With the start of the new year, I've been meaning to write more about the topics that really interest me, particularly AI and Machine Learning (ML). So, I am taking advantage of the new year to write posts that explore all of the latest developments in the field and the real challenges people face when working with AI systems.
My Passion for AI and the Struggles I’ve Observed
I'm very passionate about artificial intelligence and machine learning and integrating AI into existing projects and services. However, in my experience, I have seen that a number of people really struggle with actually putting all this together, including model training to model deployment, MLOPs, Governance, and Explainability. But mostly turning projects into production-level software.
The Gap Between Resources and Real-World Applications
There are an enormous amount of applications, libraries, services, and resources available for AI and Machine Learning. However, in my experience, most documentation and tutorials only cover very basic examples. For the most part, they don't discuss how to integrate all of these into a unified platform. Frequently, AI initiatives fail because there is a lack of preparation and knowledge to effectively employ AI in a way that allows companies and people to succeed.
What Is AI Adoption?
All of this falls under the umbrella term or what I like to call AI Adoption. AI Adoption is a set of frameworks and best practices for incorporating AI and machine learning into real-world applications effectively and sustainably. This encompasses the whole lifecycle of AI projects from the initial idea to model development and deployment, as well as observability that alerts you to the need to either retrain your algorithms or start fresh and completely develop new models and processes.
My Goal for Writing These Articles
In writing these articles, my goal is to demystify this process and provide easy-to-understand insights, tutorials, tool and software reviews, as well as the insights and strategies that I have learned on implementing successful AI-driven solutions that work as expected.
Topics to Look Forward to This Year
This next year, I'm going to explore topics such as:
- Designing robust solutions.
- Simplifying workflows through automation and incorporating practical toolsets.
- Providing useful resources that I have found to be extremely helpful in my AI and machine learning career.
- Designing robust ML pipelines.
- Setting up scalable MLOps systems.
- Ensuring model governance and compliance.
- Creating explainable AI solutions.
Additionally, I’ll be discussing case studies, common pitfalls, and lessons I’ve learned along the way.
A Resource for Building AI and ML Skillsets
Hopefully, these posts will serve as a resource for those seeking to build their AI and ML skillsets, given the challenges of AI Adoption, and turn their ideas into impactful, production-ready software. If there are any specific questions you’d like me to address, feel free to share them—I look forward to sharing my experiences, leveling up my skills, and interacting with you on this journey!