What is machine learning and how does it work? Different people will respond to your question in different ways.
Programmers may claim that it has to do with Python programming and advanced mathematical techniques.
Business partners frequently mix data, machine learning, and a hint of mystery.
Model training and data wrangling are common topics of conversation for machine learning engineers.
Who then is correct?
Data is the foundation of machine learning, no lie. Without sufficient data for the machine to learn from, there can be no machine learning. With the exponential increase in information available, machine learning development is now more accessible than ever.
It also makes perfect sense how machine learning and algorithms relate to one another. Indeed, there are complex mathematical methods that make machines learn. No math – no machine learning.
Finally, model training and data preparation are at the core of any ML project. Machine learning engineers spend significant time training models and preparing datasets. Therefore, it is the first thing on the minds of ML engineers.
Machine learning is about development, data manipulation, and modeling. These separate parts make up the lifecycle of a machine learning project, which is exactly what we’ll discuss in this blog.
A High-Level View Of The ML Lifecycle
A machine learning project’s life cycle can be visualized as a multi-component flow, where each subsequent phase influences the remainder of the flow. Let’s look at the steps in the flow at a very high level:
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Understanding the problem (aka business understanding).
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Data Collection.
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Data Annotation.
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Data Wrangling.
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Model development, training, and evaluation.
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Deploy and maintain the model in a production environment.
As you can see, the whole cycle consists of 6 consecutive steps. Each step is unique. It has its own character. These differences lead to the resources, time, and team members required to complete each step. Look at each lifecycle component and see what it’s all about.
1. Understanding The Problem
Every project starts with a problem you have to solve. Ideally, a clear definition of the problem should be numerically described. The numbers not only provide the ability to know where your starting point is but also allow you to track the effect of changes later.
2. Data Collection
Data collection is the first step in the machine learning lifecycle. The goal of this step is to identify and retrieve any data-related issues.
In this step, we need to identify different data sources because data can be collected from other sources such as files, databases, the internet, or mobile devices. It is one of the most critical steps in the life cycle. The quantity and quality of data collected will determine the effectiveness of the output. The more data there is, the more accurate the forecast will be.
This step includes the tasks below:
By performing the above task, we get a coherent set of data called a dataset.
3. Data Preparation
After collecting the data, we must prepare it for the next steps. Data preparation is the step where we put our data in a suitable place and prepare it for our machine learning training.
In this step, we put all the data together and then randomly sorted the data.
This method can be separated into two different steps:
Data exploration is used to better comprehend the nature of the data we use. We must comprehend the characteristics, structure, and caliber of the data.
An effective outcome is the consequence of a deeper knowledge of the data. We discover correlations, broad trends, and outliers in this.
Data preprocessing: Preparing the data for analysis is the next stage.
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4. Data Wrangling
Cleaning and transforming unusable raw data into a useful format is known as data wrangling. It is the procedure of prepping the data for analysis by cleaning it, choosing the appropriate variable, and putting it in the right format. It is among the most important steps in the whole procedure. In order to address quality issues, data cleaning is necessary.
We may not always use the data we collect because some may not be useful. In real-world applications, collected data can have various problems, including:
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Missing values
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Duplicate data
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Invalid data
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Noise
So we use different filtering techniques to clean the data.
It is mandatory to detect and eliminate the above problems, as they can negatively affect the quality of the result.
5. Model Development, Training, and Evaluation
The model must now be trained for the next stage. In this step, we teach our model to work better and produce better results when solving problems.
To train the model with different machine learning techniques, we use datasets. To learn the numerous patterns, laws, and features, a model must be trained.
We test the machine learning model once it has been trained on a specific dataset. In this phase, we give our model a test dataset to see if it is accurate.
The model’s percentage of accuracy is tested against the project’s requirements or issues.
6. Deploy The Model
Excellent! You have a great model ready for production. Now engineers deploy the training model and make it available for external inference requests.
This is the final step in the machine learning lifecycle. But the work is far from over. We can’t just sit back and wait for a new project.
Deployed models require monitoring. You need to monitor the performance of the deployed model to ensure it continues to do its job with the quality the business requires. We all know about some adverse effects that can occur over time: model degradation is one of the most common.
Another good practice would be to collect samples that were mishandled by the model to determine the root cause and reasons why this happened and use them to retrain the model to be more resilient to such samples. Ongoing research like this will help you better understand possible edge cases and contingencies your current model isn’t prepared for.
Some Machine Learning Trends
1. Machine Learning Operations Management (MLOps)
Machine Learning Operationalization Management, or MLOps, is the process of creating machine learning software solutions that emphasize dependability and efficiency. MLOps’ main goal is to accelerate the creation of machine learning solutions that will be more valuable to your business.
In addition, MLOps aids in team collaboration, scalability, constructing the right ML pipelines, and managing sensitive data at scale. Closing communication gaps, enhancing transparency, and enabling improved scalability can all help with this.
2. Hyperautomation
Hyperautomation refers to how a business can automate many business processes. Thanks to ML and AI technologies, in 2022, companies can automate numerous repetitive processes involving vast volumes of data and information. This move by companies has prompted a desire to increase all methods’ speed, accuracy, and reliability. It was also stimulated by the desire to reduce how much you depend on human labor.
In addition to ML and AI, robotic process automation is another essential technology for developing hyper-automation.
3. Codeless Artificial Intelligence and Machine Learning
Machine learning is usually set up and controlled using computer code, but this may not always be the case. All this is possible thanks to codeless machine learning, which is a programming method where the ML application does not have to go through tedious and time-consuming processes such as;
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Collection of new data
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Designing algorithms
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Tuning
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Modeling
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Preprocessing
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Retraining
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Development
With this codeless ML, system software development no longer requires an expert. Drag and drop inputs are used during the machine learning process without code because it simplifies the process in various ways;
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Evaluation of results
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Drag and drop training data
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Generating a forecast report
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Start with user behavior data
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Asking questions in plain English
By leveraging code-free ML, developers can now easily access machine learning applications.
4. TinyML
This is a relatively new approach to developing ML and AI models running on hardware-limited gadgets, such as microcontrollers that power refrigerators, electricity meters, and vehicles. Adopting TinyML is a better strategy because it allows algorithms to be processed faster. After all, data does not have to travel back and forth from the server. This is especially important for larger servers so that the process is less time-consuming.
Running Tiny ML on IoT edge gadgets has several benefits, including;
Privacy when using TinyML is enhanced because the calculations are wholly performed locally. There is lower power consumption, bandwidth, and latency as there is no need to send data to a data center.
5. Greater Focus on Data Security and Regulations
Cybersecurity is a popular industry that uses machine learning, and some of the applications include identifying cyber threats, fighting cybercrime, and improving current anti-virus software, among others. Since data is today’s new currency, you need to emphasize increasing the amount of data you collect. This is especially important as ML and AI increase the amount of data processed, which also brings additional risks.
An example of how ML is used to enhance cyber security is the development of Smart Antivirus Software that can detect any malware or virus.
Conclusion
Machine learning systems are becoming more critical daily as various applications’ data grows rapidly. Machine learning technology is at the heart of intelligent devices, home appliances, and online services. The success of machine learning can be further extended to safety-critical systems, data management, and high-performance computing, which have great potential for application domains.
