Tag: Deployment (67)

• Unpopular Opinion – Data Scientists Should Be More End-to-End - Sep 17, 2020.
  
  Can a do-it-all Data Scientist really be more effective at delivering new value from data? While it might sound exhausting, important efficiencies can exist that might bring better value to the business even faster.

  Tags: Career Advice, Data Science Process, Data Scientist, Deployment

• 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.

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

• Rapid Python Model Deployment with FICO Xpress Insight - Aug 20, 2020.
  
  The biggest hurdle in the use of data to create business value, is indeed the ability to operationalize analytics throughout the organization. Xpress Insight is geared to reduce the burden on IT and address their critical requirements while empowering business users to take ownership of decisions and change management.

  Tags: AI, Deployment, FICO, Machine Learning, Optimization, Python

• Why would you put Scikit-learn in the browser? - Jul 22, 2020.
  
  Honestly? I don’t know. But I do think WebAssembly is a good target for ML/AI deployment (in the browser and beyond).

  Tags: Deployment, Development, scikit-learn, Virtualization

• Stop training more models, start deploying them - Jun 30, 2020.
  
  We are hardly living up to the promises of AI in healthcare. It’s not because of our training, it’s because of our deployment.

  Tags: Deployment, Modeling, Training

• Demystifying the AI Infrastructure Stack - May 1, 2020.
  
  AI tools and services are expanding at a rapid clip, and keeping a handle on this evolving ecosystem is crucial for the success of your AI projects. This framework will help you build your technical stack to deploy AI projects faster and at scale.

  Tags: AI, Automation, Deployment, DevOps, Infrastructure, MLOps

• 4 Steps to ensure your AI/Machine Learning system survives COVID-19 - Apr 17, 2020.
  
  Many AI models rely on historical data to make predictions on future behavior. So, what happens when consumer behavior across the planet makes a 180 degree flip? Companies are quickly seeing less value from some AI systems as training data is no longer relevant when user behaviors and preferences change so drastically. Those who are flexible can make it through this crisis in data, and these four techniques will help you stay in front of the competition.

  Tags: AI, Coronavirus, COVID-19, Deployment, Machine Learning

• Peer Reviewing Data Science Projects - Apr 13, 2020.
  
  In any technical development field, having other practitioners review your work before shipping code off to production is a valuable support tool to make sure your work is error-proof. Even through your preparation for the review, improvements might be discovered and then other issues that escaped your awareness can be spotted by outsiders. This peer scrutiny can also be applied to Data Science, and this article outlines a process that you can experiment with in your team.

  Tags: Advice, Checklist, Data Science, Deployment, KPI

• Software Interfaces for Machine Learning Deployment - Mar 11, 2020.
  
  While building a machine learning model might be the fun part, it won't do much for anyone else unless it can be deployed into a production environment. How to implement machine learning deployments is a special challenge with differences from traditional software engineering, and this post examines a fundamental first step -- how to create software interfaces so you can develop deployments that are automated and repeatable.

  Tags: API, Deployment, Machine Learning, MLOps, Software Engineering

• How Kubeflow Can Add AI to Your Kubernetes Deployments - Feb 21, 2020.
  
  As Kubernetes is capable of working with other solutions, it is possible to integrate it with a collection of tools that can almost fully automate your development pipeline. Some of those third-party tools even allow you to integrate AI into Kubernetes. One such tool you can integrate with Kubernetes is Kubeflow. Read more about it here.

  Tags: AI, Deployment, Kubernetes

• 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.

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

• What Does it Mean to Deploy a Machine Learning Model? - Feb 14, 2020.
  
  You are a Data Scientist who knows how to develop machine learning models. You might also be a Data Scientist who is too afraid to ask how to deploy your machine learning models. The answer isn't entirely straightforward, and so is a major pain point of the community. This article will help you take a step in the right direction for production deployments that are automated, reproducible, and auditable.

  Tags: Deployment, Machine Learning, MLOps

• Why are Machine Learning Projects so Hard to Manage? - Feb 3, 2020.
  
  What makes deploying a machine learning project so difficult? Is it the expectations? The people? The tech? There are common threads to these challenges, and best practices exist to deal with them.

  Tags: Deployment, Kaggle, Lukas Biewald, Machine Learning, Project Fail, Training Data

• Top 5 must-have Data Science skills for 2020 - Jan 8, 2020.
  
  The standard job description for a Data Scientist has long highlighted skills in R, Python, SQL, and Machine Learning. With the field evolving, these core competencies are no longer enough to stay competitive in the job market.

  Tags: 2020 Predictions, Agile, Cloud Computing, Data Science Skills, Deep Learning, Deployment, GitHub, NLP

• 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.

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

• Deploying a pretrained GPT-2 model on AWS - Dec 12, 2019.
  
  This post attempts to summarize my recent detour into NLP, describing how I exposed a Huggingface pre-trained Language Model (LM) on an AWS-based web application.

  Tags: AWS, Deployment, GPT-2, Natural Language Generation, NLP

• Deployment of Machine learning models using Flask - Dec 10, 2019.
  
  This blog will explain the basics of deploying a machine learning algorithm, focusing on developing a Naïve Bayes model for spam message identification, and using Flask to create an API for that model.

  Tags: Deployment, Flask, Machine Learning

• Moving Predictive Maintenance from Theory to Practice - Dec 9, 2019.
  
  Here are four common hurdles that need to be overcome before tapping into the benefits of predictive maintenance.

  Tags: Deployment, Machine Learning, MathWorks, MATLAB, Predictive Maintenance, Simulation

• MLOps for production-level machine learning [Nov 14 Webinar] - Nov 12, 2019.
  
  This live webinar, Nov 14 @ 12pm EST, on MLOps for production-level machine learning, will detail MLOps, a compound of “machine learning” and “operations”, a practice for collaboration and communication between data scientists and operations professionals to help manage the production machine learning lifecycle. Register now.

  Tags: cnvrg.io, Deployment, DevOps, Machine Learning, MLOps

• An Eight-Step Checklist for An Analytics Project - Nov 6, 2019.
  
  Follow these eight headings of an audit sheet that business analysts should address before submitting the results of their analytics project. One recommended approach is to rewrite each step as a question, answer it, and then attach it to your project.

  Tags: Analytics, Checklist, Deployment, Feature Selection, Statistics

• MLOps for production-level machine learning - Nov 1, 2019.
  
  This live webinar, Nov 14 @ 12pm EST, on MLOps for production-level machine learning, will detail MLOps, a compound of “machine learning” and “operations”, a practice for collaboration and communication between data scientists and operations professionals to help manage the production machine learning lifecycle. Register now.

  Tags: cnvrg.io, Deployment, DevOps, Machine Learning, MLOps

• Why is Machine Learning Deployment Hard? - Oct 29, 2019.
  
  Developing an excellent machine learning model is one thing. Deploying it to production is another. Consider these lessons learned and recommendations for approaching this important challenge to help ensure value from your AI work.

  Tags: Deployment, Machine Learning

• How to Easily Deploy Machine Learning Models Using Flask - Oct 17, 2019.
  
  This post aims to make you get started with putting your trained machine learning models into production using Flask API.

  Tags: Deployment, Flask, Machine Learning, Python

• Four questions to help accurately scope analytics engineering project - Oct 9, 2019.
  
  Being really good at scoping analytics projects is crucial for team productivity and profitability. You can consistently deliver on time if you work out the issue first, and these four questions can help you prepare.

  Tags: Analytics, Data Engineering, Deployment

• 5 Fundamental AI Principles - Oct 3, 2019.
  
  While AI may appear magical at times, these five principles will help guide you to avoid pitfalls when leveraging this tech.

  Tags: AI, Data Cleaning, Deployment, Training Data

• TensorFlow 2.0: Dynamic, Readable, and Highly Extended - Aug 27, 2019.
  
  With substantial changes coming with TensorFlow 2.0, and the release candidate version now available, learn more in this guide about the major updates and how to get started on the machine learning platform.

  Tags: Deep Learning, Deployment, Exxact, TensorFlow

• 6 Key Concepts in Andrew Ng’s “Machine Learning Yearning” - Aug 12, 2019.
  
  If you are diving into AI and machine learning, Andrew Ng's book is a great place to start. Learn about six important concepts covered to better understand how to use these tools from one of the field's best practitioners and teachers.

  Tags: AI, Andrew Ng, Best Practices, Deployment, Machine Learning, Metrics, Training Data

• Easily Deploy Deep Learning Models in Production - Aug 1, 2019.
  
  Getting trained neural networks to be deployed in applications and services can pose challenges for infrastructure managers. Challenges like multiple frameworks, underutilized infrastructure and lack of standard implementations can even cause AI projects to fail. This blog explores how to navigate these challenges.

  Tags: Deep Learning, Deployment, GPU, Inference, NVIDIA

• Scaling a Massive State-of-the-art Deep Learning Model in Production - Jul 15, 2019.
  
  A new NLP text writing app based on OpenAI's GPT-2 aims to write with you -- whenever you ask. Find out how the developers setup and deployed their model into production from an engineer working on the team.

  Tags: Deep Learning, Deployment, NLP, OpenAI, Scalability, Transformer

• 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.

  Tags: Deployment, Jupyter, Machine Learning, Production, Training Data

• Why organizations fail in scaling AI and Machine Learning - May 29, 2019.
  
  We explain why AI needs to understand business processes and how the business processes need to be able to change to bring insight from AI into the process.

  Tags: AI, Deployment, Failure, Machine Learning, Scalability

• How to solve 4 big problems in data science – eBook. - Mar 26, 2019.
  
  This eBook includes insights on how data scientists from 4 leading companies delivered impressive business results such as accelerating global inventory from 48 hours to 45 minutes and reducing operational cost of analytics infrastructure by 30%. Get the eBook now!

  Tags: Data Science, Databricks, Deployment, ebook

• The Four Levels of Analytics Maturity - Mar 26, 2019.
  
  We outline our four-step model to categorize how successfully a company uses analytics by its ability to show the analytics, uncover underlying trends, and take action based on them.

  Tags: Analytics, Business, Deployment, Performance, Visualization

• Automatic Machine Learning is broken - Feb 19, 2019.
  
  We take a look at the arguments against implementing a machine learning solution, and the occasions when the problems faced are not ML problems and can perhaps be solved using optimization, exploratory data analysis tasks or problems that can be solved with simple statistics.

  Tags: Automated Machine Learning, AutoML, Data Preparation, Deployment

• ModelOps – Get it done. 3 Day Webinar Mini-Series - Feb 15, 2019.
  
  Join us for an educational series ModelOps - Get it done. Learn how a combination of technology and processes can help solve modelOps.

  Tags: A/B Testing, AWS, Deployment, DevOps, Open Data Group, Predictive Modeling

• How to Adopt Machine Learning: Interviews with Technical & Business Leaders - Feb 11, 2019.
  
  This 8 chapter series includes interviews with technical and business leaders from a number of large companies with the aim to help you adopt machine learning in your organization.

  Tags: ActiveState, Deployment, Interview, Machine Learning

• How to Monitor Machine Learning Models in Real-Time - Jan 18, 2019.
  
  We present practical methods for near real-time monitoring of machine learning systems which detect system-level or model-level faults and can see when the world changes.

  Tags: Anomaly Detection, Deployment, Machine Learning, MapR, Monitoring, Real-time

• [Webinar] Accelerating Machine Learning on Databricks - Jan 9, 2019.
  
  In this webinar, we will cover some of the latest innovations brought into the Databricks Unified Analytics Platform for Machine Learning.

  Tags: Databricks, Deep Learning, Deployment, Machine Learning

• A Machine Learning Deep Dive [Webinar, Dec 13] - Dec 11, 2018.
  
  Learn how ShopRunner uses Databricks on AWS and Snowflake to tackle data science problems across personalization, recommendations, targeting, and analysis of text and images.

  Tags: AWS, Databricks, Deployment, Machine Learning, Personalization

• Making Machine Learning Accessible [Webinar Replay] - Nov 27, 2018.
  
  Learn the business "why" and technical "how" for implementing machine learning in your organization - watch now.

  Tags: ActiveState, Deployment, 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.

  Tags: AI, Deployment, Ethics, Machine Learning, Production

• Self-Service Analytics and Operationalization – Why You Need Both - Nov 12, 2018.
  
  Get the guidebook / whitepaper for a look at how today's top data-driven companies scale their advanced analytics & machine learning efforts.

  Tags: Analytics, Data Science, Dataiku, Deployment, Self-service

• Top Obstacles to Overcome when Implementing Predictive Maintenance - Oct 29, 2018.
  
  We investigate how to create a systematic approach to predictive maintenance, ensuring there's enough data to create accurate systems. This post also explains how to identify a failure source and knowing how to predict it.

  Tags: Deployment, MathWorks, Predictive Maintenance, Simulation

• 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, MLOps

• 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.

  Pages: 1 2

  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