Machine Learning Development Life Cycle in 2022

Top stages in machine learning life cycle. How to complete a machine learning project in 2022

Machine learning (ML) is the key to the success of many businesses in this data-driven world. This model is used to allow machines to perform tasks without explicit instructions from humans. Machine learning technologies can “learn” on their own by analyzing data and identifying patterns.

The machine learning industry covers numerous directions and areas, and knowing and understanding the implementation life cycle of an ML project will help you properly allocate existing resources and understand where you are in the development of machine learning.

In this article, we talk about what the life cycle of an ML model is, define the stages of successful implementation of a machine learning project, and share the best tools that will definitely help with this.

Keep reading to find out everything you need to know about the machine learning development life cycle.

What is Machine Learning Life Cycle

In general, the life cycle of a machine learning model typically includes three fundamental steps: data preparation, model development, and deployment. All of these are equally important for the implementation of high-quality and efficient models that will definitely be profitable and beneficial for the business. And it stands for the cycle since, when done right, the information learned from the existing model will guide and determine the next model to be deployed.

The life cycle of ML is the acquisition of knowledge through data. It is cyclical and describes a three-step process used by data scientists and engineers to build, train, and serve models. And all of this is the result of a process called the machine learning lifecycle. It is a system that uses data as input, being able to learn and improve using algorithms without being programmed to do so.

And now let’s find out what the implementation of the machine learning life cycle looks like in 2022.

Key Steps of Machine Learning Project

First and foremost, implementing a machine learning model into a production environment and workflow is a complex and challenging task. We want to note right away that it is a misconception to assume that with the data and computing resources needed to train the model, the implementation of an ML solution is very simple.

Thus, the life cycle of machine learning development is quite extensive and laborious. It covers three key processes — pipeline development, training, inference — and consists of several stages.

Stage 1. Data

The quality of the end machine learning model depends largely on the quantity, and most critically, on the quality of the input data. And since this stage is vital to the final results, the data in the machine learning cycle receives a lot of attention.

So, let’s look at the key data-based phases.

  1. Data collection. First, you need to collect the maximum amount of any input data. Keep in mind that the annotation step will filter out the raw data, but having a large array of available data to add as needed will help deal with performance issues.
  2. Definition of annotation principle. This, perhaps, is precisely the stage on which the further ML life cycle largely depends. An improperly developed concept of annotation inevitably leads to a complication in the model training process. Conversely, introducing important parameters and attributes can give vital metadata that is responsible for developing high-quality training datasets which models can learn from.
  3. Data annotation. This is complex, monotonous, and time-consuming work that requires performing long repetitive processes. Services in this area are in demand and popular. Individual specialists tend to make a large number of mistakes, and companies specializing in annotations reduce the likelihood of error to a minimum. The main thing is a correctly determined annotation scheme.
  4. Refinement of the dataset and annotations. Improving the efficiency of the model is very resource intensive and consists of detailed sample analysis, dataset rebalancing, and updating annotations and schemas.

Hence, in general, at the Data stage within the machine learning life cycle, it is necessary to conduct the data preparation, search for the source of raw data, build the correct annotation schema, have annotators to find edge cases, and collect more data to fix biases and imbalances.

Stage 2. Model

It will sound surprising, but this stage of the cycle, which is directly related to the model, requires the least amount of effort and time.

And now let’s explore the main model-based phases.

  1. Examining existing pre-trained models. The main result of the successful completion of this phase is the reuse of the maximum amount of existing resources to get a great start for the production of models. As a rule, you will not develop from scratch but will take as a basis an already created model that has already been trained for tasks similar to yours.
  2. Creation of a training cycle. One must be prepared for the fact that the data one way or another will not be completely the same as those used for the initial preparation of the model. When setting up the training pipeline, it is necessary to take into account the input resolution for data sets and the parameters of the objects. Furthermore, it is also necessary to transform the model output structure.
  3. Experiment monitoring. The result is that you will train a large number of various models. And this means that vigilant and constant tracking of different versions of the model, parameters, and insights is critical.

Thus, in general, at the Model stage within the machine learning life cycle, it is necessary to download model weights and training pipeline from GitHub, set up model and training pipeline, train and track model and versions.

Stage 3. Evaluation

After the successful completion of the Data and Model phases, the next task is to analyze and track whether this model is able to work with new data.

And now let’s find out the crucial aspects of evaluating a machine learning model.

  1. Visualization of output data. The first thing to do when you have a ready-made trained model is to instantly test it on different samples and explore the results. Such an approach will allow you to determine the presence of failures and errors in the training/evaluation pipeline, and only after that to evaluate the whole test set. In addition, it is such a way to identify obvious bugs and inconsistencies in the model.
  2. Selecting the proper metric. The use of metrics allows you to compare and analyze the productivity of models. To be sure of the correct selection of models for your goal, it is necessary to define indicators according to the desired final results. In addition, keep in mind that metrics must be updated in case there are additional critical aspects to track.
  3. Exploring cases of failures and errors. All about your model is related to the data it was trained on. This means that if it operates worse than planned, you can resort to data. In such a situation, it can help to study the scenarios in which the model functioned properly, but it is even more important to analyze all the errors, bugs, and failures. By examining as many false positives and false negatives as possible, it will be possible to predict behavior and identify patterns. And this, in turn, will certainly lead to a performance boost.
  4. Generation of solutions. Detecting errors and bugs is the first task when it comes to finding opportunities to enhance performance and refine the model. Typically, training data is added that is analogous to where the failure occurred. In addition, it can also be a modification of the pre-processing or post-processing or reformation of annotations. However, whatever it is, the most important thing to eliminate failures is to identify them correctly.

Hence, the Evaluation phase within the machine learning life cycle includes visualization of model outputs, refinement and calculation of the evaluation metric, search for individual failure cases, and determination of the cause of errors (data or model).

Stage 4. Production

Now, you have a model that functions as expected on your evaluation parameters without any critical failures and crashes. But that’s not all. Let’s find out what you should do next.

  1. Monitoring. Test the deployment to get assurances that the model runs well on the test data in relation to your evaluation indicators and inference rate.
  2. Evaluation of new data. Applying a model in a production environment involves constantly passing new data through a model that it has not been tested on before. This is where evaluation and case studies need to be done to make sure the model works properly with the new data.
  3. Continued understanding of the model. It may be that failures and errors will be serious, and the process of identifying them will be lengthy. Therefore, you need to permanently test the model to look for edge cases and regularities that can lead to potential crashes.
  4. Expansion of opportunities. Regardless of the success of the work, it may be that the model does not bring the expected financial results. There are a huge number of options available to improve and refine the existing model — it can be like adding new classes, creating new data streams, boosting productivity, and more. To perfect the whole system, you should rerun the machine learning life cycle. This will allow you to be sure that all functionality works perfectly.

Hence, at the Production stage within the machine learning life cycle, model monitoring is carried out, the model is evaluated on new data, deviations in the model are detected, and additional functionality is added.

So, this is an iterative process that involves collecting new data that reflects bugs and updates in production, continuing the cycle until the model performs well, and finally getting a model that runs well on the test data.

Thus, now we know what four stages a machine learning model consists of. In the same order, we will consider the tools that will help to implement all this.

We will need ready-made libraries and frameworks for machine learning. In any modern programming language, such tools are already written, so take any that you like (or know). We will cover everything related to Python, which has been steadily growing in popularity over the past few years due to its flexibility, good readability, and ease of learning. The machine learning libraries written for it are the most popular at the moment.

Tools for Collecting, Processing, and Visualizing Data

Here we collect data from various sites and create a dataset, which we then use to train the algorithm. Once the data preparation and data collection phases are finished, it needs to be processed to get rid of errors, noise, and inconsistencies. This is very important since the accuracy of the results of the algorithm will depend on the correctness of the data.

Visualization will help determine the linearity of the data structure, significant features, and anomalies. For these tasks, you can use ready-made web services, or write your own code.

After we have cleaned our dataset, we need to divide it as follows: 80% for training the model, and 20% for checking and testing it.

  • Pandas: a library for data processing and analysis. It is built on top of NumPy, which we’ll talk about in a bit. These are our groupings, sortings, extractions, and transformations. To work with CSV, JSON, and TSV files, Pandas turns them into a DataFrame data structure with rows and columns.
  • Tableau, Power BI, Google Data Studio: simple online visualization without code. Tools for business intelligence and users without much programming skills. The main thing here is visualization. We load the dataset and use the built-in functions, filters, and real-time analytics. These services quickly collect insights and present them in a visual form. Tableau, Power BI, and Google Data Studio have both paid subscriptions and free versions.
  • Matplotlib: 2D plotting library. Matplotlib, in conjunction with the seaborn, ggplot, and HoloViews libraries, allows you to build a variety of plots: histograms, scatter plots, pie and polar plots, and many others.

Interactive Development Environments

These tools are often used for Data Science and Machine Learning. The web environment allows developers to test small parts of the code on the fly, test functionality and various hypotheses. However, if desired, you can put the whole project in it.

  • Jupyter Notebook: interactive simulation. In addition to Python, Jupyter Notebook supports over 40 programming languages. It is convenient to experiment with new ideas, write documentation and create analytical reports. It resembles an IDE, but in terms of functionality, although it is quite wide, it does not reach it. Among the tools for machine learning, and in general Data Science, Jupyter is good due to its fast analysis, modeling, and data visualization.
  • Kaggle: Data Science community. Kaggle also provides an interactive development environment. The difference is that you are just one click away from the whole Data Science and Machine Learning community. Here you can find ready-made datasets, models, and even program code for solving various problems.

Frameworks and Libraries for General Machine Learning

Model training falls into two broad types of machine learning: supervised machine learning and unsupervised. In the first case, we mark the dataset, explaining to the machine learning algorithm where the correct answer is and where it is not. So the data can be represented by the “element-category” correspondence table. In the second case, the algorithm itself is forced to look for signs and patterns, since in the dataset we give data without clarifying information. The dataset is represented by a continuous stream of data of the required type: text, pictures, etc.

Each category uses its own machine learning algorithms (clustering, classification, regression, association). The optimal choice depends on the task, the complexity of the model, the size, and the type of data.

Keep in mind that training and debugging your own model is a long and expensive process. It is very likely that someone has already solved a similar problem and prepared a model. Therefore, it is worth searching, using the implemented architecture, and retraining the algorithm for your data. However, the more your problem differs from the one that the finished model solves, the more you need to retrain it and change the parameters.

  • NumPy: ready-made computational algorithms and linear algebra for machine learning. Data in machine learning is represented by numeric arrays. Even if we are working with pictures or natural speech, they must be converted to numeric arrays. NumPy has everything you need to do this.
  • NLTK: breaking natural language apart. One of the leading natural language processing tools. Similar to how NumPy simplifies linear algebra, NLTK simplifies text parsing, sentiment analysis, sentence structure, and everything related to it.
  • Scikit-learn: a simple library with tons of examples on the official site, making it good for beginners. But this does not mean that it is not suitable for serious projects. It has all the basic functions such as clustering, classification, and regression.

Deep Learning and Neural Network Modeling Frameworks

The mentioned machine learning tools allow us to obtain a model capable of performing relatively simple tasks. Next, we will talk about the deep learning of neural networks. Here, to make a more complex decision, the algorithm takes into account various factors by passing incoming data through many layers of neurons. Of course, this requires more processing power and training data.

There are two leaders in the market of deep learning tools — PyTorch and TensorFlow frameworks. Previously, there were significant differences between them. But the distinctions are gradually blurred as they adopt each other’s best features.

  • PyTorch: the king of research. Easy to learn and understand, friendly with the rest of the Python ecosystem. Debugging takes place on an intuitive level: we put a breakpoint anywhere in the code and look at the values ​​of the variables. Data scientists and researchers also like dynamic graphs, thanks to which you can change the behavior of the machine learning model on the fly. All this allows you to test various theories and approaches on small datasets without long delays.
  • TensorFlow: the king of production. The main difference between these deep learning tools is in the approach. If PyTorch reigns in the academic environment, then TensorFlow is initially focused on the market. It is tailored to solve business problems: to pass huge amounts of data through itself with good performance and with the ability to seamlessly use machine learning models on mobile devices.


Bringing machine learning projects to a successful conclusion is more difficult than it might seem at first glance. By taking into account the full life cycle of techniques in machine learning, we can understand the potential pitfalls and understand what is required to not only put a model into production but also to continue it. While this task may seem daunting, the tools to help manage the machine learning lifecycle are becoming more mature.

We at Brights have been implementing machine learning models for many years, using advanced experience and world best practices. We provide industry-leading services and help you take your project to the next level.

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