What are model governance and model operations?

O'Reilly on Data

A look at the landscape of tools for building and deploying robust, production-ready machine learning models. We are also beginning to see researchers share sample code written in popular open source libraries, and some even share pre-trained models. Model development.

Build Better and Accurate Clusters with Gaussian Mixture Models

Analytics Vidhya

Overview Gaussian Mixture Models are a powerful clustering algorithm Understand how Gaussian Mixture Models work and how to implement them in Python We’ll also. The post Build Better and Accurate Clusters with Gaussian Mixture Models appeared first on Analytics Vidhya.

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Regime Shift Models – A Fascinating Use Case of Time Series Modeling

Analytics Vidhya

The post Regime Shift Models – A Fascinating Use Case of Time Series Modeling appeared first on Analytics Vidhya. Python Time Series regime shift modeling regime shift models stock market prediction time series pythonThis article is written by Sonam Srivastava. She is one of the eminent speakers at DataHack Summit 2019, where she will be talking about.

A Comprehensive Guide to Build your own Language Model in Python!

Analytics Vidhya

Overview Language models are a crucial component in the Natural Language Processing (NLP) journey These language models power all the popular NLP applications we. The post A Comprehensive Guide to Build your own Language Model in Python!

Build your First Linear Regression Model in Qlik Sense

Analytics Vidhya

The post Build your First Linear Regression Model in Qlik Sense appeared first on Analytics Vidhya. Business Intelligence Data Science linear regression linear regression qlik Predictive modeling python qilkview Qlik Qlik Sense

Deployed your Machine Learning Model? Here’s What you Need to Know About Post-Production Monitoring

Analytics Vidhya

Overview What are the next steps after you’ve deployed your machine learning model? The post Deployed your Machine Learning Model? Data Science deployment machine learning model deployment model monitoringPost-deployment monitoring is a crucial step in any machine learning project. Here’s What you Need to Know About Post-Production Monitoring appeared first on Analytics Vidhya.

11 Important Model Evaluation Metrics for Machine Learning Everyone should know

Analytics Vidhya

Overview Evaluating a model is a core part of building an effective machine learning model There are several evaluation metrics, like confusion matrix, cross-validation, The post 11 Important Model Evaluation Metrics for Machine Learning Everyone should know appeared first on Analytics Vidhya.

Proposals for model vulnerability and security

O'Reilly on Data

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks.

Learn how to Build your own Speech-to-Text Model (using Python)

Analytics Vidhya

Overview Learn how to build your very own speech-to-text model using Python in this article The ability to weave deep learning skills with NLP. The post Learn how to Build your own Speech-to-Text Model (using Python) appeared first on Analytics Vidhya. NLP Python convert speech to text speech recognition speech recognition model Speech to text speech to text model

5 Ways Data Modeling Is Critical to Data Governance

erwin

For decades, data modeling has been the optimal way to design and deploy new relational databases with high-quality data sources and support application development. Today’s data modeling is not your father’s data modeling software. erwin Data Modeler: Where the Magic Happens.

Conceptual Modeling Requires Conceptual and Critical Thinking

TDAN

I am surprised to see a lot of people jumping straight into logical or even physical modeling and skipping conceptual modeling. Don’t they understand the value of conceptual modeling? Don’t they understand the difference between the various levels of modeling?

Types of Data Models: Conceptual, Logical & Physical

erwin

There are three different types of data models: conceptual, logical and physical, and each has a specific purpose. Conceptual Data Models: High-level, static business structures and concepts. Logical Data Models: Entity types, data attributes and relationships between entities.

Rich Model, Poor Model

Teradata

An integrated data foundation allows data science models to be more accurate, actionable and engage more customers. Find out how your model can positively impact your bottom line

Build Your First Text Classification model using PyTorch

Analytics Vidhya

The post Build Your First Text Classification model using PyTorch appeared first on Analytics Vidhya. Overview Learn how to perform text classification using PyTorch Understand the key points involved while solving text classification Learn to use Pack Padding feature.

Is Class Sensitivity Model Dependent? Analyzing 4 Popular Deep Learning Architectures

Analytics Vidhya

Overview This article dives into the key question – is class sensitivity in a classification problem model-dependent? The post Is Class Sensitivity Model Dependent?

Decoding the Black Box: An Important Introduction to Interpretable Machine Learning Models in Python

Analytics Vidhya

Overview Interpretable machine learning is a critical concept every data scientist should be aware of How can you build interpretable machine learning models?

8 Excellent Pretrained Models to get you Started with Natural Language Processing (NLP)

Analytics Vidhya

The post 8 Excellent Pretrained Models to get you Started with Natural Language Processing (NLP) appeared first on Analytics Vidhya. Deep Learning NLP Python deep learning Natural language processing pretrained models pythonIntroduction Natural Language Processing (NLP) applications have become ubiquitous these days. I seem to stumble across websites and applications regularly that are leveraging NLP.

What Is Data Modeling? Data Modeling Best Practices for Data-Driven Organizations

erwin

What is Data Modeling? Data modeling is a process that enables organizations to discover, design, visualize, standardize and deploy high-quality data assets through an intuitive, graphical interface. In the modern context, data modeling is a function of data governance.

The Ultimate Guide to Model Retraining

KDnuggets

Once you have deployed your machine learning model into production, differences in real-world data will result in model drift. This guide defines model drift and how to identify it, and includes approaches to enable model training.

On Being Model-driven: Metrics and Monitoring

Domino Data Lab

This article covers a couple of key Machine Learning (ML) vital signs to consider when tracking ML models in production to ensure model reliability, consistency and performance in the future. Machine learning models: running wild. The model produced an output.

Choosing a Machine Learning Model

KDnuggets

Selecting the perfect machine learning model is part art and part science. Learn how to review multiple models and pick the best in both competitive and real-world applications. 2019 Oct Opinions Interpretability Kaggle Machine Learning

Model Interpretability: The Conversation Continues

Domino Data Lab

Model interpretability continues to spark public discourse among industry. We have covered model interpretability previously, including a proposed definition of machine learning (ML) interpretability. While we offer a platform-as-a-service , where industry can use their choice of languages, tools, and infra to support model-driven workflows, we cover practical techniques and research in this blog to help industry make their own assessments. Model Interpretability with TCAV.

How do Transformers Work in NLP? A Guide to the Latest State-of-the-Art Models

Analytics Vidhya

Overview The Transformer model in NLP has truly changed the way we work with text data Transformer is behind the recent NLP developments, including. A Guide to the Latest State-of-the-Art Models appeared first on Analytics Vidhya.

Generative and Analytical Models for Data Analysis

Simply Statistics

Another, more informal, way that I like to think about these approaches is as the “biological” model and the “physician” model. Generative Model. This model is useful for understanding the “biological process”, i.e. the underlying mechanisms for how data analyses are created, sometimes referred to as “statistical thinking”. Analytical Model. In other words, there is no outcome on which we can “train our model” for data analysis.

Business Architecture and Process Modeling for Digital Transformation

erwin

At a fundamental level, digital transformation is about further synthesizing an organization’s operations and technology, so involving business architecture and process modeling is a best practice organizations cannot ignore. Business Architecture and Process Modeling.

Build a Machine Learning Model in your Browser using TensorFlow.js and Python

Analytics Vidhya

Overview TensorFlow.js (deeplearn.js) enables us to build machine learning and deep learning models right in our browser without needing any complex installation steps There. The post Build a Machine Learning Model in your Browser using TensorFlow.js

Business Process Modeling Use Cases and Definition

erwin

What is business process modeling (BPM)? But a theoretical understanding of business process modeling will only get you so far. The following use cases demonstrate the benefits of business process modeling in real life. Business Process Modeling Use Cases.

Build your First Multi-Label Image Classification Model in Python

Analytics Vidhya

The post Build your First Multi-Label Image Classification Model in Python appeared first on Analytics Vidhya. Introduction Are you working with image data? There are so many things we can do using computer vision algorithms: Object detection Image segmentation Image. Computer Vision Deep Learning Python deep learning Image Classification multi-label classification python

Build an Image Classification Model using Convolutional Neural Networks in PyTorch

Analytics Vidhya

Overview A hands-on tutorial to build your own convolutional neural network (CNN) in PyTorch We will be working on an image classification problem – The post Build an Image Classification Model using Convolutional Neural Networks in PyTorch appeared first on Analytics Vidhya.

How to use a Machine Learning Model to Make Predictions on Streaming Data using PySpark

Analytics Vidhya

Overview Streaming data is a thriving concept in the machine learning space Learn how to use a machine learning model (such as logistic regression). The post How to use a Machine Learning Model to Make Predictions on Streaming Data using PySpark appeared first on Analytics Vidhya.

Step-by-Step Deep Learning Tutorial to Build your own Video Classification Model

Analytics Vidhya

The post Step-by-Step Deep Learning Tutorial to Build your own Video Classification Model appeared first on Analytics Vidhya. Overview Learn how you can use computer vision and deep learning techniques to work with video data We will build our own video classification.

Automate Hyperparameter Tuning for Your Models

KDnuggets

When we create our machine learning models, a common task that falls on us is how to tune them. 2019 Sep Tutorials, Overviews Automated Machine Learning Hyperparameter Machine Learning ModelingSo that brings us to the quintessential question: Can we automate this process?

Measure Twice, Cut Once: How the Right Data Modeling Tool Drives Business Value

erwin

The need for an effective data modeling tool is more significant than ever. For decades, data modeling has provided the optimal way to design and deploy new relational databases with high-quality data sources and support application development.

Statistical Modelling vs Machine Learning

KDnuggets

At times it may seem Machine Learning can be done these days without a sound statistical background but those people are not really understanding the different nuances. Code written to make it easier does not negate the need for an in-depth understanding of the problem.

4 Proven Tricks to Improve your Deep Learning Model’s Performance

Analytics Vidhya

Overview Deep learning is a vast field but there are a few common challenges most of us face when building models Here, we talk. The post 4 Proven Tricks to Improve your Deep Learning Model’s Performance appeared first on Analytics Vidhya.

Understanding NLP and Topic Modeling Part 1

KDnuggets

In this post, we seek to understand why topic modeling is important and how it helps us as data scientists. 2019 Nov Tutorials, Overviews NLP Topic Modeling

Why it’s hard to design fair machine learning models

O'Reilly on Data

They recently wrote a survey paper, “A Critical Review of Fair Machine Learning,” where they carefully examined the standard statistical tools used to check for fairness in machine learning models. Continue reading Why it’s hard to design fair machine learning models

Developing a Web Application for a Machine Learning Model

DataFloq

This post describes developing a web application for a machine learning model and deploying it so that it can be accessed by anyone. The steps involved are as follows: Creating a simple model that can be deployed to the web, where users can input variables to get predictions.

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

KDnuggets

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. 2019 Nov Opinions Interpretability Machine Learning Modeling

Exponential Organizations Start with Internal Business Process Modeling

erwin

Strong internal business process modeling and management helps data-driven organizations compete and lead. The Value of Internal Business Process Modeling. Benefits of Internal Business Process Modeling and Management.

Can Big Data Eliminate Shortcomings of Team Extension Models?

Smart Data Collective

One of the biggest applications is that new predictive analytics models are able to get a better understanding of the relationships between employees and find areas where they break down. Big Data is the Key to Stronger Team Extension Models. Team Extension Model.

Enterprise Architecture and Business Process Modeling Tools Have Evolved

erwin

Enterprise architecture (EA) and business process (BP) modeling tools are evolving at a rapid pace. Regulatory Compliance Through Enterprise Architecture & Business Process Modeling Software. The Advantages of Enterprise Architecture & Business Process Modeling from erwin.

Predictive Model Ensembles: Pros and Cons

Perficient Data & Analytics

Many recent machine learning challenges winners are predictive model ensembles. Pros of Model Ensembles. We should choose the best model from a collection of choices. Tweaking makes models fit better. With a bagging approach, each model should be tuned to overfit.