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Fast, Visual, and Explainable ML Modeling With PerceptiLabs
Pure-code ML frameworks like TensorFlow, have become popular for building ML models because they effectively offer a high-level grammar for describing model topologies and algorithms. This is a powerful approach, but it has limitations for providing insight and explainability of models. These issues are further magnified... Read more
Simplifying MLOps with Model Registry
Your iPhone tells you exactly what app you’d like to access each morning, Netflix shows you movie previews that are tailored precisely for you, and Grammarly fixes your writing so you sound like your best self! Each of these applications is the product of a team... Read more
Making Explainability Work in Practice
Complex ‘black box’ models are becoming more and more prevalent in industries involving high-stakes decisions (such as finance, healthcare, insurance). As machine learning algorithms take a prominent role in our daily lives, explaining their decision will only grow in importance via explainability. By now there is... Read more
Your Guide to Linear Regression Models
Interpretability is one of the biggest challenges in machine learning. A model has more interpretability than another one if its decisions are easier for a human to comprehend. Some models are so complex and are internally structured in such a way that it’s almost impossible to... Read more
Why Causal Machine Learning is the Next Revolution in AI
Editor’s note: Robert Ness is a speaker for ODSC East 2021. Check out his talk, “Causal Machine Learning Blitz,” there! Causal modeling and inference are perhaps at the core of the most interesting questions in data science. A common task for a data scientist at a... Read more
Black Box Optimization Using Latent Action Monte Carlo Tree Search (LaMCTS)
Black box optimization has numerous applications in industries. From a/b testing to experimental designs of new ads or UI, hyper-parameter tuning in the machine learning models, or to find the optimal configuration of a system, black-box optimization tries to optimize your decision solely by exploring the... Read more
Build NLP and Conversational AI Apps with Transformers and Large Scale Pre-Trained Language Models
Transformers have taken the AI research and product community by storm. We have seen them advancing multiple fields in AI such as natural language processing (NLP), computer vision, and robotics. In this blog, I will share some background in conversational AI, NLP, and transformers-based large-scale language... Read more
Data Science’s Role in Anomaly Detection
Anomalies. Oxford dictionary defines them as things that deviate from what is normal or expected. No matter what field you are in, they seem to pop up and occur without warning. In the realm of data, anomalies can lead to incorrect or out-of-date decisions to be... Read more
Dissecting the Software Designing Approach of “Design by Contract”
Good software is built from a good design. When we say clean code, it may be indicated that we are talking about good practices that relate only to the implementation details of the software, instead of its design. However, this assumption would be wrong since the code is not something different from the... Read more
How to Plan an Optimal Tour Using Network Optimization
A good public transportation system is crucial to develop smart cities, particularly in great metropolitan areas. It is essential in maintaining the flow of its residents’ daily commutes and allows tourists to easily move around the city. Public transportation agencies around the globe openly share their... Read more