- Coding Ethics for AI & AIOps: Designing Responsible AI Systems - Aug 26, 2021.
AI ops has taken Human machine collaboration to the next level where humans and machines are not just coexisting but are collaborating and working together like team members.
AI, Bias, DevOps, Ethics, ModelOps, Responsible AI
- 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 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
- 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
- You Don’t Have to Use Docker Anymore - Oct 29, 2020.
Docker is not the only containerization tool out there and there might just be better alternatives…
Containers, Data Engineering, DevOps, Docker
Automating Every Aspect of Your Python Project - Sep 18, 2020.
Every Python project can benefit from automation using Makefile, optimized Docker images, well configured CI/CD, Code Quality Tools and more…
Development, DevOps, Docker, Programming, Python
- 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
- Nitpicking Machine Learning Technical Debt - Jun 8, 2020.
Technical Debt in software development is pervasive. With machine learning engineering maturing, this classic trouble is unsurprisingly rearing its ugly head. These 25 best practices, first described in 2015 and promptly overshadowed by shiny new ML techniques, are updated for 2020 and ready for you to follow -- and lead the way to better ML code and processes in your organization.
Pages: 1 2
Best Practices, DevOps, Explainability, Interpretability, Machine Learning, Monitoring, Pipeline, Technical Debt, Version Control
- 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
- 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
- Observability for Data Engineering - Feb 10, 2020.
Going beyond traditional monitoring techniques and goals, understanding if a system is working as intended requires a new concept in DevOps, called Observability. Learn more about this essential approach to bring more context to your system metrics.
Data Engineering, DevOps, Explainability, KPI, Monitoring, Time Series
12-Hour Machine Learning Challenge: Build & deploy an app with Streamlit and DevOps tools - Feb 3, 2020.
This article will present the knowledge, process, tools, and frameworks required for completing a 12-hour ML challenge. I hope you can find it useful for your personal or professional projects.
App, Challenge, DevOps, Machine Learning, Streamlit
- 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
- Easy, One-Click Jupyter Notebooks - Jul 24, 2019.
All of the setup for software, networking, security, and libraries is automatically taken care of by the Saturn Cloud system. Data Scientists can then focus on the actual Data Science and not the tedious infrastructure work that falls around it
Big Data, Cloud, Data Science, Data Scientist, DevOps, Jupyter, Python, Saturn Cloud
- 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.
A/B Testing, AWS, Deployment, DevOps, ModelOps, Open Data Group, Predictive Modeling
- The Evolution of Build Engineering in Managing Open Source [Webinar Replay] - Nov 13, 2018.
Explore how the role of build engineering is evolving to reconcile two key trends: massive wide-scale adoption of open source; the most devastating cyber-attacks in recent history tied to unpatched dependencies and other vulnerabilities.
ActiveState, Cybersecurity, DevOps, Open Source, Risks
- DevOps 2.0: Applying Machine Learning in the CI/CD Chain - Oct 2, 2018.
Explore how ML can be implemented in your organization, so you can (for example) enable the automated assessment of test results for far more complex criteria, such as defining thresholds based on statistical significance rather than just presence/absence of specific criteria.
ActiveState, Development, DevOps, Machine Learning, Software
- [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
- KDnuggets™ News 18:n29, Aug 1: Building an Awesome Data Science Portfolio; Data Science + DevOps = Taming the Unicorn - Aug 1, 2018.
Also: A Practitioner's Guide to Processing & Understanding Text: Data Retrieval with Web Scraping; Remote Data Science: How to Send R and Python Execution to SQL Server from Jupyter Notebooks; Best Deal in the Galaxy? Win KDnuggets Free Pass to Strata Data Conference NYC
Data Science, Data Scientist, DevOps, Jupyter, Portfolio, SQL, Unicorn, Web Scraping
- DevOps for Data Scientists: Taming the Unicorn - Jul 27, 2018.
How do we version control the model and add it to an app? How will people interact with our website based on the outcome? How will it scale!?
Data Science, Data Scientist, DevOps, Unicorn, Version Control
- Torus for Docker-First Data Science - May 8, 2018.
To help data science teams adopt Docker and apply DevOps best practices to streamline machine learning delivery pipelines, we open-sourced a toolkit based on the popular cookiecutter project structure.
Data Science, DevOps, Docker, Machine Learning Engineer, Open Source, Python
- 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
- National Grid: Dev Ops – Operations Engineer / Sr Ops Engineer – Advanced Analytics - Mar 21, 2018.
Seeking an Analytics Operations Engineer you will package, optimize, operationalize/productionize cloud based advanced analytical and big data software solutions.
Advanced Analytics, Brooklyn, DevOps, Engineer, National Grid, New York, NY
- Applying Machine Learning to DevOps - Feb 27, 2018.
This article explains the synergy between DevOps and Machine Learning and their applications like tracking application delivery, troubleshooting and triage analytics, preventing production failures, etc.
DevOps, Machine Learning
- GraphDB for DevOps – Live Online training from Ontotext - Jan 25, 2018.
This live online training is geared towards one single goal – to prepare developers and operations specialists who need to interact with GraphDB in their daily routine. For a limited time get 20% Early Bird discount.
DevOps, GraphDB, Ontotext, SPARQL
- Ingram Micro: Data Architect - Dec 19, 2017.
Seeking a talented and highly motivated Architect with 7+ years of experience for our Global Data Infrastructure team, with responsibilities ranging from sustaining current systems to developing the future state of the Enterprise Global Data Infrastructure Architecture framework.
CA, Data Architect, Data Infrastructure, DevOps, Ingram Micro, Irvine
- Data Version Control in Analytics DevOps Paradigm - Aug 14, 2017.
DevOps and DVC tools can help reduce time data scientists spend on mundane data preparation and achieve their dream of focusing on cool machine learning algorithms and interesting data analysis.
Analytics, Data Preparation, Data Science, DevOps, DVC, Open Source, Version Control
- Celgene: Director, Big Data Ops Lead - Jul 18, 2017.
Seeking a Big Data DevOps Lead. The role will establish and manage the operational services necessary to ensure proper management of the platform health and to support ongoing use of the platform for business insights generation.
Big Data, Celgene, DevOps, Director, NJ
- The dynamics between AI and IoT - Apr 18, 2017.
We see the need for a new type of Engineer who will combine knowledge from Electronics & IoT with Machine learning, AI, Robotics, Cloud and Data management (devops).
AI, Cloud Computing, Data Management, DevOps, Engineer, IoT, Robots
- R2: DevOps for Data Science - Feb 22, 2017.
Seeking a DevOps for data science to join the research and development team. The right person will find him or herself in an exciting and challenging environment, at the interface of quantitative science, software engineering and industrial processes.
Canada, Data Science, DevOps, Montreal, R2
- RCloud – DevOps for Data Science - Nov 28, 2016.
After almost two decades of software development, term – DevOps was coined and officially given importance to collaboration between development and deployment of software systems. In this early stage of Data Science field, use of standardized and empirical practises like DevOps will definitely speed up its evolution.
Collaboration, Data Science, DevOps, GitHub, R, Scalability
Big Data Science: Expectation vs. Reality - Oct 27, 2016.
The path to success and happiness of the data science team working with big data project is not always clear from the beginning. It depends on maturity of underlying platform, their cross skills and devops process around their day-to-day operations.
Big Data, Big Data Engineer, Data Science, Data Science Team, DevOps
- Connecting Data Systems and DevOps - Jun 17, 2016.
This post will explain why anyone transforming their company into a data-driven organization should care about software development best practices, even if they don’t consider themselves a software company.
Data Engineering, Data Science, Developers, DevOps, Software Engineering
- Health Integrated: Big Data DevOps Engineer - Sep 18, 2014.
Prototyping, new development and production hardening the Big Data Platform. Work with different teams to manage the build out and administration of the big data clusters.
Big Data Engineer, DevOps, Health Integrated, Tampa-FL
