What are the challenges of deploying Data Science models in production?
The question is about data science
Answer:
Deploying Data Science models to production faces challenges such as model drift, where the performance of a model degrades because of a change in data patterns. Besides that, very important challenges are the scalability of the model, integration with existing systems, and keeping data secure. These issues require continuous monitoring and therefore updating of the models.
Hire remote Data science engineers
Developers who got their wings at:
Testimonials
Gotta drop in here for some Kudos. I’m 2 weeks into working with a super legit dev on a
critical project, and he’s meeting every expectation so far 👏
Francis Harrington
Founder at ProCloud Consulting, US
I recommend Lemon to anyone looking for top-quality engineering talent. We previously
worked with TopTal and many others, but Lemon gives us consistently incredible
candidates.
Allie Fleder
Co-Founder & COO at SimplyWise, US
I've worked with some incredible devs in my career, but the experience I am having with
my dev through Lemon.io is so 🔥. I feel invincible as a founder. So thankful to you and
the team!
Michele Serro
Founder of Doorsteps.co.uk, UK
Ready-to-interview vetted Data science engineers are waiting for your request