Machine Learning Development Cycle

Probably it is not confirmed the total number of Development Cycle. But according to my research I found somewhat 9 Cycle.

They are :-

1. Frame the problem
2. Gathering Data
3. Data preprocessing
4. Exploratory Data Analysis(EDA)
5. Features Engineering And Selection
6. Model Training , Evaluation and Selection
7. Model Deployment
8. Testing
9. Optimizing.

Now we will know each of these one by one .

1. Frame the problem:-

Choosing a machine learning method to implement data is not the easiest of processes. It is essential to first understand the precise business problem and its objectives. For instance, understanding what needs to be predicted and understanding potential outcomes is critical.

One also needs to know what data should be used to train a model, among other factors. Such considerations help with the framing of a machine learning problem. In this article, we will look at how to frame a machine learning problem correctly.

2. Gathering Data:-

Gathering data is the most important step in solving any supervised machine learning problem. Your text classifier can only be as good as the dataset it is built from.

If you don’t have a specific problem you want to solve and are just interested in exploring text classification in general, there are plenty of open source datasets available. On the other hand, if you are tackling a specific problem, you will need to collect the necessary data. Many organizations provide public APIs for accessing their data — for example, the Twitter API or the NY Times API. You may be able to leverage these for the problem you are trying to solve.

Here are some important things to remember when collecting data:

• If you are using a public API, understand the limitations of the API before using them. For example, some APIs set a limit on the rate at which you can make queries.
• The more training examples (referred to as samples in the rest of this guide) you have, the better. This will help your model generalize better.
• Make sure the number of samples for every class or topic is not overly imbalanced. That is, you should have comparable number of samples in each class.
• Make sure that your samples adequately cover the space of possible inputs, not only the common cases.

3.Data preprocessing:-

Pre-processing refers to the transformations applied to our data before feeding it to the algorithm. Data Preprocessing is a technique that is used to convert the raw data into a clean data set. In other words, whenever the data is gathered from different sources it is collected in raw format which is not feasible for the analysis.

4.Exploratory Data Analysis(EDA):-

Exploratory Data Analysis (EDA) is an approach to analyze the data using visual techniques. It is used to discover trends, patterns, or to check assumptions with the help of statistical summary and graphical representations.

5.Features Engineering And Selection:-

Feature engineering is the pre-processing step of machine learning, which is used to transform raw data into features that can be used for creating a predictive model using Machine learning or statistical Modelling. Feature engineering in machine learning aims to improve the performance of models. In this topic, we will understand the details about feature engineering in Machine learning.

6.Model Training , Evaluation and Selection:-

An essential step in the machine learning workflow is model selection and evaluation. The process step when we analyses our model is this one. We decide what steps to take to improve this model based on more meaningful performance statistics. A model that performs well and a model that performs extremely well are typically separated by this step. When we analyses our model, we have a better understanding of what it predicts accurately and what it doesn’t, which enables us to improve its accuracy from 65% to more like 80% or 90%.

7.Model Deployment:-

The process of integrating a machine learning model into an already-existing production environment is known as deployment, and it allows you to use data to make useful business decisions. It can be one of the most challenging stages of the machine learning life cycle and is one of the final ones. Frequently, traditional model-building languages are incompatible with an organization’s IT systems, requiring data scientists and programmers to spend considerable time and brainpower rebuilding them.

8.Testing:-

We’ll talk about several methods for efficient ML testing. You will discover how to evaluate and test models, get around typical roadblocks, and more.

Software testing typically entails:

unit testing : The programme is divided into blocks, and each block’s constituent unit is tested independently.

Regression Test : To ensure that previously tested software doesn’t unexpectedly break, they cover it.

Integration tests : This kind of testing looks at how the program’s many components interact with one another.

In addition, people adhere to a set of principles, such as not merging code until all tests have been passed, testing all newly added code blocks, and writing tests to capture bugs.

9.Optimizing:-

Optimization is the process where we train the model iteratively that results in a maximum and minimum function evaluation. It is one of the most important phenomena in Machine Learning to get better results.

The principal goal of machine learning is to create a model that performs well and gives accurate predictions in a particular set of cases. In order to achieve that, we need machine learning optimization.