- What Is AI Model Governance? - Dec 13, 2021.
How exactly does AI model governance help tackle these issues? And how can you ensure you’re using it to best fit your needs? Read on.
AI, Data Governance, Modeling
- Dealing with Data Leakage - Oct 8, 2021.
Target leakage and data leakage represent challenging problems in machine learning. Be prepared to recognize and avoid these potentially messy problems.
Cross-validation, Data Science, Datasets, Machine Learning, Modeling, Training Data
How to Find Weaknesses in your Machine Learning Models - Sep 20, 2021.
FreaAI: a new method from researchers at IBM.
Interpretability, Machine Learning, Modeling, Statistics
- How to Select an Initial Model for your Data Science Problem - Aug 20, 2021.
Save yourself some time and headaches and start simple.
Data Science, Linear Regression, Logistic Regression, Modeling
- DeepMind’s New Super Model: Perceiver IO is a Transformer that can Handle Any Dataset - Aug 11, 2021.
The new transformer-based architecture can process audio, video and images using a single model.
DeepMind, Modeling, Transformer
- 10 Machine Learning Model Training Mistakes - Jul 30, 2021.
These common ML model training mistakes are easy to overlook but costly to redeem.
Machine Learning, Modeling, Training
- Exploring the SwAV Method - Jul 9, 2021.
This post discusses the SwAV (Swapping Assignments between multiple Views of the same image) method from the paper “Unsupervised Learning of Visual Features by Contrasting Cluster Assignments” by M. Caron et al.
Feature Extraction, Image Classification, Modeling, Training
- MLOps is an Engineering Discipline: A Beginner’s Overview - Jul 8, 2021.
MLOps = ML + DEV + OPS. MLOps is the idea of combining the long-established practice of DevOps with the emerging field of Machine Learning.
Data Engineering, Deployment, Machine Learning, MLOps, Modeling
- Write and train your own custom machine learning models using PyCaret - May 25, 2021.
A step-by-step, beginner-friendly tutorial on how to write and train custom machine learning models in PyCaret.
Machine Learning, Modeling, PyCaret, Python, Training
How to Determine if Your Machine Learning Model is Overtrained - May 20, 2021.
WeightWatcher is based on theoretical research (done injoint with UC Berkeley) into Why Deep Learning Works, based on our Theory of Heavy Tailed Self-Regularization (HT-SR). It uses ideas from Random Matrix Theory (RMT), Statistical Mechanics, and Strongly Correlated Systems.
Learning, Modeling, Python, Training
- Learning from machine learning mistakes - Mar 19, 2021.
Read this article and discover how to find weak spots of a regression model.
Machine Learning, Mistakes, Modeling, Regression
- Evaluating Object Detection Models Using Mean Average Precision - Mar 3, 2021.
In this article we will see see how precision and recall are used to calculate the Mean Average Precision (mAP).
Computer Vision, Metrics, Modeling, Object Detection
- My machine learning model does not learn. What should I do? - Feb 10, 2021.
This article presents 7 hints on how to get out of the quicksand.
Algorithms, Business Context, Data Quality, Hyperparameter, Machine Learning, Modeling, Tips
- Backcasting: Building an Accurate Forecasting Model for Your Business - Feb 5, 2021.
This article will shed some light on processes happening under the roof of ML-based solutions on the example of the business case where the future success directly depends on the ability to predict unknown values from the past.
Business, Forecasting, Modeling
- Vision Transformers: Natural Language Processing (NLP) Increases Efficiency and Model Generality - Feb 2, 2021.
Why do we hear so little about transformer models applied to computer vision tasks? What about attention in computer vision networks?
Attention, Efficiency, Modeling, NLP, Transformer
- MLOps: Model Monitoring 101 - Jan 6, 2021.
Model monitoring using a model metric stack is essential to put a feedback loop from a deployed ML model back to the model building stage so that ML models can constantly improve themselves under different scenarios.
AI, Data Science, DevOps, Machine Learning, MLOps, Modeling
- Model Experiments, Tracking and Registration using MLflow on Databricks - Jan 5, 2021.
This post covers how StreamSets can help expedite operations at some of the most crucial stages of Machine Learning Lifecycle and MLOps, and demonstrates integration with Databricks and MLflow.
Data Science, Databricks, DataOps, Experimentation, MLflow, MLOps, Modeling, StreamSets
- Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance - Dec 21, 2020.
A practical deep dive on production monitoring architectures for machine learning at scale using real-time metrics, outlier detectors, drift detectors, metrics servers and explainers.
AI, Deployment, Explainable AI, Machine Learning, Modeling, Outliers, Production, Python
- Undersampling Will Change the Base Rates of Your Model’s Predictions - Dec 17, 2020.
In classification problems, the proportion of cases in each class largely determines the base rate of the predictions produced by the model. Therefore if you use sampling techniques that change this proportion, there is a good chance you will want to rescale / calibrate your predictions before using them in the wild.
Classification, Modeling, Predictions, R, Sampling
- Pruning Machine Learning Models in TensorFlow - Dec 4, 2020.
Read this overview to learn how to make your models smaller via pruning.
Machine Learning, Modeling, Python, TensorFlow
- Deploying Trained Models to Production with TensorFlow Serving - Nov 30, 2020.
TensorFlow provides a way to move a trained model to a production environment for deployment with minimal effort. In this article, we’ll use a pre-trained model, save it, and serve it using TensorFlow Serving.
Deployment, Modeling, Neural Networks, Python, TensorFlow
- Simple Python Package for Comparing, Plotting & Evaluating Regression Models - Nov 25, 2020.
This package is aimed to help users plot the evaluation metric graph with single line code for different widely used regression model metrics comparing them at a glance. With this utility package, it also significantly lowers the barrier for the practitioners to evaluate the different machine learning algorithms in an amateur fashion by applying it to their everyday predictive regression problems.
Data Visualization, Metrics, Modeling, Python, Regression
- Building Deep Learning Projects with fastai — From Model Training to Deployment - Nov 4, 2020.
A getting started guide to develop computer vision application with fastai.
Deep Learning, Deployment, fast.ai, Modeling, Python, Training
- Machine Learning Model Deployment - Sep 30, 2020.
Read this article on machine learning model deployment using serverless deployment. Serverless compute abstracts away provisioning, managing severs and configuring software, simplifying model deployment.
Cloud, Deployment, Machine Learning, Modeling, Workflow
- The Insiders’ Guide to Generative and Discriminative Machine Learning Models - Sep 18, 2020.
In this article, we will look at the difference between generative and discriminative models, how they contrast, and one another.
Deep Learning, GANs, Generative Adversarial Network, Modeling
- KDnuggets™ News 20:n34, Sep 9: Top Online Data Science Masters Degrees; Modern Data Science Skills: 8 Categories, Core Skills, and Hot Skills - Sep 9, 2020.
Also: Creating Powerful Animated Visualizations in Tableau; PyCaret 2.1 is here: What's new?; How To Decide What Data Skills To Learn; How to Evaluate the Performance of Your Machine Learning Model
Data Science, Data Science Skills, Data Visualization, Machine Learning, Master of Science, Modeling, Online Education, PyCaret, Tableau
The NLP Model Forge: Generate Model Code On Demand - Aug 24, 2020.
You've seen their Big Bad NLP Database and The Super Duper NLP Repo. Now Quantum Stat is back with its most ambitious NLP product yet: The NLP Model Forge.
Google Colab, Modeling, NLP, Text Analytics
- Facebook Uses Bayesian Optimization to Conduct Better Experiments in Machine Learning Models - Aug 10, 2020.
A research from Facebook proposes a Beyasian optimization method to run A/B tests in machine learning models.
Bayesian, Facebook, Machine Learning, Modeling, Optimization
Wrapping Machine Learning Techniques Within AI-JACK Library in R - Jul 17, 2020.
The article shows an approach to solving problem of selecting best technique in machine learning. This can be done in R using just one library called AI-JACK and the article shows how to use this tool.
Automated Machine Learning, AutoML, Machine Learning, Modeling, R
- Stop training more models, start deploying them - Jun 30, 2020.
We are hardly living up to the promises of AI in healthcare. It’s not because of our training, it’s because of our deployment.
Deployment, Modeling, Training
- How to make AI/Machine Learning models resilient during COVID-19 crisis - Jun 11, 2020.
COVID-19-driven concept shift has created concern over the usage of AI/ML to continue to drive business value following cases of inaccurate outputs and misleading results from a variety of fields. Data Science teams must invest effort in post-model tracking and management as well as deploy an agility in the AI/ML process to curb problems related to concept shift.
AI, Coronavirus, COVID-19, Machine Learning, Model Drift, Modeling
Build and deploy your first machine learning web app - May 22, 2020.
A beginner’s guide to train and deploy machine learning pipelines in Python using PyCaret.
App, Flask, Heroku, Machine Learning, Modeling, Open Source, Pipeline, PyCaret, Python
- Hyperparameter Optimization for Machine Learning Models - May 7, 2020.
Check out this comprehensive guide to model optimization techniques.
Hyperparameter, Machine Learning, Modeling, Optimization, Python
- Announcing PyCaret 1.0.0 - Apr 21, 2020.
An open source low-code machine learning library in Python. PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few words only. This makes experiments exponentially fast and efficient.
Machine Learning, Modeling, Open Source, PyCaret, Python
- The Double Descent Hypothesis: How Bigger Models and More Data Can Hurt Performance - Apr 20, 2020.
OpenAI research shows a phenomenon that challenges both traditional statistical learning theory and conventional wisdom in machine learning practitioners.
Deep Learning, Modeling, OpenAI
- ModelDB 2.0 is here! - Mar 19, 2020.
We are excited to announce that ModelDB 2.0 is now available! We have learned a lot since building ModelDB 1.0, so we decided to rebuild from the ground up.
MLOps, ModelDB, Modeling, Version Control
- Decision Boundary for a Series of Machine Learning Models - Mar 13, 2020.
I train a series of Machine Learning models using the iris dataset, construct synthetic data from the extreme points within the data and test a number of Machine Learning models in order to draw the decision boundaries from which the models make predictions in a 2D space, which is useful for illustrative purposes and understanding on how different Machine Learning models make predictions.
Decision Boundaries, Machine Learning, Modeling, R
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead - Nov 20, 2019.
The two main takeaways from this paper: firstly, a sharpening of my understanding of the difference between explainability and interpretability, and why the former may be problematic; and secondly some great pointers to techniques for creating truly interpretable models.
Interpretability, Machine Learning, Modeling
- Automate Hyperparameter Tuning for Your Models - Sep 20, 2019.
When we create our machine learning models, a common task that falls on us is how to tune them. So that brings us to the quintessential question: Can we automate this process?
Automated Machine Learning, Hyperparameter, Machine Learning, Modeling
- Version Control for Data Science: Tracking Machine Learning Models and Datasets - Sep 13, 2019.
I am a Git god, why do I need another version control system for Machine Learning Projects?
Data Science, Datasets, Machine Learning, Modeling, Version Control
- Introducing AI Explainability 360: A New Toolkit to Help You Understand what Machine Learning Models are Doing - Aug 27, 2019.
Recently, AI researchers from IBM open sourced AI Explainability 360, a new toolkit of state-of-the-art algorithms that support the interpretability and explainability of machine learning models.
AI, Explainability, Machine Learning, Modeling
- 7 Tips for Dealing With Small Data - Jul 29, 2019.
At my workplace, we produce a lot of functional prototypes for our clients. Because of this, I often need to make Small Data go a long way. In this article, I’ll share 7 tips to improve your results when prototyping with small datasets.
Cross-validation, Data Models, Ensemble Methods, Modeling, Tips, Transfer Learning
- From Data Pre-processing to Optimizing a Regression Model Performance - Jul 19, 2019.
All you need to know about data pre-processing, and how to build and optimize a regression model using Backward Elimination method in Python.
Model Performance, Modeling, Optimization, Regression
The Machine Learning Puzzle, Explained - Jun 17, 2019.
Lots of moving parts go into creating a machine learning model. Let's take a look at some of these core concepts and see how the machine learning puzzle comes together.
Algorithms, Explained, Machine Learning, Modeling
- All Models Are Wrong – What Does It Mean? - Jun 12, 2019.
During your adventures in data science, you may have heard “all models are wrong.” Let’s unpack this famous quote to understand how we can still make models that are useful.
Advice, Linear Regression, Modeling, Statistics
7 Steps to Mastering Intermediate Machine Learning with Python — 2019 Edition - Jun 3, 2019.
This is the second part of this new learning path series for mastering machine learning with Python. Check out these 7 steps to help master intermediate machine learning with Python!
7 Steps, Classification, Cross-validation, Dimensionality Reduction, Feature Engineering, Feature Selection, Image Classification, K-nearest neighbors, Machine Learning, Modeling, Naive Bayes, numpy, Pandas, PCA, Python, scikit-learn, Transfer Learning
- Choosing Between Model Candidates - May 29, 2019.
Models are useful because they allow us to generalize from one situation to another. When we use a model, we’re working under the assumption that there is some underlying pattern we want to measure, but it has some error on top of it.
Data Science, Modeling, Regression, Time Series
- Careful! Looking at your model results too much can cause information leakage - May 24, 2019.
We all are aware of the issue of overfitting, which is essentially where the model you build replicates the training data results so perfectly its fitted to the training data and does not generalise to better represent the population the data comes to, with catastrophic results when you feed in new data and get very odd results.
Cross-validation, Modeling, Overfitting, Validation
- Modeling Price with Regularized Linear Model & XGBoost - May 2, 2019.
We are going to implement regularization techniques for linear regression of house pricing data. Our goal in price modeling is to model the pattern and ignore the noise.
Modeling, Python, Regularization, XGBoost
- Distributed Artificial Intelligence: A primer on Multi-Agent Systems, Agent-Based Modeling, and Swarm Intelligence - Apr 18, 2019.
Distributed Artificial Intelligence (DAI) is a class of technologies and methods that span from swarm intelligence to multi-agent technologies. It is one of the subsets of AI where simulation has greater importance that point-prediction.
AI, Distributed Systems, Modeling, Swarm Intelligence
- Checklist for Debugging Neural Networks - Mar 22, 2019.
Check out these tangible steps you can take to identify and fix issues with training, generalization, and optimization for machine learning models.
Checklist, Modeling, Neural Networks, Optimization, Tips, Training
- BERT: State of the Art NLP Model, Explained - Dec 26, 2018.
BERT’s key technical innovation is applying the bidirectional training of Transformer, a popular attention model, to language modelling. It has caused a stir in the Machine Learning community by presenting state-of-the-art results in a wide variety of NLP tasks.
Explained, Modeling, Neural Networks, NLP, Transformer
- Multi-Class Text Classification Model Comparison and Selection - Nov 1, 2018.
This is what we are going to do today: use everything that we have presented about text classification in the previous articles (and more) and comparing between the text classification models we trained in order to choose the most accurate one for our problem.
Pages: 1 2
Modeling, NLP, Python, Text Classification
- Machine Learning: How to Build a Model From Scratch - Sep 20, 2018.
Register now for upcoming webinar, Building a Machine Learning Fraud Model with Momentum Travel, on Sep 27 @ 10 AM PT.
Machine Learning, Modeling, WhitePages
- How to Make Your Machine Learning Models Robust to Outliers - Aug 28, 2018.
In this blog, we’ll try to understand the different interpretations of this “distant” notion. We will also look into the outlier detection and treatment techniques while seeing their impact on different types of machine learning models.
Machine Learning, Modeling, Outliers
- Leveraging Agent-based Models (ABM) and Digital Twins to Prevent Injuries - Aug 22, 2018.
Both athletes and machines deal with inter-twined complex systems (where the interactions of one complex system can have a ripple effect on others) that can have significant impact on their operational effectiveness.
Health, IoT, Modeling, Sports
- Building Reliable Machine Learning Models with Cross-validation - Aug 9, 2018.
Cross-validation is frequently used to train, measure and finally select a machine learning model for a given dataset because it helps assess how the results of a model will generalize to an independent data set in practice.
Comet.ml, Cross-validation, Machine Learning, Modeling, scikit-learn
- Modelling Time Series Processes using GARCH - May 25, 2018.
To go into the turbulent seas of volatile data and analyze it in a time changing setting, ARCH models were developed.
Pages: 1 2
Modeling, R, Time Series
A Framework for Approaching Textual Data Science Tasks - Nov 22, 2017.
Although NLP and text mining are not the same thing, they are closely related, deal with the same raw data type, and have some crossover in their uses. Let's discuss the steps in approaching these types of tasks.
Modeling, Natural Language Processing, NLP, Text Analytics, Text Mining
Interpreting Machine Learning Models: An Overview - Nov 7, 2017.
This post summarizes the contents of a recent O'Reilly article outlining a number of methods for interpreting machine learning models, beyond the usual go-to measures.
Interpretability, Machine Learning, Modeling, O'Reilly
- Train your Deep Learning Faster: FreezeOut - Aug 3, 2017.
We explain another novel method for much faster training of Deep Learning models by freezing the intermediate layers, and show that it has little or no effect on accuracy.
Deep Learning, Machine Learning, Model Performance, Modeling, Neural Networks
- Models: From the Lab to the Factory - Apr 27, 2017.
In this post, we’ll go over techniques to avoid these scenarios through the process of model management and deployment.
Data Science, Modeling, SVDS
- Must-Know: Why it may be better to have fewer predictors in Machine Learning models? - Apr 4, 2017.
There are a few reasons why it might be a better idea to have fewer predictor variables rather than having many of them. Read on to find out more.
Feature Selection, Interview Questions, Machine Learning, Modeling
What is Structural Equation Modeling? - Mar 27, 2017.
Structural Equation Modeling (SEM) is an extremely broad and flexible framework for data analysis, perhaps better thought of as a family of related methods rather than as a single technique. What is its relevance to Marketing Research?
Data Analysis, Market Research, Modeling, Psychology
- Smart Data Platform – The Future of Big Data Technology - Dec 2, 2016.
Data processing and analytical modelling are major bottlenecks in today’s big data world, due to need of human intelligence to decide relationships between data, required data engineering tasks, analytical models and it’s parameters. This article talks about Smart Data Platform to help to solve such problems.
Big Data, Big Data Analytics, China, Data Processing, Modeling, TalkingData
- Approaching (Almost) Any Machine Learning Problem - Aug 18, 2016.
If you're looking for an overview of how to approach (almost) any machine learning problem, this is a good place to start. Read on as a Kaggle competition veteran shares his pipelines and approach to problem-solving.
Pages: 1 2
Advice, Feature Selection, Kaggle, Machine Learning, Modeling
- New Standard Methodology for Analytical Models - Aug 3, 2015.
Traditional methods for the analytical modelling like CRISP-DM have several shortcomings. Here we describe these friction points in CRISP-DM and introduce a new approach of Standard Methodology for Analytics Models which overcomes them.
Pages: 1 2 3
CRISP-DM, Data Mining, Modeling, Olav Laudy, ROI
- Automatic Statistician and the Profoundly Desired Automation for Data Science - Feb 17, 2015.
The Automatic Statistician project by Univ. of Cambridge and MIT is pushing ahead the frontiers of automation for the selection and evaluation of machine learning models. In general, what does automation mean to Data Science?
Automation, Cambridge, Data Cleaning, Data Science, Machine Learning, MIT, Modeling, Statistician
