In this I have created a face recognising model using transfer learning by using the moblinet pre trained model
in this have a collected the dataset of various celebrity and add that datset to my model so that it can predict them
loading the moblienet model
1. Create container image that’s has Jenkins installed using dockerfile
2. When we launch this image, it should automatically starts Jenkins service in the container.
3. Create a job chain of job1, job2, job3 and job4 using build pipeline plugin in Jenkins
4. Job1 : Pull the Github repo automatically when some developers push repo to Github.
5. Job2 : By looking at the code or program file, Jenkins should automatically start the respective language interpreter install image container to deploy code ( eg. …
task 3 mlops +devops
MLOps means (Machine learning + Operations). MLOps is communication between data scientists and the operations or production team. It’s deeply collaborative in nature, designed to eliminate waste, automate as much as possible, and produce richer, more consistent insights with machine learning. ML can be a game changer for a business, but without some form of systemization, it can devolve into a science experiment.
MLOps brings business interest back to the forefront of your ML operations. Data scientists work through the lens of organizational interest with clear direction and measurable benchmarks. It’s the best of both worlds.Here…
TASK 1 : merging the github master and first branch
In this we merge the first branch with master branch this is the description of the code
If Developer push to first branch then Jenkins will fetch from first and deploy on dev-docker environment.
If Developer push to master branch then Jenkins will fetch from master and deploy on master-docker environment.
both dev-docker and master-docker environment are on different docker containers.
Manually the QA team will check (test) for the website running in dev-docker environment. …
MLOps (a compound of “machine learning” and “operations”) is a practice for collaboration and communication between data scientists and operations professionals to help manage production ML (or deep learning) lifecycle.
Let’s get started :-
~ Task Description overview:
1. Create container image that’s has Python3 and Keras or numpy installed using dockerfile
2. When we launch this image, it should automatically starts train the model in the container.
3. Create a job chain of job1, job2, job3, job4 and job5 using build pipeline plugin in Jenkins
4. Job1 : Pull the Github repo automatically when some developers push…