Machine learning deployment is a multi-step process. It includes selecting a machine learning model, training it for a particular job, validating it using test data, and then deploying and monitoring the ML model in production. This article describes what machine learning is and the different steps involved in its deployment. Let’s dig in!
Introduction to Machine Learning
Machine learning involves systems that can learn and improve without explicit information. These systems continually learn from data to carry out certain tasks or functions. The main objective of machine learning is to create algorithms that automatically enable a system to collect data and utilize the data for learning more. Systems are expected to identify patterns in the data gathered and make use of them for their decision-making.
In other words, machine learning is preparing systems to think and behave like humans, reflect human-like intelligence, and give them a human brain. In today’s world, there exist machine learning models that can do tasks like:
- Identifying and sorting out spam from actual emails, for example, in Gmail
- Rectifying spellings or grammatical mistakes, as seen in autocorrect
Machine learning and artificial intelligence solutions have helped the world to see design systems that can show uncanny human-like thinking, which performs functions including:
- Object and image recognition
- Identifying fake news
- Comprehending written or verbal words
- Chatbots that communicate with humans, just like humans
- Self-driven automated cars
How Machine Learning Solutions Help Organizations
In today’s digital world, we witness several machine learning solutions that enable organizations to enhance business efficiency and productivity. Both machine learning and artificial intelligence offer numerous benefits to organizations. When organizations integrate ML/AI into their core business processes, they leverage benefits including the ability to identify patterns and correlations, enhance customer segmentation and targeting, and boost business revenue, growth, and market position.
Harnessing the power of machine learning has empowered organizations to:
- Easily adapt to constantly changing market situations
- Enhance business processes
- Gain a better understanding of the business and customer requirements.
Machine learning is quickly emerging as a core technology that can be implemented across all business sectors to address complex business problems. Machine learning solutions help in enhancing the scalability and effectiveness of an organization.
Even with all the complications associated with proper deployment of machine learning, organizations are ready to incorporate this time-consuming and expensive process because it offers desired benefits over any traditional analytical method.
Therefore, many organizations look to hire machine learning developers and engineers to build and deploy machine learning and artificial intelligence solutions for better decision-making and growth.
The 7-Step Machine Learning Deployment Journey
The process of imparting human-like intelligence to systems may seem daunting and impossible. However, it is actually not that difficult. The entire process involves the following 7 basic steps. If you’re new to the technicalities of machine learning, you can always approach a machine learning development team for a systematic deployment process.
As mentioned earlier, machines initially learn from the data that is fed into them. It is important to collect reliable data so that your machine learning model can identify accurate patterns. The quality of the data fed to the system will determine how perfect your model is. If you have inaccurate or obsolete data, you will generate wrong outcomes or irrelevant predictions.
Therefore, you must use data from a legit source to obtain better outcomes from the model. Good quality data is relevant, has fewer missing and repeated values, and has a decent representation of different categories present.
When you have your data ready, you need to prepare it. You can prepare your data by:
- Combining all the data collected and randomizing it. This helps ensure that data is distributed evenly and the organization does not have an impact on the learning process.
- Cleaning the data to eliminate all unwanted data, duplicate values, missing values, columns, and rows, data type conversion, etc. You may also need to reorganize the dataset and modify the rows and columns or index of rows and columns.
- Visualize the data to better comprehend how it is structured and understand the relationship between different variables and classes available.
- Segmenting the cleaned data into two sets, namely a training set and a testing set. Your model learns from the training set and the testing set is used to verify the accuracy of your model post-training.
Selecting a Model
A machine learning model determines the outcome you achieve after running an ML algorithm on the gathered data. Therefore, you must select a model that is relevant to the function at hand.
Over the years, engineers and scientists created different models suited for various tasks such as image recognition, speech recognition, prediction, etc. Besides this, you also need to see if your ML model is ideal for numerical or categorical data and finalize accordingly.
Training the ML Model
Training is an essential step in machine learning model deployment. In training, you send the prepared data to your machine learning model to identify patterns and make predictions. It helps the model to learn from the data so that it can achieve the task set. With proper training, the model gets better at making predictions over time.
Assessing the Model
After the model training, you need to check how it’s performing. You can test the performance of the model based on earlier unseen data. The unseen data utilized is the testing set that you divide data into earlier. If you test data on the same data that is used for training, you will get an inaccurate measure. It’s because the model is already familiar with the data, and identifies the same patterns in it, as it did earlier. Hence, you get disproportionately greater accuracy.
When used on testing data, you obtain an exact measure of how your ML model will perform and what its speed will be.
After building and assessing your ML model, look for ways to enhance its accuracy. You can do this by tuning the parameters available in your model. The model parameters are the variables that are generally determined by the developers. At a specific value of the parameter, the accuracy level will be higher. Hence, parameter tuning involves finding these values.
Making Predictions Accurately
Lastly, you can deploy and use your machine learning model on unseen data to make accurate predictions.
For effective deployment of machine learning solutions, you must start with a problem first and then consider feasible models that solve it. Next, you need to acquire data, and after cleaning and segmenting the data, the machine learning model can be trained and validated using an ML framework. However, all frameworks are not the same. Therefore, you must select and use the right framework based on your model and experience. The selected framework is further used for deploying machine learning solutions. Finally, with proper auditing, the solution functions in the real world using live data.
To ensure that your machine learning solution is deployed properly, you can always count upon a professional machine learning development team.