Thanks to all my subscribers to Enterprise Data Science Substack
I want to thank all my subscribers to my Enterprise Data Science Substack over the past year. As we build Enterprise Data Science capacity in our organizations, let’s recap key articles from 2023.
Know your True Worth as an Enterprise Data Scientist
I’ve reached a milestone in my career as an Enterprise Data Scientist- I’m rejecting contracts which I used to be so desperate for, just a few years ago. It’s because I know my worth- I value my time, both at work and at home. But I get it- the struggle is real when starting out as a data scientist doing AI work, and th…
How to Implement AI Governance within an MLOps and ModelOps Framework
Artificial Intelligence (AI) has the potential to revolutionize businesses, but with that potential comes the responsibility to ensure that AI is developed, deployed, and managed in a responsible and ethical manner. AI Governance is the process of creating policies, procedures, and standards for the development and use of AI, and it is becoming increasi…
The Difference Between Local vs Enterprise AI Solutions
People see a data scientist training an ML model, then ‘deploying’ the model on unseen data- some may confuse this as deployment in production. Indeed, some are confused that the data scientist already ‘deployed’ their ML model. This confusion may stem from the apparent blurring between exploration, development, and deployment…
Enough Talk- It’s Time to Deliver for Enterprise Data Science
When in an AI-naïve organization, you will hear a lot of noise and talk about how to move forward with data science and AI/ML. And then you find out that these discussions on bringing in AI to the organization have been ongoing for years, and still have no viable way to deploy AI/ML models in production, at Enterprise scale.
AI Governance is Required for Enterprise AI Applications
Generative AI applications are currently disruptive to enterprises, as many organizations are scrambling to set up guardrails and policies to catch up with this new technology. This technology has massive appeal to business lines, and many are prototyping with Generative AI for their business use cases. Additional…
Building complete and responsible AI systems: the importance of the purple unicorn role
Many enterprises struggle to build complete and responsible AI systems. At a high level, you have product managers moving forward with AI use cases and claim IT doesn’t know the business. Fair enough- that is true in many cases.Thanks for reading Enterprise Data Science! Subscribe for free to receive new…
Maturing AI in AI-Naive Enterprises via the Generative AI Hype
Many AI-naive organizations have bought into the Generative AI hype brought about by the latest Large Language Models that have been globally accessible to the public, notably to laypersons. When BERT and ELMo came out in 2018, this was mainly an NLP tool for data scientists, but ChatGPT released in late 2022 made it available to eve…
Build Large IT Projects at Enterprise Scale to Stay Relevant and Marketable
In a rapidly evolving tech landscape, staying relevant and marketable is a constant challenge for developers. Whether you’re seeking a more meaningful IT role or have recently faced a layoff, the path to securing your career lies in taking ownership of large-scale IT projects. Focusing on projects that demonstrate your author…
Transitioning from Technical Lead to IT Strategist
In the transition from a Technical Lead to an IT Strategist, there is a fundamental shift in how one thinks and solves complex problems. I will discuss the differences between a Technical Lead as an individual contributor for IT projects, and an IT Strategist as an executive advisor for the Enterprise.
Critical Path Considerations for Deploying AI/ML Solutions in Production
At many Enterprise organizations with low AI maturity, there is an over-reliance on AI researchers and local AI prototypes are mistaken for complete ML systems. The fancy dashboards and visualizations and the predictions and forecasts impress the executives and payors, and soon everyone wants interactive dashboards for data driven decision making.
The Value of MLOps for Operationalization of AI Solutions Beyond a Single Use Case
In a previous article I spoke about deploying AI/ML solutions rapidly on Serverless ML, so that AI-naive Enterprises can evaluate the value of shifting from AI prototypes to an operational AI/ML system. So now that you deployed your first AI/ML solution in production for a very specific use case, how do you repeat this and scale this to other use cases?…
The Deployment Gap in Machine Learning Solutions is Addressed with MLOps
Enterprise organizations that are AI-naive often start their journey to build data science capacity by hiring a platoon of data scientists. They focus on the organizations’ mission, use cases, domain data and domain experts to carry out their data science research and build multiple AI prototypes to get buy-in for AI/ML solutions to their managers and e…
How Enterprise Organizations Get Value from Data Science/ Machine Learning
Congratulations! You are an Enterprise organization that has taken the crucial first step in the implementation of Data Science and Machine Learning (DS/ML). You’ve successfully deployed a basic ML project in your production environment, and that in itself is no small feat. You’ve dedicated time and resources to build a Proof of Concept, secure an Autho…
Enterprise AI High Level Design for Enterprise AI Solutions
When starting out with Enterprise Data Science at your organization, you may find that your company has already hired data scientists to build AI point solutions in the various departments, for a specific use case. However, when they try to scale their point solution to the Enterprise, the individual AI project teams don’t have the proper solutions arch…
If you're looking for support from me, here are a few options:
Enterprise Data Science Consultancy: With my consult team comprised of a Senior Data Scientist, Senior ML Engineer, Senior Data Engineer, and Senior Cloud Engineer, we will help you architect and build your Enterprise Data Science platform, and transfer knowledge to your IT team to maintain and optimize it. We will also overlay an MLOps framework to manage the AI solutions you build on this platform. If you don’t have an MLOps team, we will help you build one. Please get in touch about this consultancy here
Coaching and Mentorship: I offer coaching and mentorship; book a coaching session here