You have spent weeks to clearly define a problem statement, now you have spent several weeks to collect and prepare the data, then you did Exploratory analysis for next few weeks or a month, and finally ready to build and try several ML models, and say you are then satisfied with one or two models and how they work. It's time to deploy them in a production data or on mobile devices and see what results does it bring?
In parallel to knowing how ML models are built, you also have to know how ML models are deployed in production, checked for accuracy and also maintain and tweak them when necessary.
Here are few course recommendation which you must study for Data Science/Machine Learning career.
This course will teach you how to deploy ML models on mobile devices and what else you will learn:
Prepare models for battery-operated devices
Execute models on Android and iOS platforms
Deploy models on embedded systems like Raspberry Pi and micro-controllers.
This course covers: Python web server frameworks, such as Flask, Django, and Dash.
Deploy and monitor model performance, deployment scripts, serializing models, APIs. also build recommendation system, deploy the recommender system, and then also complete a Capstone Project.
This course will cover how to deploy ML model on a Enterprise level. This course introduces you to spark, model management in IBM Watson cloud, how to scale data, high performance computing, Docker Containers, NVIDIA Docker, use Flask framework, case studies, Spark pipelines. Spark has become one of the key components today for machine learning.
This course will cover the following:
Deploy regression, NLP and computer vision machine learning models to scalable AWS Lambda environment. How to effectively prepare scikit-learn, spaCy and Keras / Tensorflow frameworks for deployment. How to use basics of AWS and Serverless Framework. How to monitor usage and secure access to deployed ML models and their APIs and much more.
We hope, these courses will teach you how to deploy your ML models in production and battery operated devices.
All the very best.
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