Machine Learning

We will not get lost in more details however it's good to know that MLflow help in managing ML models and we can deploy it to cloud services like Amazon sagemaker , Azure and kubernetes etc. pip install mlflow  or pip install mlflow[extras] Components of MLflow: 1. Tracking 2. Model Registry  3. Model 4. Projects 

Flask is web application framework in python. It's built on the Werkzeug WSGI toolkit and Jinja2 template engine.  Web server gateway interface (WSGI) helps in requests, response object and other utility functions. Jinja2 : templating engine helps in rendering dynamic web pages.  Pip install Flask  ML model deployment using flask Step 1 : Pickling the ML model  import pickle  pickle.dump(model1, open('myfirst_model.pkl','wb')) Pickle module help in serializing ML model and save to a file.  Later we can load model from file and deserialize to make predictions. model = pickle.load(open('myfirst_model.pkl','rb')) 

Machine learning is part of Artificial Intelligence (AI) that help computer to learn without explicit programming.  - learn from data and predict pattern. There are 2 type of Machine learning category mainly. 1. Supervised learning : need some existing sample data with output level for training. Then it can predict bases on training. Example Classification and Regression. 2. Unsupervised learning : there is no output level for training. It get data and try group data based on similarities.  Steps involved in creating ML models. 1. Train the Model 2. Package model 3. Validate model 4. Deploy model 5. Monitor performance Some tools to help with ML development. 1. Apache Airflow : schedule, monitoring workflow or data pipeline. 2. Kubeflow : Orchestrate complicated workflow running on kubernetes. 3. MLflow : tracking ML experience, deployment of the model. 4. Flask : framework to deploy ML models. 5. Seldon : deployment in kubernetes at a massive scale.