Vue and Django work very well together. However I'd recommend completely decoupling the frontend and backend source code - and avoid using django templates entirely. Instead expose REST endpoints using django rest framework
"When you google for Django + Vue, most results will be focused on using Django for your backend and Vue for a separate frontend project."
There are times when this makes sense, but there are times when you just need to sprinkle a but of Vue magic onto part of your site, without turning the whole thing into an SPA.
I once developed a UI to enable a non-technical user to answer 20 multiple-choice questions about a video. Some of the UX requirements I teased out during the process:
- easy to answer questions whilst watching the video
- quick to navigate to a specific question
- video visible at all times
- short+long summary of each answer visible when answering a specific question
- long description of question visible
- can be operated using mouse or keyboard (on laptop/desktop) or touch (tablet)
The rest of the site was regular Django, with a bit of jQuery here and there. I settled on:
- a regular Django form, with a single field to accept the a json string of the actual answers (the questions might change in future)
- a description of all the questions, descriptions, choices stored in a Django model, and sent to the page as json
- Vue rendering part of the page (the bit with the category selectors, question text, choices, descriptions)
Vue helped me deliver a pretty slick UI and didn't add any technical debt. (Not saying I didn't add any technical debt overall, just that the front-end part didn't contribute.)
Yes, serverside rendering is generally more performant than a decoupled client/server-side approach. If server-side rendering is a requirement of the project I'd look into nuxt.js + vue
I was commenting more generally on the approach to building a web app with Django and including external js libraries in Django templates, which I've done in the past as project requirements have changed over time. After including external js libraries to Django templates, there is a lot less support in terms of resources and supporting libraries such as testing frameworks. If the client-side project is initialized with Vue, the project can benefit from the overwhelming amount of supporting resources.
I've been working in data roles for 10 years and hold a masters in ML. I've hired and managed each of the roles you mentioned. I think of the responsibilities of each of those roles as:
-ML Engineers as building software infrastructure to scale machine learning inference and training.
-Data engineers focusing on data infrastructure and pipelining into either model inference, training, or other business intelligence platforms
-Analysts consume the product of the data engineer in the BI platform or excel, where the results would be consumed as a report in some form.
-And ML Researchers would be those inventing novel machine learning algorithms to deploy in the ML Infrastructure managed by the ML Engineers
-And data scientists to deploy well-known ML algorithms or statistical inference on varying datasets on the ML Infrascturue or as a slide deck.
Depends on the amount of data, reports, pipelines... If the company is small you might not have any of these problems. Every Mom&Pop store has some sort of data to run the business but they don't need a "data" person.
Once you have 10s of datastores + pipelines, 100s of reports and a "data lake" in the TBs you'll likely be needing specialized people.
So far I've spent my career in small teams / startups and it's starting to become apparent that a lot of what's assumed in these titles only applies in larger corporations where resources are abundant and it makes business sense to have a specialist focused on a single aspect.
Unfortunately I'm at a point where I have 'jack of all trades master of none' syndrome and it's causing me to fall in between the cracks professionally. I'd like to move to a larger company where I can develop deep expertise in a narrow topic.
ymmv, but as a data scientist at young startups, I often am the one giving new tasks to the software engineers, and facilitate teaching and training if they need help.
Stay Wanderful | Software Engineers, ML Engineers, UX, Design | New York, NY | On-site
Stay Wanderful is a multi-sided platform with a new take on loyalty. We connect consumers, hotels, and merchants, driving business growth for our partners through instantly gratifying, personalized rewards via machine learning
We're pre-series A, which translates to small size and big impact. We're looking for a few more software engineers to help out with these mission critical projects: dynamic content optimization, personalization, machine translation, self-serve design editors, email marketing capabilities, data engineering. We operate in a microservices, machine learning friendly architecture, with a handful of django rest apps, Vue 2.0 front end, and scalable distributed data processing apps on AWS.
Basically, about 2 seconds for the road signs photo. 6+ seconds for the spreadsheet image (with occasional timeouts). So, probably not optimized/ideal for reading large amounts of text
They're claiming the Pi3 benchmarks at 10x the speed of the Pi1, and the Pi2 hit 6x, so they're claiming that the Pi3 will be 166% the speed of the Pi2 at doing at least a few things. Would that be enough of a speed boost to get reasonable framerates, from what you've seen?
And request timeouts against cognito-idp.us-east-1.amazonaws.com
And the cognito console won't load