How to Tackle Challenges Deploying ML Models
A handy checklist for your deploys.
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- By deploying machine learning models, other teams in your company can use them, send data to them, and get their predictions, which are in turn populated back into the company systems to increase training data quality and quantity.
- Once this process is initiated, companies will start building and deploying higher numbers of machine learning models in production. They will master robust and repeatable ways to move models from development environments into business operations systems.
Today's data scientists and developers have a much easier experience when building AI-based solutions through the availability and accessibility of data and open source machine learning frameworks. This process becomes a lot more complex, however, when they need to think about model deployment and pick the best strategy to scale up to a production-grade system.
In this article, we will introduce some common challenges of machine learning model deployment. We will also discuss the following points that may enable you to tackle some of those challenges:
- Why successful model deployment is fundamental for AI-driven companies.
- Why companies struggle with model deployment.
- How to select the right tools to succeed with model deployment.
Why Successful Model Deployment Is Fundamental for AI-Driven Companies
Machine learning model deployment is the process by which a machine learning algorithm is converted into a web service. We refer to this conversion process as operationalization: to operationalize a machine learning model means to transform it into a consumable service and embed it into an existing production environment.
Model deployment is a fundamental step of the Machine Learning Model Workflow. Through machine learning model deployment, companies can begin to take full advantage of the predictive and intelligent models they build, develop business practices based on their model results, and, therefore, transform themselves into actual AI-driven businesses.
When we think about AI, we focus our attention on key components of the machine learning workflow, such as data sources and ingestion, data pipelines, machine learning model training and testing, how to engineer new features, and which variables to use to make the models more accurate. All these steps are important; however, thinking about how we are going to consume those models and data over time is also a critical step in every machine learning pipeline. We can only begin extracting real value and business benefits from a model's predictions when it has been deployed and operationalized.
We believe that successful model deployment is fundamental for AI-driven enterprises for the following key reasons:
- Deployment of machine learning models means making models available to external customers and/or other teams and stakeholders in your company.
- By deploying models, other teams in your company can use them, send data to them, and get their predictions, which are in turn populated back into the company systems to increase training data quality and quantity.
- Once this process is initiated, companies will start building and deploying higher numbers of machine learning models in production and master robust and repeatable ways to move models from development environments into business operations systems.
Why Companies Struggle With Model Deployment
Many companies see AI-enablement effort as a technical practice. However, it is more of a business-driven initiative that starts within the company; in order to become an AI-driven company, it is important that the people who currently operate and understand the business begin to collaborate closely with those teams who are responsible for the machine learning deployment workflow.
Each step of a machine learning deployment workflow is based on specific decisions about the different tools and services that need to be used in order to make the deployment successful, from model training and registration to model deployment and monitoring:
Right from the first day of the AI application development process, machine learning teams should interact with business counterparts. It is essential to maintain constant interaction to understand the model experimentation process parallel to the model deployment and consumption steps. Most organizations struggle to unlock machine learning's potential to optimize their operational processes and get data scientists, analysts, and business teams speaking the same language.
Moreover, machine learning models must be trained on historical data. This demands the creation of a prediction data pipeline — an activity requiring multiple tasks, including data processing, feature engineering, and tuning. Each task — down to versions of libraries and handling of missing values — must be exactly duplicated from the development to the production environment. Sometimes, differences in technology used in development and in production contribute to difficulties in deploying machine learning models.
Companies can use machine learning pipelines to create and manage workflows that stitch together machine learning phases. For example, a pipeline might include data preparation, model training, model deployment, and inference/scoring phases. Each phase can encompass multiple steps, each of which can run unattended in various compute targets. Pipeline steps are reusable and can be run without rerunning subsequent steps if the output of that step hasn't changed. Pipelines also allow data scientists to collaborate while working on separate areas of a machine learning workflow.
How to Select the Right Tools to Succeed With Model Deployment
Building, training, testing, and finally deploying machine learning models is often a tedious process for companies that are looking at transforming their operations through AI. Moreover — even after months of development that delivers a machine learning model based on a single algorithm — the management team has little means of knowing whether their data scientists have created a great model, or how to scale and operationalize it.
Below we share a few guidelines on how a company can select the right tools to succeed with model deployment. We will illustrate this workflow using Azure Machine Learning Service, but it can be also used with any machine learning product of your choice.
The model deployment workflow should be based on the following three simple steps:
- Register the model.
- Prepare to deploy (specify assets, usage, compute target).
- Deploy the model to the compute target.
Register Your Model
A registered model is a logical container for one or more files that make up your model. For example, if you have a model that is stored in multiple files, you can register them as a single model in the workspace. After registration, you can then download or deploy the registered model and receive all the files that were registered.
Machine learning models are registered when you create an Azure Machine Learning workspace. The model can come from Azure Machine Learning or can come from somewhere else.
Prepare to Deploy
To deploy a model as a web service, you must create an inference configuration (InferenceConfig) and a deployment configuration. Inference, or model scoring, is the phase where the deployed model is used for prediction, most commonly on production data. In the inference config, you specify the scripts and dependencies needed to serve your model. In the deployment config, you specify details of how to serve the model on the compute target.
The entry script receives data submitted to a deployed web service and passes it to the model. It then takes the response returned by the model and returns that to the client. The script is specific to your model; it must understand the data that the model expects and returns.
The script contains two functions that load and run the model:
- init(): Typically, this function loads the model into a global object. This function is run only once when the Docker container for your web service is started.
- run(input_data): This function uses the model to predict a value based on the input data. Inputs and outputs to be run typically use JSON for serialization and de-serialization. You can also work with raw binary data. You can transform the data before sending it to the model, or before returning it to the client.
When you register a model, you provide a model name used for managing the model in the registry. You use this name with Model. get_model_path() to retrieve the path of the model file(s) on the local file system. If you register a folder or a collection of files, this API returns the path to the directory that contains those files.
Deploy to Target
Finally, before deploying, you must define the deployment configuration. The deployment configuration is specific to the compute target that will host the web service. For example, when deploying locally, you must specify the port where the service accepts requests.
In this article, we introduced some common challenges of machine learning model deployment. We also discussed why successful model deployment is fundamental to unlock the full potential of AI, why companies struggle with model deployment, and how to select the right tools to succeed with model deployment. If you want to learn more about machine learning and model deployment, visit the following pages:
- Azure Machine Learning Service
- Azure Machine Learning for Visual Studio Code
- Get Started with Azure Machine Learning
- Deploy Models with Azure Machine Learning
- Azure Machine Learning Notebooks
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