Production (24)

• A $9B AI Failure, Examined - Dec 7, 2021.
  
  What happened at Zillow? An important real-world lesson in... just because you have a cool AI tool, doesn't mean that alone becomes your business model.

  AI, Business Strategy, Predictive Modeling, Production, Project Fail, Real Estate

• New Poll: What Percentage of Your Machine Learning Models Have Been Deployed? - Nov 29, 2021.
  
  Take a moment to participate in the latest KDnuggets poll and let the community know what percentage of your machine learning models have been deployed.

  Deployment, Poll, Production, Success

• AI Infinite Training & Maintaining Loop - Nov 4, 2021.
  
  Productizing AI is an infrastructure orchestration problem. In planning your solution design, you should use continuous monitoring, retraining, and feedback to ensure stability and sustainability.

  AI, Deployment, Machine Learning, Production, Training

• Serving ML Models in Production: Common Patterns - Oct 18, 2021.
  
  Over the past couple years, we've seen 4 common patterns of machine learning in production: pipeline, ensemble, business logic, and online learning. In the ML serving space, implementing these patterns typically involves a tradeoff between ease of development and production readiness. Ray Serve was built to support these patterns by being both easy to develop and production ready.

  FastAPI, Machine Learning, Production, Python, Ray

• How to Detect and Overcome Model Drift in MLOps - Aug 12, 2021.
  
  This article has a look at model drift, and how to detect and overcome it in production MLOps.

  Machine Learning, MLOps, Production

• 2021 State of Production Machine Learning Survey - Aug 11, 2021.
  
  We invite you to take the 2021 State of Production Machine Learning survey and help shed light on the latest trends in the adoption of machine learning (ML) in the industry. 

  Anyscale, Machine Learning, Production, Survey

• Data Validation in Machine Learning is Imperative, Not Optional - May 24, 2021.
  
  Before we reach model training in the pipeline, there are various components like data ingestion, data versioning, data validation, and data pre-processing that need to be executed. In this article, we will discuss data validation, why it is important, its challenges, and more.

  Data Quality, Machine Learning, Production, Validation

• Production-Ready Machine Learning NLP API with FastAPI and spaCy - Apr 21, 2021.
  
  Learn how to implement an API based on FastAPI and spaCy for Named Entity Recognition (NER), and see why the author used FastAPI to quickly build a fast and robust machine learning API.

  API, FastAPI, NLP, Production, Python, spaCy

• Continuous Training for Machine Learning – a Framework for a Successful Strategy - Apr 14, 2021.
  
  A basic appreciation by anyone who builds machine learning models is that the model is not useful without useful data. This doesn't change after a model is deployed to production. Effectively monitoring and retraining models with updated data is key to maintaining valuable ML solutions, and can be accomplished with effective approaches to production-level continuous training that is guided by the data.

  Machine Learning, MLOps, Model Performance, Production, Real-time, Training Data

• 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

• How to deploy PyTorch Lightning models to production - Nov 5, 2020.
  
  A complete guide to serving PyTorch Lightning models at scale.

  Deployment, Neural Networks, Production, Python, PyTorch, PyTorch Lightning

• 5 Best Practices for Putting Machine Learning Models Into Production - Oct 12, 2020.
  
  Our focus for this piece is to establish the best practices that make an ML project successful.

  Best Practices, Machine Learning, Production

• Here’s what you need to look for in a model server to build ML-powered services - Sep 15, 2020.
  
  More applications are being infused with machine learning while MLOps processes and best practices are becoming well established. Critical to these software and systems are the servers that run the models, which should feature key capabilities to drive successful enterprise-scale productionizing of machine learning.

  Deployment, Life Cycle, MLOps, Model Drift, Model Performance, Monitoring, Production, Scalability

• Optimize Response Time of your Machine Learning API In Production - May 1, 2020.
  
  This article demonstrates how building a smarter API serving Deep Learning models minimizes the response time.

  API, Machine Learning, Optimization, Production, Python

• Scaling the Wall Between Data Scientist and Data Engineer - Feb 17, 2020.
  
  The educational and research focuses of machine learning tends to highlight the model building, training, testing, and optimization aspects of the data science process. To bring these models into use requires a suite of engineering feats and organization, a standard for which does not yet exist. Learn more about a framework for operating a collaborative data science and engineering team to deploy machine learning models to end-users.

  Advice, Data Engineer, Data Engineering, Data Scientist, Deployment, DevOps, Machine Learning Engineer, MLflow, MLOps, Production

• The Ultimate Guide to Model Retraining - Dec 16, 2019.
  
  Once you have deployed your machine learning model into production, differences in real-world data will result in model drift. So, retraining and redeploying will likely be required. In other words, deployment should be treated as a continuous process. This guide defines model drift and how to identify it, and includes approaches to enable model training.

  Deployment, Machine Learning, Model Drift, Model Performance, Monitoring, Production, Training Data

• Overview of Different Approaches to Deploying Machine Learning Models in Production - Jun 12, 2019.
  
  Learn the different methods for putting machine learning models into production, and to determine which method is best for which use case.

  Deployment, Jupyter, Machine Learning, Production, Training Data

• How to use continual learning in your ML models, June 19 Webinar - May 29, 2019.
  
  This webinar for professional data scientists will go over how to monitor models when in production, and how to set up automatically adaptive machine learning.

  cnvrg.io, Kubernetes, Machine Learning, Production, TensorFlow

• Project Hydrogen, new initiative based on Apache Spark to support AI and Data Science - Aug 16, 2018.
  
  An introduction to Project Hydrogen: how it can assist machine learning and AI frameworks on Apache Spark and what distinguishes it from other open source projects.

  AI, Apache Spark, Data Science, Databricks, Distributed Computing, Production

• Data Science: 4 Reasons Why Most Are Failing to Deliver - May 24, 2018.
  
  Data Science: Some see billions in returns, but most are failing to deliver. This article explores some of the reasons why this is the case.

  Data Science, Deployment, Domino, Failure, Production

• What should be focus areas for Machine Learning / AI in 2018? - Apr 27, 2018.
  
  This article looks at what are the recent trends in data science/ML/AI and suggests subareas DS groups need to focus on.

  2018 Predictions, AI, Machine Learning, Production

• How to Build a Data Science Pipeline - Jul 14, 2017.
  
  Start with y. Concentrate on formalizing the predictive problem, building the workflow, and turning it into production rather than optimizing your predictive model. Once the former is done, the latter is easy.

  Data Science, Pipeline, Production

• Your Checklist to Get Data Science Implemented in Production - Jun 7, 2017.
  
  For over a year we surveyed thousands of companies from all types of industries and data science advancement on how they managed to overcome these difficulties and analyzed the results. Here are the key things to keep in mind when you're working on your design-to-production pipeline.

  Checklist, Data Science, Dataiku, Production

• Questions To Ask When Moving Machine Learning From Practice to Production - Nov 18, 2016.
  
  An overview of applying machine learning techniques to solve problems in production. This articles covers some of the varied questions to ponder when incorporating machine learning into teams and processes.

  Data Science, Deep Learning, Deployment, Machine Learning, Production