Duration
5 days
Outline
- 1. End to End Time Machine Learning (2 days) (Retail Sales Data)
a. Basics of Machine Learning
i. Types of machine learning
ii. Deep learning and machine learning
iii. ML life cycle
iv. ML metrics
b. Deep Learning with neural networks
c. Using RNN/LSTM/GRU based deep neural networks for forecasting
- 2. Performance Management of ML Models (1 day)
a. Measuring performance
b. Improvement of models with Quantization
c. Measuring performance before and after quantization
d. Peek into Federated Learning (if time permits POC)
- 3. Data Preprocessing and Steps for ML (1 day)
a. Practical illustration of steps for ML data preprocessing in a real case study (structured data, text data)
b. Typical preprocessing steps and cleaning
c. EDA of data
d. Statistical Properties of Data
e. Dimensionality Reduction and Identification of right components in multivariate scenario
- 4. APUI zation of ML (1/2 day)
a. Practicality of REST Apis for ML
b. Best Practices for REST API design
c. Design and Deploy REST API with ML
d. Inference as API
- 5. CI/CD and Orchestration of ML (1/2 day)
a. Overview of MLFlow
b. Illustration of MLFlow components
c. Practical illustration of MLFlow
Programme scope, delivery arrangements and suitability are confirmed during enquiry.