The amount of data businesses amass increases daily. As it increases, so does the importance of using machine learning and other AI strategies to:
- Convert data into insights
- Drive business decisions
- Improve products and services
A machine learning platform combines intelligent algorithms with data, allowing entrepreneurs to derive business insights and deploy thoughtful, new solutions at scale. If you visit SentiSight.ai machine learning platform for example, you’ll find the tools required to develop, deploy, and improve machine learning. A machine learning platform:
- Automates data workflows
- Accelerates data processing
- Optimizes related functionality
This detailed article explores the world of machine learning platforms to help you learn how they work and more. Keep reading for more details.
How a Machine Learning Platform Works
A machine learning platform facilitates machine learning from end to end. It provides users with the ability to manage the whole data lifestyle, from ingestion to inference. A machine learning platform should support a few key processes, which include:
- Data ingestion: It should provide users with the ability to ingest and integrate data from different external and internal sources. These sources include internet of things devices, databases and enterprise applications.
- Data preparation: It should support data cleansing and augmentation.
- Feature engineering: The platform should be able to transform data into features that present the underlying problems to the predictive models.
- Model deployment: Making the models readily available in manufacturing environments where they offer prediction services to other systems.
- Model training: This is where proper values are determined for the different biases and weights from the raw data by either unsupervised or supervised learning.
- Model comparison: The platform should provide users with the ability to easily and quickly compare models to a previous outcome or a baseline to determine the built model’s quality. A high-end platform also has tools for tracking metrics like loss and accuracy.
- Model management: A good platform needs to monitor and manage models and make sure that they’re accurate and up-to-date.
Additional capabilities should be an accessible development environment, built-in support for familiar frameworks like Python, and enterprise-grade governance, control and security to help safeguard infrastructure.
Machine Learning Types
Machine learning is complex. That is why it’s divided into two main areas: unsupervised and supervised learning. Each of these areas has a specific action and purpose, providing results and using various forms of data.
Supervised machine learning accounts for approximately 70% in popularity. Unsupervised learning is anywhere between 10% and 20%. Reinforcement learning takes the remaining percentage.
- Supervised learning: Involves labelled or known data. Since the data is known or labelled, it’s supervised learning.
- Unsupervised learning: It involves unlabelled and unknown data. No one has looked at the raw data before.
- Reinforcement: Here, the algorithm discovers data via a trial and error process and decides the action that results in higher rewards.
Whether you want an image similarity search pre-trained model that you can use without additional training or train your own, you should find a reputable platform. Remember that each platform is unique. And an industry leader will always provide you with the best outcome.