Scale and Govern AI Initiatives with ModelOps
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.
By Giuliano Liguori on Sep 30, 2021 in AI, MLOps, ModelOps, Scalability
Data Science Process Lifecycle
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.
By Lillian Pierson, P.E. on Sep 29, 2021 in Analytics, Data Science, Data Scientist, Workflow
MLOps and ModelOps: What’s the Difference and Why it Matters
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.
By Stu Bailey on Sep 28, 2021 in Enterprise, MLOps, ModelOps
How Data Scientists Can Compete in the Global Job Market
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.
By Devin Partida on Sep 24, 2021 in Career Advice, Data Science, Data Scientist
What 2 years of self-teaching data science taught me
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.
By Vishnu U on Sep 17, 2021 in Advice, Data Science, Data Science Education
How Many AI Neurons Does It Take to Simulate a Brain Neuron?
A new research shows some shocking answers to that question.
By Jesus Rodriguez on Sep 13, 2021 in AI, Brain, Human Intelligence, Humans vs Machines
Smart Ingestion: Using ontology-driven AI
Imagine data that organizes itself to power your decision-making.
By Prad Upadrashta on Sep 8, 2021 in AI, ETL, Ontology
Math 2.0: The Fundamental Importance of Machine Learning
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.
By Dr. Claus Horn on Sep 8, 2021 in AI, Machine Learning, Mathematics
Antifragility and Machine Learning
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?
By Prad Upadrashta on Sep 6, 2021 in Machine Learning, Mathematics, Statistics
Behind OpenAI Codex: 5 Fascinating Challenges About Building Codex You Didn’t Know About
Some ML engineering and modeling challenges encountering during the construction of Codex.
By Jesus Rodriguez on Sep 3, 2021 in Codex, NLP, OpenAI
How to solve machine learning problems in the real world
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.
By Pau Labarta Bajo on Sep 2, 2021 in Advice, Business, Data Quality, Machine Learning, SQL, Tips, XGBoost
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Nine Tools I Wish I Mastered Before My PhD in Machine Learning
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