Machine Learning Platforms

What is a Machine Learning Platform

Machine learning platforms are software products that help machine learning engineers in training and deploying machine learning models. Machine learning platforms help you automate the full machine learning lifecycle: from training and testing a model through deploying and running it. Even someone who is not an expert in machine learning or coding can utilize built in tools to help bring models to production.

Companies are seeking AI-driven solutions in almost all sectors of industry like healthcare, finance, manufacturing, etc. Machine learning provides solutions in every sector, ranging from predicting financial outcomes to supporting breakthroughs in medicines. To deliver these solutions, just building a model is not enough. You need to access data, clean data, perform feature extraction, and feature engineering before you even start to build a machine learning model. Once you have your model ready, you need to evaluate the model, deploy it, analyze the results and transform them in a way that can be understood by an audience beyond machine learning engineers. Machine learning platforms provide an ecosystem for all steps involved in the ML lifecycle.

A data science platform is a software product which supports data scientists by enabling them to do all sorts of data science tasks in one centralized location. Data science platforms take care of tools, infrastructure issues, environment for models, deployment code, scheduling and more so that data scientists can focus on other things. A data scientist whose main job is to make analyses and models does not have to switch between multiple tools or worry about DevOps or backend engineering.

While Saturn Could is primarily a data science platform, it can also function as a machine learning platform or be used in conjunction with a separate one.

Features of Machine Learning Platforms

High-Quality Solutions
Automation of steps involved in building ML pipeline, ensures best practices are followed and human errors are minimized.
Increased Productivity
ML platforms provide a variety of ready-to-use toolkits to create high performing machine learning models for your business.
Monitor
Monitor the state of your models and track their performance. Convert your machine learning evaluations to interpretable dashboards.
Data Preparation
Machine learning platforms with their built in tools can assist you in inital data preprocessing steps like finding relationships in data, building features, reducing noise in data.

Differences between Data Science Platforms and Machine Learning Platforms

Machine learning platforms are different from data science platforms in that they focus less on creating a space for data scientists to easily create analyses and models then share with coworkers, but instead focus more on the model deployment pipeline. To put it directly, data science platforms are for enabling data scientists with their work, while machine learning platforms are more for helping machine learning engineers with their work. Since the two roles often have a heavy overlap in what they do, data science platforms and machine learning platforms often have overlapping features.

	Machine Learning Platform	Data Science Platform
Objective	Create accurate and high performing models.	Create infrastructure to support data science teams.
Usability	Less human errors in creating ML pipeline. Support in hyperparameter tuning and monitoring your models	Easy access to GPUs, more number of machines, software packages and infrastructure support in data science work.
Features	Automation and built in tools for data cleaning, feature engineering, choosing model, model building, analyzing results etc.	Notebooks support of various packages, scalability, job scheduling, easy Deployment, software package integration etc.
Users	Machine learning teams. AutoML ensures that someone who is not expert in Machine learning/coding can also use ML platform for AI solutions.	Data Scientists, Data Science Managers and Software engineers.