Published on

January 4, 2024

Machine Learning

Deploying ML Models: A Guide

Navigate the complexities of deploying ML models with our in-depth guide. Streamline your ML model deployment process and overcome challenges with Akkio.
Julia Dunlea
VP of Marketing
Machine Learning

Harnessing the predictive prowess of machine learning (ML) or deep learning (DL) models is a pivotal step toward capitalizing on data-driven decisions. The process of building and training ML models is just the beginning; the real value lies in deploying these models into real-world environments. 

Successful deployment is the cornerstone of an ML project's success, as it determines the impact the model will have on business operations and user experience.

The journey from conceptualizing a powerful ML model to deploying it into production is a challenging endeavor. It demands a comprehensive understanding of the model's intricacies, its interaction with data, and the nuances of integration with existing systems. 

This article will navigate through the various stages of ML model deployment, underscore its importance, and delve into the obstacles that might come up. Even better, we’ll introduce a leading AI platform designed to streamline the process of ML model deployment – Akkio!

What does it mean to deploy an ML model?

The deployment phase of an artificial intelligence (AI) model marks a pivotal moment in its lifecycle, transforming it from a theoretical concept into a practical and powerful tool.

Deployment, also known as machine learning operations (MLOps), means making the ML model available and operational for use in real-world applications. Just like how you launch a fully developed app or website for people to access, MLOps puts your trained ML model into action so that it can perform its intended task automatically.

Deployment involves seamlessly integrating a trained ML model into a business's automated systems. This allows the model to perform a wide range of tasks, from generating text for end-users to automatically directing user inquiries or even providing intelligent suggestions based on monitored data. This concluding phase follows a series of preceding stages: data collection, data cleaning, model training, and model validation.

Deployment is a fundamentally different process from training the model. Training an ML model is a resource-intensive process, requiring significant technical input, data, and often considerable financial investment. For instance, training GPT4 is allegedly estimated to have cost around $50M in cloud computing alone, not including additional costs for data sourcing, salaries, and other expenses. 

In contrast, deploying an ML model is typically a much more cost-effective process with a low and scalable variable cost per user. For example, deploying GPT4 in ChatGPT is approximated to cost just a few cents per query and is done completely automatically.

Nonetheless, MLOps is not a simple task. It requires careful planning, strategy, and ongoing monitoring to ensure the model performs as expected when exposed to new environments. This phenomenon, known as model drift, can occur when a model's performance deteriorates due to changes in the data it encounters. 

If a new feature is implemented on a website, for instance, an AI model analyzing customer interactions may perform poorly on this new task due to its lack of practical experience. By continuously testing the model on newly gathered data post-deployment, potential issues can be identified and resolved quickly, ensuring the model continues to perform optimally.

The importance of deploying ML models

While some models can be interpreted without deployment, many require specific settings to function optimally, such as being part of an app or an integrated pipeline. This can often be achieved by placing the model in an application programming interface (API) endpoint, allowing it to interact with other software development components.

The deployment of ML models enables businesses to harness the power of AI to drive business outcomes by:

  • Enhancing efficiency through the automation of repetitive tasks, which leads to significant cost savings. 
  • Improving decision-making using the model’s accurate predictions and insights.
  • Uncovering hidden patterns and trends in data, and providing valuable insights that might otherwise remain undiscovered.

Real-world applications of deployed ML models are even more diverse and span various fields and use cases. 

In marketing, for example, ML models can be used to route customer messages to the appropriate department, enhancing customer service efficiency. In finance, ML models can forecast market trends, providing valuable insights for investment decisions. Meanwhile, in the healthcare sector, ML models can aid in disease detection, improving patient outcomes. 

These examples illustrate the transformative potential of deploying ML models and the significant value they can add to various industries.

What are the stages of ML model deployment?

Deploying a machine learning model is a multi-stage process, each with its own set of tasks and challenges. These phases are sequential, with each one building upon the previous stage. 

1. Collecting, preparing, and preprocessing data

The foundation of any ML model lies in the quality and relevance of the data it is trained on. Most businesses already have vast amounts of data from sources like customer interactions, sales transactions, and social media feeds. 

The challenge lies in ensuring this data is organized, labeled, complete, and consistent. In some cases, further data collection through surveys or studies may be necessary. 

Data is then transformed or preprocessed to make it usable for an ML model. This stage also includes continuous machine learning model development, which involves gathering data to test and improve the model's performance post-deployment.

2. Building and training the model

Building the model involves selecting an appropriate algorithm based on the nature of the data and the problem being solved. An AI platform like Akkio can automatically select the most suitable algorithm. 

The model is then exposed to the training data, allowing it to learn patterns, relationships, and representations to make accurate predictions or classifications. This training process involves optimizing the model's parameters using various techniques such as gradient descent or backpropagation. Feature selection and extraction are important aspects of this phase.

3. Validating and testing the model

Model validation is crucial to ensure the model can make accurate predictions on new, unseen data.

This involves testing the model on a separate set of data, that it was never exposed to before, to evaluate its performance and results. Common metrics used for model validation include accuracy, precision, recall, and F1 score

4. Deploying the model

The deployment phase marks the transition from the development environment to the real-world application. The ML model is integrated into the production system, and it starts generating predictions or classifications based on real-time data.

5. Monitoring and iterating the model

Deploying an ML model is not a one-time process. Once a model is deployed, it needs to be monitored to ensure it continues to perform well. 

As new data comes in or when the underlying data distribution changes, the model may need to be updated or retrained. This model monitoring prevents model drift, where the model's performance deteriorates over time due to changes in the input data or the environment. 

Challenges of ML model deployment

Deploying machine learning models is a complex process fraught with stumbling blocks. According to a study, even seasoned AI practitioners face the difficulties of using unclean data and choosing the wrong ML model.

That being said, if you keep the following considerations in mind, you can guarantee a smoother proceeding of the deployment.

Choosing the right production environment

This involves deciding between server hosting (on-premises) and cloud hosting

Server hosting requires a high initial investment but offers more control, making it a preferred choice for handling sensitive data. On the other hand, cloud hosting can be more cost-effective and provides scalability and flexibility.

Factors influencing this decision include the size and nature of the data, the complexity of the model, cost considerations, and data privacy and security requirements.

Determining who is responsible for the deployment

Typically, data scientists handle data collection, cleaning, model building, and validation, while software engineers lead the deployment process. 

This necessitates close collaboration between data scientists and engineers, emphasizing detailed requirement accessibility and knowledge sharing from both sides to ensure smooth deployment.

Identifying when the model needs to be updated

Ensuring an ML model stays updated and performs optimally is crucial for its success. To achieve this, we rely on the concept of Continuous Integration and Continuous Deployment (CI/CD).

At the heart of CI/CD is the timely identification of when the model needs updates. This entails monitoring its performance and detecting any changes that might impact its effectiveness. Once changes are identified, the next step is to decide on the necessary modifications and implement them seamlessly without disrupting the production environment.

CI/CD facilitates the process by enabling the smooth integration of these modifications into the ML model. It ensures that the model is always up-to-date and functioning at its best by allowing continuous updates and deployment. This way, we can adapt to new challenges and evolving requirements effectively.

What's more, CI/CD places great emphasis on continuous testing. This involves not only evaluating the model's performance on previously used datasets but also testing it with newly collected data, acquired after the model was trained. By doing so, we can ensure the model remains reliable and accurate in real-world scenarios.

Building and deploying ML models with Akkio

Akkio is a cutting-edge ML platform aimed at revolutionizing the way businesses build and deploy ML models. From beginners to seasoned data scientists, Akkio offers a seamless and intuitive experience for all.

Akkio addresses many of the challenges associated with machine learning model deployment, providing a streamlined and seamless approach with its plethora of key features:

  • Cloud-based platform: As a cloud-based platform, Akkio doesn’t put any strain on your infrastructure. This eliminates the need for substantial initial investment associated with on-premises servers, providing auto-scaling and flexibility.
  • User-friendly interface: No more cumbersome coding or steep learning curves. Akkio is a no-code tool that provides an easy-to-use interface, simplifying the entire ML model development process. Its drag-and-drop functionality allows you to effortlessly design, customize, and connect different components of your model.
  • Easy data integration: Integrating your datasets is a breeze with Akkio's built-in connectors for various data sources like Snowflake, Big Query, Google Sheets, and more. It effortlessly handles data preparation, cleansing, and transformation, leaving you more time to focus on model optimization.
  • Customization and flexibility: Akkio enables you to tailor your models to your specific needs. With a rich library of pre-built components and the ability to incorporate your own, you have complete freedom to create personalized solutions.
  • Efficient model training: Akkio's powerful infrastructure ensures quick and efficient model training. You can experiment with various algorithms and hyperparameters without worrying about hardware limitations. This means you can iterate and fine-tune your models faster than ever before.
  • Insightful visualizations and data exploration: Akkio provides intuitive visualizations and comprehensive reports that help you evaluate the effectiveness of your models. It also provides Chat Explore functionality that will enable you to explore your data via chat to find actionable insights faster than traditional methods.
  • Simplified ML model deployment: Once you've trained the model, you merely need to select the endpoint where you want to deploy the model. This can be another cloud-based integration, such as Zapier, Hubspot, or others. You can also configure and deploy a production-ready API for your ML mode or choose to make it accessible via a web app.
  • Model iteration: Akkio's API allows you to iterate the model as many times as needed. You can do this in the Akkio interface, without needing to impinge on the capacity of any dedicated software engineering team you may have in your company. 

Try Akkio yourself

The above key functionalities of Akkio guarantee the continuous integration and deployment of your AI project. To illustrate, consider the following example of a web app deployment of an ML model built with Akkio.

This simple predictive model showcases the ease with which ML models can be deployed using Akkio, demonstrating the platform's potential to transform your data science and decision-making processes.

Deploy ML models with Akkio today!

Deploying machine learning models is an indispensable process that transforms theoretical models into practical tools for businesses. It empowers organizations to leverage the predictive capabilities of ML models, automate processes, and make informed, data-forward decisions. 

Akkio streamlines the process of building and deploying ML models by simplifying the complexities of ML model deployment, enabling organizations to iterate models seamlessly and unlock the full potential of their data. Akkio also offers high-performance solutions for data preparation, model selection, real-time reporting, and simplified deployment options like API integration or via web services or apps. 

With Akkio, you can navigate the intricacies of ML model deployment with ease and efficiency. Ready to get started? Try Akkio today and harness the power of your data to transform your business with intelligent, data-driven decisions.

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