Goto

Collaborating Authors

How to Future-Proof Your Data Science Project - KDnuggets

#artificialintelligence

Nontechnical stakeholders struggle to define business requirements. Crossfunctional teams face an uphill battle to set up robust pipelines for replicable data delivery. Machine learning models can take on a life of their own. If you've been ignoring these critical elements in the past, you may find your deployment rate skyrockets. Your data products may depend on correctly deploying the tips from this article.


Is Your Machine Learning Model Likely to Fail?

#artificialintelligence

Add in a dash of Data Science tinkering ("I think I ran my Jupyter Notebook cells out of order") -- and it's no surprise 9 out of 10 Data Science projects never see the light of day. Given that the only data product I've deployed thus far is this clustering-based Neighborhood Explorer dashboard, I'll leave it to the more experienced to walk you through the deployment process. Data Scientists should understand how to deploy and scale their own models…Overspecialization is generally a mistake. She recommends courses and reading material on Kubernetes for Data Science. This container management tool represents the dominant force in cloud deployment.


Tools for efficient MLOps (Machine Learning DevOps)

#artificialintelligence

To answer the basic question of "What is MLOps?" we need to understand first that what is DevOps. DevOps is a set of practices that combines software development and IT operations. It aims to shorten the systems development life cycle and provide continuous delivery with high software quality. DevOps is complementary with Agile software development; several DevOps aspects came from Agile methodology. DevOps is the offspring of agile software development – born from the need to keep up with the increased software velocity and throughput agile methods have achieved.


3 ways to apply agile to data science and dataops

#artificialintelligence

Just about every organization is trying to become more data-driven, hoping to leverage data visualizations, analytics, and machine learning for competitive advantages. Providing actionable insights through analytics requires a strong dataops program for integrating data and a proactive data governance program to address data quality, privacy, policies, and security. Delivering dataops, analytics, and governance is a significant scope that requires aligning stakeholders on priorities, implementing multiple technologies, and gathering people with diverse backgrounds and skills. Agile methodologies can form the working process to help multidisciplinary teams prioritize, plan, and successfully deliver incremental business value. Agile methodologies can also help data and analytics teams capture and process feedback from customers, stakeholders, and end-users.


Automated machine learning and MLOps with Azure Machine Learning Blog Microsoft Azure

#artificialintelligence

Azure Machine Learning is the center for all things machine learning on Azure, be it creating new models, deploying models, managing a model repository, or automating the entire CI/CD pipeline for machine learning. We recently made some amazing announcements on Azure Machine Learning, and in this post, I'm taking a closer look at two of the most compelling capabilities that your business should consider while choosing the machine learning platform. Before we get to the capabilities, let's get to know the basics of Azure Machine Learning. Azure Machine Learning is a managed collection of cloud services, relevant to machine learning, offered in the form of a workspace and a software development kit (SDK). Let us introduce you to Machine Learning with the help of this video where Chris Lauren from the Azure Machine Learning team showcases and demonstrates it.