- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- How to break a model in 20 days — a tutorial on production model analytics - Mar 29, 2021.
This is an article on how models fail in production, and how to spot it.
- Why Do Machine Learning Projects Fail? - Feb 24, 2021.
At the beginning of any data science project, many challenges could arise that lead to its eventual collapse. Making sure you look ahead -- early in the planning -- toward putting your resulting model into production can help increase the chance of delivering long-term value with your developed machine learning system.
- 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.
- Is Your Machine Learning Model Likely to Fail? - Nov 27, 2020.
Read about these 5 missteps to avoid in your planning process.
- How to Future-Proof Your Data Science Project - Nov 18, 2020.
This article outlines 5 critical elements of ML model selection & deployment.
- How to deploy PyTorch Lightning models to production - Nov 5, 2020.
A complete guide to serving PyTorch Lightning models at scale.
- 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.
- 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.
- 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.
- 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.
- 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.
- Upcoming Webinar, Machine Learning Vital Signs: Metrics and Monitoring Models in Production - Oct 11, 2019.
In this upcoming webinar on Oct 23 @ 10 AM PT, learn why you should invest time in monitoring your machine learning models, the dangers of not paying attention to how a model’s performance can change over time, metrics you should be gathering for each model and what they tell you, and much more.
- 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.
- 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.
- The ultimate guide to starting AI - Nov 13, 2018.
A step-by-step overview of how to begin your project, including advice on how to craft a wise performance metric, setting up testing criteria to overcome human bias, and more.
- Accelerating Your Algorithms in Production [Webinar Replay] - Oct 16, 2018.
Numerical algorithms are computationally demanding, which makes performance an important consideration when using Python for machine learning, especially as you move from desktop to production.
- 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.
- 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.
- 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.
- Analytic Creation to Production: Bridging The Chasm, Webinar, Dec 7 - Dec 1, 2017.
Understand best practices for optimizing the handoff from analytic team to IT across your business as a core competency, how to create scalable peak model performance, and more.
- KDnuggets™ News 17:n37, Sep 27: Essential Data Science & Machine Learning Cheat Sheets; 5 Machine Learning Projects to Check Out Now! - Sep 27, 2017.
30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets; 5 Machine Learning Projects You Can No Longer Overlook - Episode VI; Putting Machine Learning in Production; 5 Ways to Get Started with Reinforcement Learning; Ensemble Learning to Improve Machine Learning Results
- Putting Machine Learning in Production - Sep 22, 2017.
In machine learning, going from research to production environment requires a well designed architecture. This blog shows how to transfer a trained model to a prediction server.
- Accelerating Your Algorithms in Production with Python and Intel MKL, Sep 21 - Sep 8, 2017.
We will provide tips for data scientists to speed up Python algorithms, including a discussion on algorithm choice, and how effective package tool can make large differences in performance.
- Get Out of the Sandbox – Put Your Models in Production, Aug 10 Webinar - Aug 2, 2017.
Learn how to deploy your Data Science work in production, both in batch and real-time environments, where people and programs can use them simply and confidently.
- What is hardcore data science – in practice? - Aug 1, 2017.
Data Science expert Mikio Braun on the anatomy of an architecture to bring data science into production. Learn more at his talk at Strata NYC - Use code KDNU for additional 20% off (best price ends Aug 11).
- 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.
- KDnuggets™ News 17:n23, Jun 14: The Practice of Machine Learning, Data Science Implementation, and Feature Selection - Jun 14, 2017.
A Practical Guide to Machine Learning; Your Checklist to Get Data Science Implemented in Production; The Practical Importance of Feature Selection; Machine Learning in Real Life: Tales from the Trenches.
- A Practical Guide to Machine Learning: Understand, Differentiate, and Apply - Jun 9, 2017.
So, if Machine Learning was first defined in 1959, why is this now the time to seize the opportunity? It’s the economics.
- Machine Learning in Real Life: Tales from the Trenches to the Cloud – Part 1 - Jun 8, 2017.
We live in a world where everyone knows enough about the Buzzwords “Deep Learning” and “Big Data”... we also live in a world where if you’re a developer you can, while knowing nothing about machine learning, go from zero to training a OCR model in the space of an hour.
- 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.
- 3 Ways to Move Your Data Science Into Production, May 24 - May 22, 2017.
In this live webinar, on May 24th at 11AM Central, learn how Anaconda empowers data scientists to encapsulate and deploy their data science projects as live applications with a single click.
- 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.
- Survey: Why Companies Still Fail to Get Full Value From Big Data - Apr 29, 2016.
Any company that has decided to put efforts in data has to face bringing these projects from the design and development phase to the production phase at some point. So tell us how you do it. And we’ll tell you what we learned from you.
- Portable Format for Analytics: moving models to production - Jan 5, 2016.
There are many ways to compute the best solution to a problem, but not all of them can be put into production. The Portable Format for Analytics (PFA) provides a way of formalizing and moving models.