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How to Train and Deploy Machine Learning Models

How to train a machine learning model

The process of creating a Machine Learning (ML) model includes training an ML algorithm with relevant data, so the model can make predictions on similar kind of unseen data. The prediction may be a classification (assigning labels) or a regression (a real value). The goal of a machine learning project is to achieve the final model that predicts accurately.

The situation of solving problems with machine learning is currently being approached in an ad-hoc manner. As result companies are missing out on utilising the complete benefits of machine learning, as they are solving a limited number of problems with time-intensive methods. These techniques need to change as solving issues with machine learning should follow a standardised approach, with processes that can be adapted quickly and deployed as a constant to a problem change.

Having a single unified platform for all your machine learning needs, where you can quickly create templatized projects through guided workflows, really takes away the whole pain and apprehension of adapting to new technology.

In this blog, we’ll lay out the approach to building machine learning models in a structured way, along with the steps on how to train a machine learning model with

Related: Download the checklist that lays out everything you should consider  before implementing a machine learning project or workflow →

What is a Machine Learning model?

A Machine Learning (ML) model is a mathematical model, most commonly used in data science projects. It is a piece of code made smarter by a data scientist through training with data. So, the data you give to the model is most important, as the trained model you get is completely dependent on the kind of data you’ve trained it with. Predictions are generated using patterns extracted from the input data. Therefore, as the model finds predictions through patterns in your data, it can provide false or wrong predictions, in case of being fed with the wrong data.

How do Machine Learning models train?

Machine Learning model refers to the model artifact created by a training process. The process of how to train a Machine Learning model involves providing an ML algorithm (or learning algorithm) with training data. The training data needs to contain the correct answer, which is known as the target attribute or target.

Patterns in the training data are identified by the learning algorithm, which maps the input data attributes to the target. The target is the answer that you wish to predict, and it produces an ML model that captures these patterns. ML models can be used to get predictions on new data for which the target is not known.

Modeling is not dependent on the previous steps of the machine learning process. There are standardized inputs that can handle the prediction problem without requiring to rewrite all the codes. If the business needs change, new labels can be generated, building corresponding features, and those can be fed into the model.

What is Data collection?

The kind of project dictates the process of gathering data, for example, if it’s an ML project using real-time data then we can build an IoT system, which uses different sensor data. Various sources can be used for the collection of data, like a file, database, sensor, and other such sources. The collected data, however, cannot be used directly to perform the analysis process, since there might be a lot of missing and unorganized data or extremely large values. Data preparation helps in solving this problem.

What is Data Labeling in Machine Learning models?

A group of samples tagged with one or more labels is known as labeled data. A set of unlabeled data is typically taken by labeling, and each piece of unlabeled data is embedded with informative tags.

An important step in enhancing the computer vision model is to set a training algorithm and validate them with high-quality training data. This would require interpreting images by manually defining and making text-based descriptions of regions in images. Image annotation use cases include computer vision for autonomous vehicles or identifying sensitive content on an online media platform.

Data labeling makes the machine learning algorithm understand data. For example, if there’s an image of a cat, a Machine Learning algorithm will only understand it if the image is labeled as a cat. Therefore, the model is trained based on that.

What is Machine Learning model retraining?

How the Machine Learning models are trained is by mapping a set of input features and output targets. This mapping is generally achieved by optimizing some cost function to reduce prediction error. Once the model is optimal, it’s released to generate accurate predictions on future data. Ideally, we hope that these models predict future instances as well or accurately as the data used during the training process. But this assumption will generally not hold. For a model to predict correctly, the data that is being used to make predictions must have a similar distribution to the data which was used for the model training. Since we expect the data to change, model deployment should be treated as a continuous process. Models need to be retrained by practitioners if the data distribution has deviated significantly from the original training set.

How to train a Machine Learning model from labeling to model monitoring in provides end-to-end Machine Learning workflow in a single unified platform by building and deploying ML models quickly on unstructured data. The Machine Learning model training corresponds with an ML algorithm, with selected featureset training data. The learning process is through features and patterns in the training data, which map the input data attributes to the target or the value that is required to predict. The patterns are captured by a successfully trained ML model, which can then be deployed to perform predictions.

To demonstrate the complete flow of how to train a machine learning model from labeling to modeling, takes a Named-entity recognition (NER) use case to extract keywords and relevant entities from resume text.

Related: Download the checklist that lays out everything you should consider  before implementing a machine learning project or workflow →

1. Creating a Project provides multiple templates in Computer Vision and Natural Language Processing (NLP) for a guided machine learning workflow which can be chosen depending on the use case being implemented. For this project, we chose the NER template, which deals with the extraction of specific keywords and phrases from any text.

Creating a project

2. Designing the Dataset Schema then designed the schema of the dataset through a guided workflow. We provided the dataset name, description and designed our schema as per the requirements of our project. We provided class names as ‘name’, ‘college name’, ‘skills’ and ‘designation’. These are the entities that we wanted to extract from the resume text.

3. Collecting Data

The data is uploaded using the ‘CSV upload’ feature of Skyl. You can easily see the format in which the CSV file needs to be uploaded by downloading the schema from the button provided on the top right-hand side of the drag and drop window.

Collecting data 1

Collecting data 2

4. Labelling Data provides Collaborative Form-based and Mobile based methods for data labeling. It’s an easy 4-step job creation process where you can configure and assign collaborators to your job who will do the labeling for your dataset. For our project, we created a collaboration job and assigned some of my colleagues to it, who received an email to take them to the web portal, where they could start doing the labeling.

Labeling data

Labeling data 2

Labeling data 3

Labeling data 4

Labeling data 5

Labeling data 6

5. Visualization and Data Analysis provides various kinds of visualization techniques for your dataset depending upon what kind of problem you are trying to solve. For the NER template, you can see analysis around your free text as well as column-wise statistics of each column depending upon the datatype.

Visualization and data analysis

Visualization and data analysis 2

Visualization and data analysis 3

6. Creating the Featureset

A featureset is a subset of your dataset which is used as the input to your machine learning algorithm. also provides a summary of the featureset you are creating so you can analyze if your featureset is properly balanced and there are no biases or bad data. After all, your machine learning model is only as good as the data it’s being fed.

Creating the featureset

7. Model Training

The model training was initiated using’s suggested algorithms and parameters. allows you to tune parameters like batch size, number of epochs, learning rate, etc. as well as suggests the best possible optimized training parameters for your model training.

Model training

Model training 2-1

8. Model Deployment

As soon as the training finished, a model was created which was listed under ‘Model Deployment’. The model achieved an accuracy of 91%. Training reports for the model were also generated for metrics like loss, accuracy, recall, precision, etc. allows one-click model deployment for your models, thus eliminating all the work required for setting up a model deployment pipeline.

Model deployment

9. Inference API

Inference APIs were generated once the model was deployed which could be hooked in any application and can be used for predictions.’s inference API is easy to use and available in all major programming languages for seamless integration.

Inference API

10. Model Monitoring

Model monitoring is one of a kind features of and is helpful to keep track of how your model is performing and to maybe analyze if you need to re-train your model if you see the average confidence score going down way too much. On the dashboard, you can see parameters like requests per minute, average confidence and execution time and day-wise, hour-wise statistics for your inference requests and execution time.

Model monitoring

Model monitoring 2

As soon as the model is deployed for production, a machine learning model’s predictive performance usually declines. Therefore, practitioners must be ready for reduced performance by setting ML specific monitoring solutions and workflows, which enable retraining. It functions in a similar way to website templates, i.e. we don’t have to start from scratch every time a website is built, we can use the existing template and fill in the new details. The model can be improved further for accuracy by tuning the hyper-parameters and looking at the confusion matrix, which increases the number of true positives and true negatives. helps businesses train Machine Learning models for improved speed and data efficiency. Try out our risk-free trial to train your Machine Learning model.