Machine learning as a service on various cloud platforms

       Introduction 

Machine Learning helps users make predictions and develop algorithms that can automatically learn by using historical data. However, various machine learning algorithms such as Linear Regression, Logistic Regression, SVM, Decision Tree, Naïve Bayes, K-Means, random forest, Gradient Boosting algorithms, etc., require a massive amount of storage that become pretty challenging for a data scientist as well as machine learning professionals. Cloud computing becomes a game-changer for deploying machine learning models in such situations. Cloud computing helps to enhance and expand machine learning applications. The combination of machine learning and cloud computing is also known as intelligent Cloud.

 

 

 

 

Machine learning is relatively new, but cloud computing is an emerging technology. Although both technologies are crucial to a company's development, their combined power is greater. While cloud computing offers storage and security to access these apps, machine learning creates intelligent software or machines. 

 

Machine learning requires a lot of computer power to generate sample data, but not everyone has access to many powerful machines. For these reasons, cloud computing is generally employed for computation. Task scheduling and storage are discovered by machine learning (sometimes) in cloud computing.

Machine learning as a service is an umbrella definition of various cloud-based platforms that cover most infrastructure issues such as data pre-processing, model training, and model evaluation, with further prediction. Prediction results can be bridged with your internal IT infrastructure through REST APIs. Amazon Machine Learning services, Azure Machine Learning, Google AI Platform, and IBM Watson Machine Learning are four leading cloud Machine learning as a service that allow for fast model training and deployment

 

These web services for machine learning are offered by many cloud computing platforms. Amazon Web Services, Microsoft Azure, Google Cloud, and IBM Cloud are the most well-liked of these.

Types of Cloud Computing:

• Software as a Service (SaaS)
• Platform as a Service (PaaS)
• Infrastructure as a Service (IaaS) 

The key machine learning as a service platforms offered by Amazon, Google, Microsoft, and IBM will be briefly discussed, and then the machine learning APIs that these providers support will be contrasted. 

 

 

 

Let's analyze them now in more detail:

1. Amazon Web Services

With AWS, Amazon stands out from the competition in terms of cloud services. At the same time, its voice assistant Alexa became well-known when discussing realistically implemented AI from a consumer's standpoint.Three methods of prediction are available with Amazon ML: binary classification, multi-class classification, and regression. The user must choose a target variable to label it in a training set because the Amazon ML service does not support any unsupervised learning techniques. Additionally, a user is not required to be familiar with machine learning techniques because Amazon selects them on its own after understanding the supplied data.

 

 

Sage-Maker Studio, the first IDE for machine learning, was introduced by Amazon in 2021. We can run all of the ML model training tests using this tool's web-based interface in a single setting. SageMaker Studio provides access to all development techniques and tools, including notebooks, debugging tools, data modelling, and its automatic creation. 

 

Other from being built for large data and distributed systems, Amazon also includes built-in algorithms. These include of:

 

1. Linear learner is a supervised method for classification and
2. Factorization machines is for classification and regression designed for sparse datasets.
3. XGBoost is a supervised boosted trees algorithm that increases prediction accuracy in classification, regression, and ranking by combining the predictions of simpler algorithms. 
4. K-means is an unsupervised learning method for clustering
5. An index-based method called K-nearest neighbour (k-NN) can be combined with a neural topic model to create unique recommender systems. Additionally, Amazon.com itself uses a different Amazon Personalize engine for real-time recommendations.
6. Using machine learning, Amazon Forecast improves forecast accuracy.
7. The language translation service Amazon Translate makes use of machine learning and natural language processing.
8. In machine learning systems, Amazon Personalize generates customised suggestions.

       When to Use Machine Learning:

It is important to remember that ML is not a solution for every type of problem. There are certain cases where robust solutions can be developed without using ML techniques. For example, you don’t need ML if you can determine a target value by using simple rules, computations, or predetermined steps that can be programmed without needing any data-driven learning.

There are many different ML models there. Amazon ML only trains linear models as ML models. When a model is referred to as a linear model, it means that its specification is a linear collection of features.

To create a model that can predict or estimate the target value, the learning process computes one weight for each feature based on training data.

 

The most common accuracy measures for regression tasks are root mean square error (RMSE) and mean absolute percentage error (MAPE). These metrics gauge how far the actual numerical response is from the projected numerical target (ground truth). The RMSE measure is used in Amazon ML to assess a regression model's prediction power.

Online prediction scenarios are used when you want to make predictions in a low-latency environment for each example separately from the other examples. Predictions can be used, for instance, to quickly determine whether a specific transaction is likely to be fraudulent.

 

2. Microsoft Azure 

 

The primary environment for managing datasets, training models, and deploying them is Azure Machine Learning, Azure Data Catalog and Azure Cloud Functions. It is close to Google and IBM in the race for AI dominance through Azure Machine Learning and manages to integrate other services like business intelligence solutions quite well.Data exploration, preprocessing, method selection, and model validation are all possible with Azure Studio. About 100 methods for classification (binary+multiclass), anomaly detection, regression, recommendation, and text analysis are supported by the Studio. It's important to note that there is just one clustering algorithm on the platform (K-means).

 

Azure AI Gallery

Azure AI Gallery is a major part of Azure ML. It is a repository of community-provided machine learning solutions that data scientists can study and apply.

 

 

 

Both ML Designer and Automated ML give novice users the tools they need to create ML solutions. The Azure Machine learning studio, on the other hand, has a lot of tools that tech-savvy data scientists and enterprise-level solutions can use. However, since Azure ML is designed to be used as a single platform with all of its features, this does not restrict these tools.

1. Microsoft Azure Bot Service - This offers scalable, intelligent, and clever bot services.
2. For mobile and web apps, Microsoft Azure Cognitive Search is a machine learning-based service.
3. Create and deploy machine learning models on the cloud with Microsoft Azure Machine Learning.

By switching to Azure Machine Learning, Wolters Kluwer Health was able to control its machine learning workflow. The inefficiencies of carrying out these processes on independent Azure VMs are now gone thanks to the data science team's ability to do rapid tests and ad hoc tasks from within an Azure Machine Learning workspace. Workflows in Azure Machine Learning can be used by Wolters Kluwer Health to handle model training, tracking, and automating model retraining.And now Wolters Kluwer Health can simply put in the fresh data to effectively handle retraining models, as opposed to having to construct up complete settings again.

 

Better ML outcomes nearly always derive from more data. However, the sheer volume of data has a quality all of its own that makes it harder for enterprises to meet their existing compliance obligations in terms of data gravity, data sovereignty, and privacy. Enterprises can integrate hybrid-cloud and multi-cloud capabilities into their ML strategies to address these data concerns. 

 

Azure Arc-enabled ML is made possible by the integration of Azure Machine Learning and Azure Arc. Organizations like yours can employ Azure Machine Learning anywhere—across one or more clouds, on-premises, or at the edge—with the help of Azure Arc-enabled ML capabilities.

 3. Google CloudML

 

If you require extreme scalability over the long term while also needing flexibility for initial trials, Google Cloud is the ideal solution for you. If your developers are already familiar with Google Cloud Services, they will navigate machine learning pretty effortlessly. The Google platform has drawbacks, such as its business and real-time analytics capabilities, which are still in their infancy compared to those of other service providers. A no-code approach to developing data-driven solutions is advocated by the cloud-based machine learning platform Google Cloud AutoML. Both novice and seasoned machine learning engineers can create unique models using AutoML.

 

The fundamental idea behind Google's platform may be summed up by its AI building components. These are essentially many technologies, like as TensorFlow, AutoML, and APIs, which are intended to be combined to create ML solutions. This implies that a custom model and pretrained models can be combined into a single product. Another Google offering is TensorFlow, which is not ML-as-a-service but rather an open source machine learning library comprising several data science tools. TensorFlow lacks a visual user interface, and learning it would be challenging. The three-tier cloud service model basically suggests infrastructure-as-a-service and platform-as-a-service solutions when TensorFlow and Google Cloud are combined.

 

1. Google Cloud AutoML is used for developing and training AutoML machine learning models.
2. This platform from Google Cloud AI is used to develop, train, and maintain ML models.
3. 120 languages are supported by Google Cloud Speech-to-Text, a speech recognition system for converting spoken words into text.
4. Create machine learning models for cloud vision using Google Cloud Vision AI to recognise text, among other things.
5. Google Cloud Text-to-Speech is a technology for creating speech that converts text to speech. 

      Model Creation

The most crucial stage is when data scientists and developers code the model in their own local environments. Python-based toolkits are supported by Google Cloud ML Engine for building machine learning models. In addition to TensorFlow, available frameworks and toolkits also include XGBoost, Scikit-learn, and Scikit-learn. Before submitting their code for a training job conducted in the cloud, developers can test and debug it using a portion of the original dataset.

      Model Training

We feed a model known data points called features along with the initial result as a label while training a model. We use the test dataset as input for the assessment and contrast the projected value with the real labels. The method is continued until there is a minimal discrepancy between the anticipated labels and the actual labels. ML Engine also offers hyperparameter adjustment for complex models like artificial neural networks. The training phase ends and the finished model is ready for use when the predictions match the actual labels for the majority of the dataset's data points.

 

Predictions

The core value of machine learning is derived from accurate predictions. Google Cloud ML Engine hosts the fully-trained models for prediction. 

 

 

 

 

4. IBM Watson:

Moreover, one of IBM's most well-liked open-source cloud computing systems is IBM Cloud (formerly known as Bluemix). It includes multiple hybrid, private, and public cloud delivery options.Watson helps organizations predict future outcomes, automate complex processes, and optimize time .

It offers different hybrid, private, and public cloud delivery strategies.

 

In an effort to turn Watson into a cloud-based data operating system and keep its corporate clients satisfied by meeting their needs in the analytics space, IBM places a lot of emphasis on Watson.

1. IBM Watson Studio is a tool for creating, utilising, and managing AI and machine learning models.
2. We may use IBM Watson Natural Language Understanding to analyse and categorise text.
3. As the name implies, this device, IBM Watson Speech-to-Text, transforms spoken or voiced commands into text.
4. The personal virtual assistant is created and managed using the IBM Watson Assistant software.
5. IBM Watson Visual Recognition: It aids machine learning in the search and classification of visual images.
6. This product, called IBM Watson Text-to-Speech, converts written or spoken instructions into voice format. 

• You may create analytical models and neural networks with IBM Watson Machine Learning, train them with your own data, and then utilise them in applications. You may design, train, and deploy Machine Learning models using the comprehensive set of tools and services offered by Watson Machine Learning.
  
• Statistical analysis and IBM Cloud On a data and AI platform, IBM Watson Studio on IBM Cloud Pack for Data enables the entire machine learning lifecycle. In your hybrid multicloud environment, you can create, manage, train, and deploy machine learning models wherever your data resides.
  

Respondents Running App 2017 vs 2018

 

 

Advantages of Machine Learning with Cloud Computing 

• Cloud operates around the tenet of "pay for what you need." For businesses that want to deploy ML capabilities for their operations without incurring significant costs, the pay-per-use cloud model is beneficial.
  
• It gives users the freedom to deal with machine learning features without having to have highly developed data science expertise.
• As projects go into production and demand rises, it scales up and makes it easier for us to experiment with different ML technologies.
• The cloud enables access to intelligent capabilities without requiring highly developed artificial intelligence or data science skills. 

AI M&A activity between 2014 and 2018

 
Since Google made significant investments in AI start-ups in 2014, M&A cases in this sector have really taken off. Since then, just those 6 firms have bought more than 30 AI businesses: 

 

 

 

Applications of Machine Learning Algorithms using the Cloud

1) Cognitive Computing

A special form of technology called cognitive computing tasks that normally require human behavior by using the principles of artificial intelligence and signal processing. In cognitive computing, a machine-learning algorithm is trained using a lot of data. Applications for cognitive computing can be accessed through cognitive clouds, which are created by combining cloud and machine learning technology.

 2) Internet of Things (IoT)

The Internet of Things (IoT) is a platform that provides cloud services, including online data processing and archiving. The popularity of cloud-based ML models has recently increased. It begins by requesting input data from the client end, uses cloud servers to process machine learning algorithms using artificial neural networks (ANN), and then sends back output to the client. In this situation, the client's private information might be kept on the server, posing privacy concerns and discouraging users from using the services. 

3) Personal Assistant:

Having a personal virtual assistant that can support customers like a human is now essential for growing a company's business. These chat bots or personal virtual assistants are now being used in all industries, including banking, healthcare, education, and infrastructure, to perform a variety of tasks. 

4) AI-as-a-Service

These days, all major cloud providers offer AI services using platforms that offer AI as a service. When used in the cloud, open-source AI features are considerably less expensive. These services offer AI and machine learning capabilities, enhancing the system's intelligence by increasing the capacity for cognitive calculations. It contributes to the system being comparatively quick and effective.

 

Conclusion:

Cloud computing and machine learning are particularly important for next-generation technology. Because cloud computing provides the optimal setting for machine learning models with a lot of data, demand for machine learning is always rising. It can also be used to find patterns, train new algorithms, and make predictions. A scalable, on-demand environment for data collection, archiving, curation, and processing is provided by the cloud.

Additionally, all cloud service providers are aware of the value of machine learning in the cloud, which has led to an increase in the demand from small, midsize, and big businesses for cloud-based ML models. Cloud computing and machine learning are incompatible with one another. Cloud computing broadens the possibilities for machine learning applications if machine learning makes cloud computing more improved, efficient, and scalable. As a result, we can conclude that Ml and cloud computing are closely related, and when used together, they can provide amazing outcomes. 

 

References:

1)https://www.analyticsvidhya.com/blog/2022/01/introduction-to-cloud-computing-for-machine-learning-beginners/

2)Top Cloud Computing Platforms for Machine Learning - GeeksforGeeks

3)Machine Learning and Cloud Computing - Javatpoint