Machine Learning Real value comes from actually deploying a machine learning solution into production and the necessary monitoring and optimization work that comes after it.
Most of the problem nowadays as I have made a machine-learning model but what next.
How it is available to the end user, the answer is through API, but how it works?
How you can understand where the Docker stands and how the to monitor the build we created.
This Course have been design to keep these areas under consideration. The combination of industry standard build pipeline with some of the most common and important tools.
At the end of this course, you will be able to:
- Learn about Building NLP model
- Tuning the hyper-parameters and selecting the best model using cross validation
- Using Flask and API building
- Use of Docker and writing Docker file
- Understanding the concept of GitLab and end-to-end integration of Jenkins
- Basic programming in any language
- Some exposure to Python (but not mandatory)
- Learn the synchronization of DevOps & Machine Learning
- Learn flask for to expose your model through API
- Building Natural Language, processing Sentiment Analysis model and Expose it with API
- Build your own sentiment Analysis NLP model
- Select the most efficient Machine Learning Model, tune the Hyper-parameter, and deploy it on our server cluster
- Learn Docker , Docker Files, Docker Applications & Docker Containers (DevOps)
- Learn basics of GitLab, Jenkins and configure CI-CD Pipeline for end-to-end integration
- Complete Resources for getting started with all these technology