Still, using the bulky immovable hardware to run your models?
Is your infrastructure costs giving you a hard time in your development? – It is time to switch to the cloud. In this article, we lay down a list of platforms available as a service for Machine learning and AI developers. The platforms provide a web-based interface with the ability to scale up and scale down your compute as needed.
The following platforms are powered with cloud infrastructure, which is deemed to be resilient and agile.
Amazon Sagemaker is a platform dedicated to the machine learning domain.
The platform provides a jump start to data scientists and AI developers to build their models, utilize the models from the community, and code right on the platform. Amazon Sagemaker provides you with a scalable cloud computing platform to build, train, and deploy machine learning models quickly. Major benefits of using Amazon Sagemaker are:
- Readily available pre-built algorithms for use
- Gives you a jump start with primary installations and setup did for you
- Allows you to scale up quickly and train models faster
- Provides popular Jupyter Notebook like interface to perform all relevant operations on a single platform
- Provides an auto-pilot functionality to auto train your models
- A massive repository of high quality pre-trained data for training your models faster
- Straightforward collaboration with fellow data scientists by sharing the web platform
Learning Sagemaker is easy.
Azure ML Studio
Azure ML Studio is probably the most sought after platform today in the machine learning domain. It offers a grand suite of pre-built examples and startup codes, to begin with. These coding examples help the developer to get off the feet quickly.
It provides a developer with an interface that is powered with a backend dedicated to machine learning. The backend is pre-installed with the majority of the required libraries for machine learning.
The primary benefits of using ML Studio as a platform are:
- Comes with inbuilt Jupyter Notebook support
- Provides a platform to build, scale and deploy a predictive model easily
- Numerous predictive analysis libraries plugged in for use with the code
- Facility to run, analyze and monitor experiments in an excellent manner
- Has a vast library of pre-built models helpful for faster development
- Provides a graphical flow designer for creating a ML job pipeline for model training
You can try Azure ML for free.
IBM Watson Studio
IBM Watson Studio is an excellent platform for collaborative development.
The leading features of IBM Watson Studio include:
- Auto AI – automates tasks like data preparation, filtering, and cleanup
- Excellent visual interface for modeling
- Supports facility for deep learning
- An excellent workflow designer for deep automated learning
Deep Cognition is a platform dedicated to automating your deep learning process with almost no coding!
It provides a graphical workflow designer to feed data, define the flow, and continuously train your model to improve its predictability. Being focused on deep learning, the platforms are pre-configured to do the desired jobs and have the right tools to take your model from training to production rapidly.
Some of the benefits it offers.
- Visual design tools help you get better clarity on your workflows
- AutoML facility helps in training models automatically with minimal efforts
- Ready to deploy a server for your trained AI model
Dataiku is an enterprise-ready platform which offers all the tools that allow business analysts, data scientists, data analysts, and AI developer to work together. The platform provides an elaborate platform to allow the tasks through a defined pipeline and allow each user to do respective jobs.
Dataiku is highly preferred by organizations for the below reasons:
- The platform supports the majority of the programming languages popular for data science
- Provides inbuilt data visualization tools for easily plotting data
- Provides popular machine learning libraries like Scikit-learn, MLLib, XgBoost
DataRobot, as the name suggests, is a platform that focuses on delivering large scale data to automate model tuning.
It is a premium platform with over a hundred open-source libraries pre-configured for use. It has a self-learning and analyzing data modeling algorithm. It is able to ingest your data, relate based on desired predictions, and build a model ready to predict for you. This is made possible with absolutely no coding at your end.
DataRobot is loved by data scientists for some of the below facts:
- Smart data ingestion engine that can learn and build models
- Helps you compare and visualize the outcome of each model
- Post comparison, you can easily deploy you, model, right from the platform itself
C3 – AI Suite
C3 – AI Suite is probably the most exhaustive suite of AI tools available for an enterprise. This suite is built with the majority of the necessary algorithms coded in. This allows enterprise developers to get a jump start for their applications and build rapidly around it.
The image above depicts how vast the suite is spread. Some of the benefits are as below.
- One suite – for every enterprise developer and data scientist
- Provides full flexibility for the choice of data structure, storage and compute
- Comes with a suite of visualization tools to visualize data as well as workflows
- Easily connects with popular cloud environments for data storage
- Can handle batch processing jobs out of the box
- Single software approval – Reduces start-up time for enterprise projects
Machine Learning and AI are covering the world with its impactful outcomes. The technologies are here to stay and evolve with time. The products utilizing these technologies are resource hungry and need sufficient power to develop as well as deploy them. With a platform as a service, the above platforms and suites of tools make life easier for the data scientists, machine learning developers, and AI developers.
These platforms not only help you get rid of the in-house hardware but also help you save huge investments at the start of the projects. Most of these platforms being billed as per usage or at regular intervals, they do not demand any major commitments. This makes it easier to transition between platforms and keep the development going without any major hiccups.
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