- Getting Started with GitHub CLI - Mar 6, 2023.
Learn about the super command line tool that makes it easy to create, view, and manage GitHub repositories.
MLOps
- 7 Best Tools for Machine Learning Experiment Tracking - Feb 20, 2023.
Tools for organizing machine learning experiments, source code, artifacts, models registry, and visualization in one place.
MLOps
- Learn MLOps From These GitHub Repositories - Feb 14, 2023.
Kickstart your MLOps career with these curated GitHub repositories.
MLOps
- skops: A New Library to Improve Scikit-learn in Production - Feb 1, 2023.
There are various challenges in MLOps and model sharing, including, security and reproducibility. To tackle these for scikit-learn models, we've developed a new open-source library: skops. In this article, I will walk you through how it works and how to use it with an end-to-end example.
MLOps
- Setup and use JupyterHub (TLJH) on AWS EC2 - Jan 23, 2023.
JupyterHub is a multi-user, container-friendly version of the Jupyter Notebook. However, it can be difficult to setup. This blog post will make you less likely to run into issues in this 15+ step process.
MLOps
- 12 Docker Commands Every Data Scientist Should Know - Jan 17, 2023.
Looking to add Docker to your data science toolbox? Here’s a list of essential Docker commands to help you get started.
MLOps
- Beginner’s Guide to Cloud Computing - Jan 10, 2023.
Learn how cloud computing works, different types of models, top cloud platforms, and applications.
MLOps
- The Complete MLOps Study Roadmap - Dec 14, 2022.
Kickstart your career as an MLOps Engineer with this study roadmap.
MLOps
- Top 10 MLOps Tools to Optimize & Manage Machine Learning Lifecycle - Oct 25, 2022.
As more businesses experiment with data, they realize that developing a machine learning (ML) model is only one of many steps in the ML lifecycle.
MLOps
- The Machine Learning Lifecycle - Sep 23, 2022.
Learn about the standard process for building sustainable machine learning applications.
MLOps
- The Absolute Basics of MLOps - Sep 22, 2022.
This article is for people who don’t know a thing about MLOps or want to refresh their memory.
MLOps
- Is There a Way to Bridge the MLOps Tools Gap? - Aug 16, 2022.
Converting Jupyter notebooks to a well-designed software system is a mandatory step in every ML project. But there is a notable lack of tooling to assist developers with such translation, beyond the basic nbconvert utility.
MLOps
- Free MLOps Crash Course for Beginners - Aug 1, 2022.
Interest in, and demand for, MLOps is growing exponentially. What, exactly, is it? Why is it important? Where should you turn next to learn more? Check out this crash course to find the answers to these questions and more.
MLOps
- MLOps: The Key To Pushing AI Into The Mainstream - Jul 14, 2022.
In this blog, we will aim at discussing the reasons that make MLOps an essential aspect of pushing AI mainstream. Besides, we will highlight the capabilities of MLOps as a catalyst for AI implementation.
MLOps
- Machine Learning Model Management - Jul 4, 2022.
The tools used in the development cycle for Machine Learning and the managing of the models require MLOps - Machine Learning Operations.
MLOps
- Learn MLOps with This Free Course
- Jun 6, 2022.
Learn to train and track your experiments, create ML pipelines, model deployment, monitor the performance in production, and adopt best practices from DevOps.
MLOps
- Design Patterns in Machine Learning for MLOps - Feb 23, 2022.
This article outlines some of the most common design patterns encountered when creating successful Machine Learning solutions.
MLOps
- Ploomber vs Kubeflow: Making MLOps Easier - Feb 14, 2022.
This article covers some background on Ploomber, Kubeflow pipelines, and why we need those tools to make our lives easier.
MLOps
- Accelerating AI with MLOps - Nov 24, 2021.
Companies are racing to use AI, but despite its vast potential, most AI projects fail. Examining and resolving operational issues upfront can help AI initiatives reach their full potential.
AI, Deployment, MLOps
Machine Learning Model Development and Model Operations: Principles and Practices - Oct 27, 2021.
The ML model management and the delivery of highly performing model is as important as the initial build of the model by choosing right dataset. The concepts around model retraining, model versioning, model deployment and model monitoring are the basis for machine learning operations (MLOps) that helps the data science teams deliver highly performing models.
Algorithms, Deployment, Feature Engineering, Machine Learning, MLOps
- Level-Up This November with the ODSC West 2021 Keynotes and Training Sessions - Oct 20, 2021.
At ODSC West 2021 this November 16th-18th, we’ll have 80+ training sessions and workshops on essential tools and languages led by some of the best and brightest minds in data science and AI.
Data Engineering, Meetings, MLOps, ODSC, Online Education
- Scale and Govern AI Initiatives with ModelOps - Sep 30, 2021.
AI/ML model life cycle automation and orchestration ensures reliable model operations and governance at scale. The path to production for each enterprise model can vary, along with different monitoring, continuous improvement, retirement needs. Organizations must now consider ModelOps as a fundamental capability towards operational excellence and immediate ROIs.
AI, MLOps, ModelOps, Scalability
- MLOps and ModelOps: What’s the Difference and Why it Matters - Sep 28, 2021.
These two terms are often used interchangeably. However, there are key distinctions between the functionality and features each provide, and the AI value and scalability at your organization depend on them.
Enterprise, MLOps, ModelOps
- Adventures in MLOps with Github Actions, Iterative.ai, Label Studio and NBDEV - Sep 16, 2021.
This article documents the authors' experience building their custom MLOps approach.
GitHub, Machine Learning, MLOps, Pipeline, Python, Workflow
- Leaders at Allstate, eBay & Red Bull Agree: Don’t Miss the Rev 3 Enterprise MLOps Summit - Aug 17, 2021.
Join data science and MLOps leaders in-person in Chicago this November.
Data Science, Domino, MLOps, Summit
- MLOps And Machine Learning Roadmap - Aug 12, 2021.
A 16–20 week roadmap to review machine learning and learn MLOps.
Courses, DataRobot, Deployment, DevOps, Kubeflow, Kubernetes, Machine Learning, Microsoft Azure, MLOps
- 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
- MLOps Best Practices - Jul 29, 2021.
Many technical challenges must be overcome to achieve successful delivery of machine learning solutions at scale. This article shares best practices we encountered while architecting and applying a model deployment platform within a large organization, including required functionality, the recommendation for a scalable deployment pattern, and techniques for testing and performance tuning models to maximize platform throughput.
Best Practices, MLOps
- When to Retrain an Machine Learning Model? Run these 5 checks to decide on the schedule - Jul 20, 2021.
Machine learning models degrade with time, and need to be regularly updated. In the article, we suggest how to approach retraining and plan for it in advance.
Data Science, Deployment, Machine Learning, MLOps
- MLOps is an Engineering Discipline: A Beginner’s Overview - Jul 8, 2021.
MLOps = ML + DEV + OPS. MLOps is the idea of combining the long-established practice of DevOps with the emerging field of Machine Learning.
Data Engineering, Deployment, Machine Learning, MLOps, Modeling
- Unleashing the Power of MLOps and DataOps in Data Science - Jun 29, 2021.
Organizations trying to move forward with analytics and data science initiatives -- while floating in an ocean of data -- must enhance their overall approach and culture to embrace a foundation on DataOps and MLOps. Leveraging these operational frameworks are necessary to enable the data to generate real business value.
Best Practices, Data Science, DataOps, MLOps
- Easy MLOps with PyCaret + MLflow - May 18, 2021.
A beginner-friendly, step-by-step tutorial on integrating MLOps in your Machine Learning experiments using PyCaret.
Machine Learning, MLflow, MLOps, PyCaret, Python
- Feature stores – how to avoid feeling that every day is Groundhog Day - May 6, 2021.
Feature stores stop the duplication of each task in the ML lifecycle. You can reuse features and pipelines for different models, monitor models consistently, and sidestep data leakage with this MLOps technology that everyone is talking about.
Data Preparation, Feature Store, Machine Learning, MLOps
- 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
- KDnuggets™ News 21:n13, Apr 7: Top 10 Python Libraries Data Scientists should know in 2021; KDnuggets Top Blogs Reward Program; Making Machine Learning Models Understandable - Apr 7, 2021.
Top 10 Python Libraries Data Scientists should know in 2021; KDnuggets Top Blogs Reward Program; Shapash: Making Machine Learning Models Understandable; Easy AutoML in Python; The 8 Most Common Data Scientists; A/B Testing: 7 Common Questions and Answers in Data Science Interviews, Part 1
Automated Machine Learning, AutoML, Data Science, Data Scientist, Explainable AI, Interpretability, Machine Learning, MLOps, Python
- Overview of MLOps - Mar 26, 2021.
Building a machine learning model is great, but to provide real business value, it must be made useful and maintained to remain useful over time. Machine Learning Operations (MLOps), overviewed here, is a rapidly growing space that encompasses everything required to deploy a machine learning model into production, and is a crucial aspect to delivering this sought after value.
Data Science, Deployment, Machine Learning, MLOps, Monitoring
A Machine Learning Model Monitoring Checklist: 7 Things to Track - Mar 11, 2021.
Once you deploy a machine learning model in production, you need to make sure it performs. In the article, we suggest how to monitor your models and open-source tools to use.
Checklist, Data Science, Deployment, Machine Learning, MLOps, Monitoring
- Feature Store as a Foundation for Machine Learning - Feb 19, 2021.
With so many organizations now taking the leap into building production-level machine learning models, many lessons learned are coming to light about the supporting infrastructure. For a variety of important types of use cases, maintaining a centralized feature store is essential for higher ROI and faster delivery to market. In this review, the current feature store landscape is described, and you can learn how to architect one into your MLOps pipeline.
Data Engineering, Data Infrastructure, Data Lake, Feature Engineering, Feature Store, Machine Learning, Metadata, MLOps, Pipeline
- Machine learning is going real-time - Jan 28, 2021.
Extracting immediate predictions from machine learning algorithms on the spot based on brand-new data can offer a next level of interaction and potential value to its consumers. The infrastructure and tech stack required to implement such real-time systems is also next level, and many organizations -- especially in the US -- seem to be resisting. But, what even is real-time ML, and how can it deliver a better experience?
China, Machine Learning, MLOps, Real-time, Stream Processing
- How to Use MLOps for an Effective AI Strategy - Jan 21, 2021.
The need to deal with the challenges and other smaller nuances of deploying machine learning models has given rise to the relatively new concept of MLOps. – a set of best practices aimed at automating the ML lifecycle, bringing together the ML system development and ML system operations.
AI, DevOps, Machine Learning, MLOps, Strategy
- KDnuggets™ News 21:n02, Jan 13: Best Python IDEs and Code Editors; 10 Underappreciated Python Packages for Machine Learning Practitioners - Jan 13, 2021.
Best Python IDEs and Code Editors You Should Know; 10 Underappreciated Python Packages for Machine Learning Practitioners; Top 10 Computer Vision Papers 2020; CatalyzeX: A must-have browser extension for machine learning engineers and researchers
Computer Vision, Data Science, DevOps, IDE, Machine Learning, MLOps, Python, Research
- MLOps: Model Monitoring 101 - Jan 6, 2021.
Model monitoring using a model metric stack is essential to put a feedback loop from a deployed ML model back to the model building stage so that ML models can constantly improve themselves under different scenarios.
AI, Data Science, DevOps, Machine Learning, MLOps, Modeling
- Model Experiments, Tracking and Registration using MLflow on Databricks - Jan 5, 2021.
This post covers how StreamSets can help expedite operations at some of the most crucial stages of Machine Learning Lifecycle and MLOps, and demonstrates integration with Databricks and MLflow.
Data Science, Databricks, DataOps, Experimentation, MLflow, MLOps, Modeling, StreamSets
- KDnuggets™ News 20:n48, Dec 23: Crack SQL Interviews; MLOps – Why and How; 2021 AI, Data Science, ML Predictions - Dec 23, 2020.
In this last issue of the year learn how to crack SQL interviews, find why and how of MLOps, check top online courses Data Science, and read the predictions for AI, Data Science, and Machine Learning from our panel of experts and a group of innovative companies.
2021 Predictions, Courses, MLOps, SQL
- MLOps Is Changing How Machine Learning Models Are Developed - Dec 21, 2020.
Delivering machine learning solutions is so much more than the model. Three key concepts covering version control, testing, and pipelines are the foundation for machine learning operations (MLOps) that help data science teams ship models quicker and with more confidence.
Deployment, Machine Learning, MLOps
- MLOps – “Why is it required?” and “What it is”? - Dec 18, 2020.
Creating an model that works well is only a small aspect of delivering real machine learning solutions. Learn about the motivation behind MLOps, the framework and its components that will help you get your ML model into production, and its relation to DevOps from the world of traditional software development.
Data Science, DevOps, MLOps
Main 2020 Developments and Key 2021 Trends in AI, Data Science, Machine Learning Technology - Dec 9, 2020.
Our panel of leading experts reviews 2020 main developments and examines the key trends in AI, Data Science, Machine Learning, and Deep Learning Technology.
2021 Predictions, AI, AutoML, Bill Schmarzo, Carla Gentry, COVID-19, Doug Laney, GPT-3, Kirk D. Borne, Machine Learning, MLOps, Predictions, Ronald van Loon, Tom Davenport, Trends
- Primer on TensorFlow and how PerceptiLabs Makes it Easier - Nov 18, 2020.
With PerceptiLabs, beginners can get started building a model more quickly, and those with more experience can still dive into the code. Given that PerceptiLabs runs TensorFlow behind the scenes, we thought we'd walk through the framework so you can understand its basics, and how it is utilized by PerceptiLabs.
AutoML, Deep Learning, Machine Learning, MLOps, PerceptiLabs, TensorFlow
- 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
- Data Science Meets Devops: MLOps with Jupyter, Git, and Kubernetes - Aug 21, 2020.
An end-to-end example of deploying a machine learning product using Jupyter, Papermill, Tekton, GitOps and Kubeflow.
Data Science, DevOps, Jupyter, Kubeflow, Kubernetes, MLOps
- Implementing MLOps on an Edge Device - Aug 4, 2020.
This article introduces developers to MLOps and strategies for implementing MLOps on edge devices.
Edge Analytics, Machine Learning, MLOps, Speech Recognition, Workflow
- A Tour of End-to-End Machine Learning Platforms - Jul 29, 2020.
An end-to-end machine learning platform needs a holistic approach. If you’re interested in learning more about a few well-known ML platforms, you’ve come to the right place!
AirBnB, Data Science Platform, Google, Machine Learning, MLOps, Netflix, Pipeline, Uber, Workflow
- What I learned from looking at 200 machine learning tools - Jul 21, 2020.
While hundreds of machine learning tools are available today, the ML software landscape may still be underdeveloped with more room to mature. This review considers the state of ML tools, existing challenges, and which frameworks are addressing the future of machine learning software.
Data Science Platform, Data Science Tools, Machine Learning, MLOps, Open Source, Tools
- Taming Complexity in MLOps - May 28, 2020.
A greatly expanded v2.0 of the open-source Orbyter toolkit helps data science teams continue to streamline machine learning delivery pipelines, with an emphasis on seamless deployment to production.
Best Practices, Docker, MLOps, Python
- 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.
AI, Automation, Deployment, DevOps, Infrastructure, MLOps
- ModelDB 2.0 is here! - Mar 19, 2020.
We are excited to announce that ModelDB 2.0 is now available! We have learned a lot since building ModelDB 1.0, so we decided to rebuild from the ground up.
MLOps, ModelDB, Modeling, Version Control
- 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.
API, Deployment, Machine Learning, MLOps, Software Engineering
- 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
- 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.
Deployment, Machine Learning, MLOps
- Managing Machine Learning Cycles: Five Learnings from comparing Data Science Experimentation/ Collaboration Tools - Jan 29, 2020.
Machine learning projects require handling different versions of data, source code, hyperparameters, and environment configuration. Numerous tools are on the market for managing this variety, and this review features important lessons learned from an ongoing evaluation of the current landscape.
Collaboration, Comet.ml, Data Operations, Data Workflow, DataOps, MLflow, MLOps, Pipeline, Reproducibility
- A bird’s-eye view of modern AI from NeurIPS 2019 - Jan 28, 2020.
With the explosion of the field of AI/ML impacting so many applications and industries, there is great value coming out of recent progress. This review highlights many research areas covered at the NeurIPS 2019 conference recently held in Vancouver, Canada, and features many important areas of progress we expect to see in the coming year.
AI, Efficiency, GANs, Graph, Interpretability, MLOps, Neural Networks, NeurIPS, NLP, Reinforcement Learning, Trends
- Live Webinar: Learn how to build better machine learning pipelines - Jan 6, 2020.
In this webinar, Jan 15 @ 12PM EST, we'll offer solutions to the common challenges data scientists and data engineers face when building a machine learning pipeline. Register now to attend live or to watch a recording afterwards.
cnvrg.io, Machine Learning, MLOps, Pipeline, Webinar
- 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.
cnvrg.io, Deployment, DevOps, Machine Learning, MLOps
- 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.
cnvrg.io, Deployment, DevOps, Machine Learning, MLOps
- [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.
ActiveState, Deployment, DevOps, Machine Learning, MLOps
- Operational Machine Learning: Seven Considerations for Successful MLOps - Apr 30, 2018.
In this article, we describe seven key areas to take into account for successful operationalization and lifecycle management (MLOps) of your ML initiatives
DevOps, Machine Learning, Metrics, MLOps
