- Building a Machine Learning Model through Trial and Error - Sep 24, 2018.
A step-by-step guide that includes suggestions on how to preprocess data and deriving features from this. This article also contains links to help you explore additional resources about machine learning methods and other examples.
Tags: Deployment, Machine Learning, MathWorks
- [Live Webinar] MLOps: Machine Learning Operationalization, Sep 27 - Sep 19, 2018.
Successfully pushing ML to production requires a shift in your DevOps practices to become MLOps, machine learning operationalization. Learn how to do it in this Sep 27 webinar.
Tags: ActiveState, Deployment, DevOps, Machine Learning
- See NVIDIA Deep Learning In Action [Webinar Series] - Sep 13, 2018.
Hear how three companies benefitted from the performance, simplicity and convenience of NVIDIA DGX Station to supercharge their deep learning development, infusing their products and services with the power of AI.
Tags: AI, Deep Learning, Deployment, NVIDIA
- One-Click Machine Learning Deployments with Anaconda Enterprise - Aug 20, 2018.
With Anaconda Enterprise, your organization can develop, govern, and automate machine learning pipelines, while scaling with ease.
Tags: Anaconda, Deployment, Machine Learning, Scalability
- Production ML for Data Scientists: What You Can Do and How to Make It Easy, August 22 Webinar - Aug 8, 2018.
Learn about MLOps –machine learning operationalization that breaks down the silos between data science and IT; Streamlines deployment and orchestration, and adds advanced functionality.
Tags: Deployment, Machine Learning, ParallelM
- 9 Reasons why your machine learning project will fail - Jul 25, 2018.
This article explains in detail some of the issues that you may face during your machine learning project.
Tags: Deployment, Failure, Machine Learning, Project Fail
- 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.
Tags: Data Science, Deployment, Domino, Failure, Production
- [eBook] Solving 4 Big Problems in Data Science - Mar 6, 2018.
Insights and tools from leading data science teams to accelerate results.
Tags: Apache Spark, Big Data, Cloud Computing, Databricks, Deployment, ebook
- 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.
Tags: Analytics, Deployment, Open Data Group, Production, Scalability
- 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.
Tags: Data Science, Dataiku, Deployment, Production
- How to Turn your Data Science Projects into a Success - Jul 14, 2017.
This interview with Dr. Olav Laudy, Chief Data Scientist for IBM Analytics, is a summary of a recent conference where he participated in a panel on the Big Data and Analytics
Tags: Data Science, Deployment, IBM, Project Fail
- Deploying Data Science Projects [Whitepaper] - Jul 6, 2017.
In a new whitepaper from Team Anaconda, Productionizing and Deploying Data Science Projects, our data science experts share the factors to consider when deploying data science projects, how to leverage Anaconda Project to encapsulate your data science projects, and more.
Tags: Anaconda, Continuum Analytics, Data Science, Deployment
- An ode to the analytics grease monkeys - Feb 2, 2017.
Analytics is not one time job. It needs to be automated, deployed and improved for future business analytics requirements. Here an IBM expert discusses about development & deployment of analytics assets and capabilities of it.
Tags: Analytics, Analytics Leader, CRISP-DM, Deployment, IBM, IBM DSX, ROI
- Deploying Production-grade Data Products – Special Report - Jan 24, 2017.
Dataiku launched a survey a few months back to find out how companies handled going from designing to deploying a data product. Read the report and learn four ways that companies approach production.
Tags: Data Science, Dataiku, Deployment, White Paper
Continuous improvement for IoT through AI / Continuous learning - Nov 25, 2016.
In reality, especially for IoT, it is not like once an analytics model is built, it will give the results with same accuracy till the end of time. Data pattern changes over the time which makes it absolutely important to learn from new data and improve/recalibrate the models to get correct result. Below article explain this phenomenon of continuous improvement in analytics for IoT.
Tags: AI, Deployment, IoT, Machine Learning, Model Performance, Realtime Analytics
- 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.
Tags: Data Science, Deep Learning, Deployment, Machine Learning, Production
- Deep Learning meets Deep Deployment - Oct 17, 2016.
We now have a deep learning model that is able to deliver valuable results, but how can we apply it easily to new data where and when we need to?
Tags: Deep Learning, Deployment, Zementis
- Rexer Analytics Data Science Survey Highlights - Oct 14, 2016.
Regression, Decision Trees, and Cluster analysis remain the most commonly used algorithms in the field, R continues to ascend, job satisfaction remains high, but customer understanding still needs improvement.
Tags: Algorithms, Customer Analytics, Data Science, Deployment, R, Rexer Analytics, Survey
- Streamlining Analytic Deployment: Inside the FICO Decision Management Suite 2.0 - Jul 8, 2016.
This post explains what’s new in the 2.0 version of the FICO Decision Management Suite, and how it can be used by data scientists and others to create stronger customer relationships and provide strategic competitive advantage.
Tags: Decision Management, Decision Support, Deployment, FICO
- Standards-based Deployment of Predictive Analytics - Jun 21, 2016.
Using a standards-based approach to deploy predictive analytics on operational systems from mainframes to Hadoop.
Tags: Deployment, James Taylor, Predictive Analytics, Standards, White Paper, Zementis
- Building Data Systems: What Do You Need? - Jun 3, 2016.
This post shares some insight gained through years of building data-powered products, and discusses the capabilities you need to have in place in order to successfully build and maintain data systems and data infrastructure.
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Tags: Big Data, Data Engineering, Deployment, Quality Control
- Predictive Analytics Deployment to Mainframe or Hadoop – Webinar, March 3 - Feb 16, 2016.
Join Decision Management guru James Taylor and Michael Zeller, CEO of Zementis, to learn how the Predictive Model Markup Language (PMML) provides a standards-based, repeatable and efficient deployment approach.
Tags: Deployment, James Taylor, PMML, Standards, Zementis
- Predict. Share. Deploy. With Open Data Science – Jan 20 Webinar - Jan 12, 2016.
Building, tuning, sharing, deploying, and scaling predictive models is challenging, and rarely covered in statistics class. Learn how to make data science work in the real world in this webinar.
Tags: Anaconda, Continuum Analytics, Deployment, Python, R
- Webinar, Feb 18: Fine-tune your analytics strategy for 2014 - Feb 4, 2014.
Join analytics experts from Johnson & Johnson, InterContinental Hotels, and HP on Feb 18 to fine-tune your analytics strategy.
Tags: Analytics Strategy, Data-Driven Business, Deployment, Integration, Johnson & Johnson
