How to Create an End-to-end Machine Learning Workflow

The machine learning workflow is one of the most important parts of any machine learning project. It is also the most crucial part because it determines the success rate of the entire model. The goal here is to give you an understanding of how the machine learning pipeline works, and how data pre-processing, different machine learning techniques, and project management are all intertwined.

Moreover, the sole purpose of any reliable and efficient ML workflow is to automate the whole pipeline. This way we don't need to worry about time-consuming manual work that usually takes up most of our time when dealing with big datasets. With this approach, we only need to focus on the big picture and not on all these tiny pieces.

An important aspect to always bear in mind when building your ML workflow is to start building small and consolidate a flexible workflow that would allow you to scale up to a production-grade solution. When developing a machine learning process, you must first describe the project and then identify an approach that works.

In this piece, we’ll be introducing the stages of a Machine Learning workflow from the very ground up to a production-ready scalable solution. And along the way, we’ll provide a general overview of some of the best solutions and products that enable efficient workflow building.

Evaluate Your Problem

Take some time to think about the problem you're attempting to solve before you start thinking about how to tackle it with ML. Usually, a set of three fundamental questions are to be answered before proving the need for ML:

Have you clearly stated and defined your problem?

When using ML to discover patterns in data, there are several ways that may be used. It's critical to specify the information you're looking to extract from the model and why you need it.

A general schema for problem definitions can be well-suited in this context, for example:

1. Make a formal and informal description of the problem,
2. Create a list of assumptions about the problem and its phrasing,
3. Think of a set of problems that share some similarities with your problem and see what was the adopted solution in each case.

Is ML the right fit for your context?

You should only consider utilizing ML for your problem if you have a large quantity of data to train your model on. There are no hard and fast rules on how much data is enough. Each feature added to your model increases the number of instances required to successfully train the model.

And one important question to ask yourself when implementing an ML pipeline is whether you have the right data infrastructure to gain the most value out of investing in Data Science.

Finally, how can you measure your model’s success?

One of the most difficult aspects of developing an ML model is determining when the model development process is complete. It's tempting to keep tweaking the model indefinitely, deriving little increases in accuracy. Before you begin the process, you need to understand what success entails. Consider the amount of precision that is appropriate for your needs. Consider the implications of the appropriate amount of inaccuracy.

Data Engineering

Data preprocessing is the most critical step in a machine learning workflow. It is done before training and testing the model. Typically, data engineers integrate data from various sources, while data integrators combine multiple sources of data into one.

Data engineers are responsible for both of these tasks. Data preprocessing is the process of converting messy, inconsistent data into a format that is more easily explored and analyzed. It involves cleaning up missing values, finding patterns in data sets, and transforming variables to be more meaningful to users.

Typically, a Data Engineering pipeline is required to supply training and testing datasets for machine learning algorithms.

• Data Ingestion: Data collection utilizing multiple frameworks and formats such as Spark, HDFS, CSV, and so on.
• Data Wrangling: The process of re-formatting certain characteristics and addressing data issues such as .
• Data Labeling: The process of the Data Engineering pipeline, in which each data point is assigned to a certain category.
• Data Segregation: for use in the basic machine learning steps of creating the ML model.

Model Engineering

Model engineering involves the design and development of models for a particular purpose. This includes defining a specific problem and evaluating a number of possible solutions. Model engineering is the core part of the whole workflow and requires the data science team to create the best fit model by specifying new operations and methods or by applying known ML techniques.

In a classical pipeline, ML engineering involves:

• Model training,
• Model evaluation,
• Model testing,
• Model packaging and deployment.

However, if we were to detail in each section we would find other important steps that strengthen the reliability of our workflow, like, for example:

ML Tracking and Debugging

Model debugging investigates ML response functions and decision boundaries to find and rectify errors in accuracy, fairness, security, and other aspects of ML systems.

A large portion of the ML training code is executed on clusters or in the cloud. When running a distributed training task on a cluster, the primary approach to tracking progress is to instruct your code to produce logs and put them in a centralized location for analysis.

There are great tools that can help you monitor and keep track of your ML engineering:

• : Tool that helps you track and monitor all the stages of your ML training and testing. It also helps you compare different runs and versions of your model so that you can always pick the optimal version.
• : Comet, like Neptune, is intended to help data scientists track and manage their research.
• : Combines data lineage with enterprise-engineered end-to-end pipelines on Kubernetes.
• : An all-in-one machine learning platform for developing and deploying AI models at scale.

Model Deployment

Deployment of models is a process that takes the trained models and loads them into production for use, sometimes focusing on security or privacy concerns. This has become a popular topic lately due to recent .

The deployment also includes model packaging, which is just what it sounds like - taking information from an ML model and repackaging it for easy . Model packaging can include any extra flags, metadata, or other information needed to ensure compatibility with downstream systems.

Model serving is often used in conjunction with deployment to ensure that models are accessible by applications on demand. Model performance monitoring allows us to examine how well our ML models are performing over time while interacting with various data sets in production environments. Model serving can be operated in different forms:

Web-Based Model Serving

A web-based framework is a code library that provides common principles for constructing dependable, scalable, and maintainable online applications, making web development faster, more efficient, and easier.

The functionality of common web-based frameworks includes:

• Input handling and validation: The objective is to evaluate the data before saving the form if it requires input.
• URL Routing: The routing method is used to map the URL straight to the code that finally builds the web pages.
• Database connection: Object Relational Mapping allows for persistent data modification and database connection setting (ORM).
• Web Security and Protection: Cross-site scripting (XSS), cross-site request forgery (CSRF), SQL Injection, and other harmful threats are all protected by frameworks.

4 Awesome Web-Based Frameworks for Model Serving

End-to-end MLOps platforms

MLOps enables you and your company to grow capacity in a production environment to produce faster and more efficient outcomes, producing considerable value.

Data Science and Machine Learning initiatives fail because they lack a proper framework and architecture to support model construction, deployment, governance, and monitoring. To succeed, data science team members such as Data Scientists, DevOps Engineers, Data Analysts, Data Architects, and Data Engineers must collaborate to productize ML algorithms and deploy them at scale.

The most important functionalities that come right out of the bow when going for an MLOps platform are:

• Data pipeline management and model versioning,
• Model and experiment versioning,
• Hyperparameter tuning and optimization,
• Model monitoring in a production setting.

Of course, there are a lot of companies that propose MLOps solutions, but the most reliable ones that really provide a scalable environment are:

Model performance monitoring is also important for any organization deploying AI models because it provides insights into how well-deployed algorithms are doing in meeting your business objectives and KPIs.

It’s important that organizations maintain a balance between model deployment, performance monitoring, and model management activities. If you let your deployed algorithms operate without any supervision then you will not be able to determine how well they are performing in meeting

Final Thoughts

Machine Learning is a very complex and vast topic. It can be difficult to decide which ML tool to use for your project.

In this article, I tried to provide you with a variety of tools and their features that might help you decide on the right ML workflow for your project. We hope this article helped you decide on the right ML packings tool.

However, before starting any serious project in ML, I highly encourage you to consult professionals that will hopefully carry you through the pros and cons of implementing a scalable ML workflow in your particular context and production scenario.

With all that being said, below are some interesting references that you might want to consult for further acquaintance with the topic.

• ,
• An insightful blog from Google on ,
• .