The purpose of an ML pipeline is to streamline the process of developing and deploying machine learning models, so that data scientists and machine learning engineers can focus on the more important tasks of building and fine-tuning their models, rather than getting bogged down in the details of data preprocessing and model deployment.
A pipeline is a series of steps that are followed in a predetermined order to complete a task, such as building and training a machine learning model. These steps can include things like data collection and preparation, model training, model evaluation, and deployment. On our platform this is a generic sequential workload for execution either on CPU or GPU. A model, on the other hand, is a mathematical representation of a real-world process.
In the context of machine learning, a model is trained to make predictions or decisions based on data. A machine learning model is just one component of an ML pipeline, but it is an important one because it is what actually does the "learning" and makes the predictions or decisions.
A pipeline surrounds a model with inference logic so that your user's inputs and the model's raw outputs can be processed to produce the desired output. For example, the GPT-J pipeline that is available in our model library adds input arguments and logit sampling techniques to the underlying GPT-J model within it.
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From simple addition tasks to the training of a multi-GPU neural network, an ML pipeline takes in inputs, performs tasks on them, and then returns outputs. They are flexible, diverse, and highly composable.
Pipeline Cloud is an all-in-one platform for building and deploying models without the hassle or trouble of a traditional cloud provider. There are no hourly GPU rental fees as we charge on a pay-as-you-go basis. Infrastructure automatically scales with your usage and our software handles the task success logic entirely for you. Simply submit a request to our REST API and you'll receive the output of the pipeline when the run completes. By using an automation platform, AI practitioners can reduce cloud spend through optimising the cloud compute usage and save costs on headcount by managing the deployment (MLops) from a dashboard.
While it is possible to run ML pipelines locally, it can be more challenging than using a cloud-based solution, and may not be the best choice for all situations. A personal computer or on-premises server may not have the same level of computing power as a cloud-based solution, which can limit the complexity and size of the pipeline that can be run, as well as the size of the dataset. Running and scaling AI pipelines locally requires setting up and maintaining the necessary hardware and software, which can be time-consuming and requires technical expertise.
Pipeline Cloud follows a shared infrastructure philosophy, where requests (runs) are not assigned to specific machines, but distributed to the next fastest machine. Serverless GPU refers to the use of graphics processing units (GPUs) in a serverless computing environment. In a serverless environment, you can run your applications and services without having to worry about the underlying infrastructure. Instead of setting up and maintaining physical servers or virtual machines, you can simply deploy your model and let Pipeline AI handle the rest.
Some of the benefits of using serverless compute for ML pipeline deployment include:
Overall, serverless compute can be a useful tool for deploying AI because it allows you to focus on the ML pipelines and models rather than the infrastructure required to run them.
ML pipelines are used in a wide variety of real-world applications. The goal of machine learning is to create algorithms that can learn from and make predictions or decisions based on data. This is done by training a machine learning model on a dataset and enabling computers to learn and adapt on their own, without being explicitly programmed. This can be used to solve a wide variety of problems, such as classifying emails as spam or not spam, recognising objects in images, or predicting stock prices.
Fraud detection: ML pipelines can be used to analyse transaction data to identify patterns that may indicate fraudulent activity.
Customer service: ML pipelines can be used to process and analyse customer service inquiries, such as phone calls or email messages, to identify common issues and provide automated responses.
Predictive maintenance: ML pipelines can be used to analyse sensor data from equipment to identify potential failures and schedule maintenance before they occur.
Supply chain optimisation: ML pipelines can be used to analyse data from various sources within a supply chain, such as production, logistics, and sales, to identify opportunities for optimization.
Personalisation: ML pipelines can be used to analyse customer data to provide personalized recommendations or experiences, such as personalized product recommendations on an e-commerce website.