The Lifecycle Of Machine Learning In 2021 – Programming Insider

Machine Learning has evolved the already existing computer systems by providing them the ability to automatically learn without being explicitly programmed and assist them in finding the solutions to problems of various projects.

The following blog will help you understand the story of the Machine Learning Life-cycle better along with valuable insights into the business problems and solutions to those problems after deploying the Machine Learning model. The blog is written specifically to help both the beginners and mid-level practitioner in the process of building an end-to-end ML Lifecycle model. 

Starting off with the most basic question, what is machine learning lifecycle? The answer to this is very simple. Basically, a ML lifecycle is the process of developing, deploying and managing a ML model for some specific application. Building a Machine Learning model is a frequentative and iterative process. To build a successful ML Model, most of the steps are repeated several times to obtain the optimal results and thus the deployment gets successful. 

To further assist you with getting to know the steps involved in a ML Lifecycle model, here is a list of things you should do.

Steps Involved In An Machine Learning Lifecycle

  1. Defining the problem, keeping in mind the business context.
  2. Making use of AI problem after translating. 
  3. Planning and fixing goals.
  4. Gathering the required data and understanding it.
  5. Data preparation
  6. Data cleaning
  7. Data analysis
  8. Machine Learning model assumption.
  9. Preparing the data for modeling
  10. Building the model.
  11. Model evaluation and validation.
  12. Model deployment.

Undoubtedly, the most important thing in the entire process is to understand and know the purpose of the problem. Because the result depends on how good our understanding of the problem has been, so it becomes necessary to understand every detail in the problem. The problems are solved by creating a ML system known as the “Model”. This model is prepared by providing “training” and this training requires data.

Let’s have a deeper look into the steps involved in Machine Learning Lifecycle.

1. Determining The Objective 

As we’ve earlier established that to solve the problem, the first step is to understand the problem. The Lifecycle typically starts with determining the objective of the business. For example, the business objective of a bank can be decreasing fraudulent transactions.

2. Data Collection

To fulfill the established business objective, you need to collect the relevant data for ML Model. After this, the data is made to go through an exploratory analysis and data visualization to get to know the essence of data. From what the available data provides to rectification of unnecessary information and then to making the data ready before training.

3. Data Processing

Once the data is analyzed thoroughly, it is transformed accordingly to satisfy the business objective and to make it ready for the Machine Learning Model. This entire process includes, data cleaning, validation, test sets and feature engineering 

(transforming the data for better representation of business objective.)

4. Model Training

In this phase, the Ml model is trained on the prepared data. It is an iterative process in which you test various algorithms. 

5. Model Testing

The prepared model is then tested to ensure that its predictive performance is adequate. The model testing and validation process also makes sure to address some issues such as excessive resource requirements and insufficient performance.

6. Model Deployment

The final model after testing is then deployed to make predictions. This deployment can be of three types. The online deployment, the batch deployment and the embedded model.

7. Model monitor

After deployment of the ML model, its performance is monitored to ensure that it works well over time. And if any changes are required then corrective measures are taken.

Advantages Of Machine Learning Model Lifestyle

As far as advantages of ML is considered, the examples are endless. These advantages tells us a lot about how using Machine Learning would benefit us and our business to be particular. Here we’ll take a look at some of the most common advantages which are really helpful. 

  • Automation

Machine Learning plays a vital role in cutting down the workload and increasing the efficiency. Having a set algorithm that does all your work without any delays or any error sounds like a relief from burden of performing hard work. The reason people have started to rely more on automation these days are its traits

 of being reliable and efficient. Also, by reducing the workload, the human workforce can focus more on the creative aspect of the business.

  • Diverse Application

The sheer diversity with which ML can function is quite impressive. Literally, Machine Learning models can be applied on any of the major fields such as medical, business, banking and tech. Ml creates more opportunities. Therefore, it also helps in improving the customer interaction and service.

From an investment point of view, looking at the growing popularity of ML from detecting diseases in medical field to lifting up the tech firm’s revenue, ML is worth investing in.

  • Scope Of Improvement

Machine Learning in ever evolving and therefore has a lot of potential to become the top technology in upcoming years. The reason behind this is, it has a lot of functional areas of research in it. This assists the improvement of both software and hardware.ML is used in hardware such as laptops and GPUs for increasing the processing power of the system. While ML in software have various libraries in use which helps in designing more efficient algorithms.

  • Handles Data With Efficiency

One of the most reliable factors of ML is its efficient data handling. ML can handle any type of data at this point of time. One of high points of Machine Learning Model is that it is multidimensional and can handle different types of data and can analyze them, that normal systems fail to do. 

  • Great For Online Shopping And Education

 It is pretty evident that how ML helps increase the creativity. In future, Machine Learning Models can prove to be a great tool for education as they provide creative techniques to help students study and improve there focus.

In the same way, ML Models can be extremely helpful for online shopping. How is works is that, ML scans your search history and based on that, it would provide you with advertisements of companies of your interest. Thus, it can be a great way of improving the e-commerce industry.

As we’ve had a fair look at the benefits of Machine Learning, let’s go through some of the drawbacks or disadvantages of ML.

Disadvantages Of Machine Learning Model

  • The manual selection of algorithms.
  • Inconsistency in data acquisition.
  • Sometimes hamper the CPUs power and takes more time to process large data.
  • Possibility of errors in selection of algorithm.

From the above blog, we can understand the complexities of ML. The advantages and drawbacks of this technology tells us a lot about certain aspects involved like algorithm design and decision making. Looking at the increasing popularity of Machine Learning Model in practices like automation of the lifecycle, standardization of the process and continuous training, opting for it a great addition. However some challenges like manual labor and scalability needs to be addressed.

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