Machine Learning enables computers to learn from data, identify patterns and trends and use these insights to make decisions or aid decision-making in businesses.
However, it is a hard subject that relies on lots of Math and programming. This is not to say it is impossible to learn; it is very much possible. It is also possible to avoid technical complexities using the platforms we will cover in this article.
Not only do these platforms simplify the process of building the model, but they also hide the details related to infrastructure.
What is Machine Learning?
Machine Learning is the field of study that aims to create computers that can make decisions without needing explicit programming. Before Machine Learning, computers could only do explicitly programmed tasks.
Programmers had to lay out exactly how decisions are to be made by computers. While this works for some functions, some are too complicated to program explicitly.
For example, writing a program to classify images is impossible, given how many different angles, orientations, and lightings are possible for the same image. Machine Learning enables computers to perform tasks without being programmed.
Why Use Machine Learning Platforms?
Machine Learning platforms offer a simplified way to build models. Most platforms offer low-code and no-code builders. All you have to do is supply the data for learning, and the platform handles the rest. You often also do not have to worry about provisioning infrastructure cost-effectively and deploying your models.
Platforms are usually cost-effective compared to DIY setups for smaller businesses building smaller models infrequently. Setting up your own machine learning setup will require purchasing GPUs that are expensive.
However, by renting a setup, you only pay for what you use when you use it. Of course, if you are training larger models and or training frequently, the result of this may be different.
Platforms also simplify managing MLOps. They help you keep logs and metrics for reproducibility.
Now, we will discuss Machine Learning infrastructure platforms.
Baseten
Baseten provides an easy way to deploy Machine Learning models using Truss – an open-source standard for packaging models built using any popular machine Learning framework.
After deployment, Baseten logs and monitors the health of your deployed models. It helps you manage infrastructure by autoscaling your model serving infrastructure based on the traffic you are getting.
With Baseten, you can also finetune models such as FLAN-T5, Llama and Stable Diffusion. The platform also integrates with your existing CI/CD workflows so you can build according to your process.
You can also write server serverless Python functions that integrate with your models. Billing is done by the minute your models are deployed, scaling, or making predictions. This helps you manage costs better.
Replicate
Replicate is a simple way to run Machine Learning models. Replicate simplifies the process of developing and training models by providing a Python SDK and Rest API that you can use to make predictions.
It essentially provides a low-code builder. It provides models for performing common machine learning tasks such as image restoration, creating and editing videos, generating text using large language models, converting images to text and vice versa, and increasing the resolution of images.
Replicate utilizes Cog, a tool for deploying machine Learning models in a production-ready container that is then built into a Docker container for deployment. Replicate provides a production runtime environment that scales according to use. This runtime exposes a REST API that you can access and make use of. Billing is also done by the second.
Hugging Face
Hugging Face is an AI community and data science platform that equips you with the tools you need to build, train and deploy state-of-the-art machine Learning models.
The main attraction of Hugging Face in this context is AutoTrain, a no-code way of building Machine Learning models by simply uploading the training dataset.
AutoTrain will automatically try different models to find the one that works best for your training data. You can then deploy the trained model to Hugging Face Hub, a model serving service.
With AutoTrain, you can build models for image classification, text classification, token classification, question answering, translation, summarisation, text regression, tabular data classification, and tabular data regression. Once deployed, your models will be available via HTTP.
Google AutoML
Google AutoML provides an easy way to build Machine Learning models with minimal effort and expertise. It includes Vertex AI- a unified platform for building, deploying, and scaling your AI models.
With Google AutoML, you are able to store datasets and access the Machine Learning tools used by teams at Google. It also enables you to manage structured data, either AutoML Tabular, detect objects in images, and classify images using AutoML Image.
You can also do the same for video files using AutoML Video. In addition, you can perform sentiment analysis on text using AutoML Text and translate between more than 50 language pairs using AutoML Translation. Deployed models are accessible using REST and RPC APIs.
Azure OpenAI
Azure OpenAI Service provides you access to different models created by OpenAI. These models include GPT-3 and GPT-4, which are models that understand natural language and code and produce natural language and code as a result. GPT-3.5 powers ChatGPT.
In addition, the service also provides access to DALL-E, natural language text to image generator. There’s also Codex, a model that understands and generates code from natural language.
Lastly, there are embedding models that deal with a specialized data set called embedding. These models can be accessed via Azure OpenAI using a REST API, Python SDK, or Web-based Azure OpenAI Studio.
The Azure platform provides the security of the Azure cloud, such as private networking, regional availability, and responsible AI content filtering.
AWS Sagemaker
Sagemaker is a managed AWS service offered as part of the AWS suite of services. It equips you with the tools to build, train and deploy Machine Learning models.
Essentially, Sagemaker helps you automate the tedious process of building a production-grade AI/ML model development pipeline. It provides a framework to build, host, train, and deploy AI models at scale in AWS Public Cloud. Sagemaker provides built-in algorithms to perform tasks such as linear regression and image classification.
In addition, it supports Jupyter Notebooks, which you can use to create custom models. Sagemaker also comes with a continuous model monitor that tries to automatically find the set of parameters and hyperparameters that produces the best results for your algorithm.
SageMaker also helps you easily deploy your models across different availability zones as HTTP endpoints. AWS Cloudwatch can be used to monitor your models’ performance over time.
Databricks
Databricks is a data lakehouse that enables the preparation and processing of data. It makes it easier to manage machine Learning model development throughout its life cycle.
Databricks make it easier to build generative AI and large language models. It provides several crucial features, such as collaborative Databricks notebooks that support programming languages such as Python, R, SQL, and Scala.
Databricks also provides a Machine Learning Runtime that is preconfigured with Machine Learning optimized clusters. To help with deployment, the platform provides model serving and monitoring. It also helps you manage the development pipeline using AutoML and MLFLow.
Final Words
Machine Learning is no doubt going to be useful for any business. However, the deep technical know-how required to build and train machine learning models creates a barrier to entry for most businesses.
However, the platforms covered in this article simplify the process and make machine Learning development more accessible.
Next, check out the detailed article on DataBricks vs. Snowflake.
