai-webinar.

And if you are in the Bay Area, mark your calendar for a special event in the Google Cloud offices in Sunnyvale: gofurther.com/solutions/partners/google/leveraging-google-cloud-ai-for-competitive-advantage-in-marketplace.

Finally, we have a major event coming up in November. Join us in Ohio for the Ohio AI Summit, November 20th in Columbus, Ohio: www.ohioaisummit.org.

We have some very insightful and influential Own the Unknown interview guests lined up for the end of this year, including Donald Farmer , Ian Barkin , and Tom Davenport .