- Scale and Govern AI Initiatives with ModelOps, by Giuliano Liguori - Sep 30, 2021.
AI/ML model life cycle automation and orchestration ensures reliable model operations and governance at scale. The path to production for each enterprise model can vary, along with different monitoring, continuous improvement, retirement needs. Organizations must now consider ModelOps as a fundamental capability towards operational excellence and immediate ROIs.
- Data Science Process Lifecycle, by Lillian Pierson, P.E. - Sep 29, 2021.
How would it feel to know that without a doubt, the data projects you were working on would create TRUE ROI for your organization? Stick around until the end to get my data science process lifecycle framework so that each data project you run is a smashing success.
- MLOps and ModelOps: What’s the Difference and Why it Matters, by Stu Bailey - Sep 28, 2021.
These two terms are often used interchangeably. However, there are key distinctions between the functionality and features each provide, and the AI value and scalability at your organization depend on them.
- How Data Scientists Can Compete in the Global Job Market, by Devin Partida - Sep 24, 2021.
Data scientists wanting to stay competitive or break into the field will need the right approach. These techniques will help them search for and secure a new position.
- Nine Tools I Wish I Mastered Before My PhD in Machine Learning, by Aliaksei Mikhailiuk - Sep 22, 2021.
Whether you are building a start up or making scientific breakthroughs these tools will bring your ML pipeline to the next level.
- What 2 years of self-teaching data science taught me, by Vishnu U - Sep 17, 2021.
Many of us self-learn data science from the very beginning. While continuing to self-learn on demand is crucial, especially after you become a professional, there can be many pitfalls early on for learning the wrong way or missing out on key ideas that are important for the real-world application of data science.
- How Many AI Neurons Does It Take to Simulate a Brain Neuron?, by Jesus Rodriguez - Sep 13, 2021.
A new research shows some shocking answers to that question.
- Smart Ingestion: Using ontology-driven AI, by Prad Upadrashta - Sep 8, 2021.
Imagine data that organizes itself to power your decision-making.
- Math 2.0: The Fundamental Importance of Machine Learning, by Dr. Claus Horn - Sep 8, 2021.
Machine learning is not just another way to program computers; it represents a fundamental shift in the way we understand the world. It is Math 2.0.
- Antifragility and Machine Learning, by Prad Upadrashta - Sep 6, 2021.
Our intuition for most products, processes, and even some models might be that they either will get worse over time, or if they fail, they will experience an cascade of more failure. But, what if we could intentionally design systems and models to only get better, even as the world around them gets worse?
- Behind OpenAI Codex: 5 Fascinating Challenges About Building Codex You Didn’t Know About, by Jesus Rodriguez - Sep 3, 2021.
Some ML engineering and modeling challenges encountering during the construction of Codex.
- How to solve machine learning problems in the real world, by Pau Labarta Bajo - Sep 2, 2021.
Becoming a machine learning engineer pro is your goal? Sure, online ML courses and Kaggle-style competitions are great resources to learn the basics. However, the daily job of a ML engineer requires an additional layer of skills that you won’t master through these approaches.