Best Cloud Platform for Machine Learning (Pros and Cons)
Best Cloud Platform for Machine Learning (Pros and Cons). Firstly, artificial intelligence (AI) and machine learning are growing at such a fast pace that it is hard to find the best cloud platform for it. But why are we looking at the cloud computing option? Well, cloud service let’s you quickly provision, turn on and use large scale compute clusters. No downtime, and quick scaling. In this article the introduction starts with machine learning (ML) and how it works, followed by why cloud computing is vital in ML. All in all, cloud platform is a concept, that refers to the operating system and server hardware in an online data center. Therefore, enterprises rent to access to computing services such as servers, databases, etc. Coming back to the main topic of today’s blog about machine learning. Well, it is a branch of artificial intelligence and computer science, that focuses on using data and algorithms to copy the way humans learn and over time improving the accuracy of the tasks.
Shall we start with Best Cloud Platform for Machine Learning (Pros and Cons).
What is Machine Learning ?
Algorithms build mathematical models that help make predictions or decisions without being explicitly programmed. All in all, Machine learning combines computer science and statistics to create predictive models. Primarily, machine learning is about building or using algorithms that learn from historical data. The more information you provide, the better the performance gets.
Additionally, machines automatically learn from data, improve performance based on experience, and predict situations without being explicitly programmed.
Various methods allow computers to operate autonomously without explicit programming. Hence, machine learning applications receive new data and learn, grow, evolve, and adapt on their own.
Also, machine learning extracts useful information from large amounts of data by using algorithms to discover patterns and learn from an iterative process. Instead of using predefined equations as models, machine learning algorithms use computational methods to learn directly from data.
How does Machine Learning work?
In essence, machine learning algorithm is created from the training data set to create a model. As new inputs are introduced to the trained ML algorithm, it uses the developed model to make predictions.
The picture above shows a high level use case. However, a typical machine learning example may include many other elements, variables, and steps.
Why Cloud Computing is important in Machine Learning?
For a long time in the past, companies needed to invest a lot of money in machine learning to be profitable. Furthermore, machine learning required a lot of infrastructures, data analytics were expensive and there was very little data available to feed these machine learning algorithms.
While this was not that big a deal for large multinational corporations, it was very difficult for small and mid level companies. But the popularity and advancement of cloud services have made everything much easier. Now companies access Machine Learning algorithms and technologies from a third party vendor (cloud platform). Hence, they started getting the benefits with a much smaller initial investment.
This is why cloud computing is so important for machine learning! This is a solution for many small businesses that don’t want to create, test and implement their own machine learning algorithms from scratch. These companies then focus on their core business and take benefits of machine learning without becoming experts. In this way, they increase returns while reducing investment risk. In short, it’s a win win situation for everyone.
Cloud Platforms for Machine Learning
1. Google Cloud
First cloud platform is Google Cloud Platform. Simply put, a cloud computing platform provided by Google. Launched in 2008 and provides companies with the same infrastructure that Google uses for its in house products. Basically, Google Cloud offers a number of machine learning products:
Features
- The Google Cloud – AI platform is used to build, train, and manage machine learning models.
- Google Cloud natural language – for natural language processing for text analysis and classification.
- Google Cloud Vision AI – used to build cloud vision machine learning models to detect text and more.
- Google Cloud AutoML – used to train and develop AutoML machine learning models.
Pros
- GCP provides API services to customers to transfer information. It is an individual item or a client package proposal.
- Google Cloud AI has out of the box CV algorithms and video processing modules/APIs that makes is easy to use for image/video processing application and use cases.
- Google Cloud AI was easy to set up without need for lot of customization and configuration.
- Integrates very well with the Google BigQuery and Google PubSub that makes it easy to have a ready to use pipeline from data ingestion to analysis.
- Analysis of the results of the conversion in real time.
Cons
- Companies need to take care to protect from scammers and cybercriminals.
- Better support for Python and other coding languages is required.
- Customization of existing modules and libraries is harder and it does need time and experience to learn.
- More security features needed to keep unauthorized users out.
2. Amazon Web Services
Amazon Web Services is a cloud computing platform owned by Amazon. Launched in 2006 and is currently one of the most popular cloud computing platforms for machine learning. AWS offers a number of machine learning products, including:
Features
- Amazon Personalize, where machine learning system generates personalized recommendations.
- Amazon Translate uses machine learning and natural language processing for language translation.
- Augmented AI used to test human machine learning models.
- SageMaker to create and train machine learning models.
Pros
- Multi featured Cloud offering. There are services for computing, storage, networking, database, analytics, application services, and security.
- Physical Security, where data is stored in AWS data centers located inside nondescript facilities.
- Many cloud security tools support over 90 security standards and compliance certifications.
- AWS Lambda lets developers run coding without provisioning or using a managed server.
Cons
- User Interface for some of the services should improve (for ex: ECS).
- Support plan pricing needs to consider start ups.
- No free technical support.
3. Microsoft Azure
Microsoft Azure is a cloud computing platform created by Microsoft. First launched in 2010, it is a popular cloud computing platform for machine learning and data analytics. Here are some Microsoft Azure offerings for machine learning:
Features
- Microsoft Azure Machine Learning is used to build and deploy machine learning models in the cloud.
- This provides a scalable, intelligent and intelligent bot service.
- Intelligent cognitive services for applications.
- Used to create and deploy machine learning models on the cloud.
- Offers Apache Spark based analytics.
Pros
- Seems like the cheapest machine learning tool out there.
- Since Azure ML is a cloud based tool, no processing is performed on your computer, so reliability and speed are top notch.
- Many companies rely on Microsoft packages. Azure ML works well with Excel, CSV, and access files.
Cons
- Since it is a Cloud based solution, you always need a good internet connection to use it.
- Multiple Models: there are many machine learning models, but they are quite limited compared to R. Most data scientists still use and prefer R, so newer models tend to be released as R libraries. With Azure ML, you have to wait for Microsoft to come up with new models. Evaluate and decide if including is a good idea or not.
- Tableau interface- no easy way to connect with Tableau.
4. IBM Cloud
The IBM Cloud Platform is a cloud computing platform provided by IBM. Offers a variety of cloud delivery models, including public, private, and hybrid models. IBM Cloud offers a number of machine learning products:
Features
- A speech generation system that converts text into natural audio.
- Natural language processing for text analysis and classification.
- Finds and classifies visual images using machine learning.
- Used to create and manage virtual assistants.
Pros
- Very positive experience with good support and tools.
- The storage capabilities are good and affordable.
- Looks very secure for your data to stay safe.
Cons
- In some cases it is slow and present problems of delay to synchronize and does not have much memory.
- Issue faced in Data Transfer at the time peak hours.
- Some references are too big that they stuck my web browser.
Why Do You Need MLaaS?
Well, machine learning (ML) used as a service is a very important business model. Getting a pre built platform saves you time and money.
Why should you choose MLaas? Pure simply, you can acquire a huge amount of data due to the scaling effect. You will not have a problem of lacking the data for training purposes, for example.
In turn, with machine learning cloud platform your product effectiveness or company services you offer significantly improves. In addition there is an automation of work processes, and that leads to improved efficiency, getting right through to customers interaction.
The core of business is maintained on the highest level, with no need to hire specialist on regular basis. Thus, time saving.
Correct Cloud Platform ensures optimization of your activities as a business and opens new business prospects to develop your business further.
Thank you for reading Best Cloud Platform for Machine Learning (Pros and Cons). We shall conclude.
Best Cloud Platform for Machine Learning (Pros and Cons) Conclusion
In summary, the tools listed above are considered are just the tip of the iceberg. You need to investigate thoroughly for your individual needs. The main cloud platforms are presented, and some of them have tools designed to handle automation, support versioning or provide an end to end environment for machine learning development.
Finally, the key is finding these tool that gives you the best results at any given time. Each data scientist or engineer has a unique way of working, and some parts of the process take more time than others. So each of those tools are unique to your company requirements.
