Machine Learning as a Service: What and How to Use It?

The potential of Machine Learning (ML) has been realized across different industries and has become a game-changer for industries such as healthcare, telecommunications, and finance, over the past decade. Its ability to analyze vast amounts of data has transformed decision-making processes and operational efficiencies. Small and Medium-sized enterprises utilize this technology to enhance their business operations by gaining meaningful insights from data.

In fact, the global machine learning industry is expected to grow to USD 209.91 billion by 2029, with a Compound Annual Growth Rate (CAGR) of 38.8%.

To maintain the business growth in the competitive market, ML engineers are assigned to develop more models to meet the dynamic business demands. However, the model development alone cannot sustain the market demands. It is significant to maintain the models, monitor the performance of the models, deploy, and experiment with innovative solutions.

The lack of adequate data scientists as developers makes it difficult to achieve the business’ needs at scale. There comes in Machine Learning as a Service (MLaaS)! MLaaS allows businesses to accomplish complex work and tackle issues easily. It makes technology scalable and cost-effective, enabling pay for what you use. Through this comprehensive guide, you’ll learn everything about ML as a service, including its use case, benefits, and best ML tools.

What is ML and MLaaS?

Machine learning (ML) is defined as a subset of Artificial Intelligence (AI) which enables machines to automatically learn from data and historical experiences to recognize patterns and make predictions or estimation with nominal human intervention.

ML as a service refers to a range of cloud-based platforms offering machine learning tools. The first step is understanding AI and ML technologies and MLaaS simplifies their use without needing deep technical skills. MLaaS is like Software as a Service (SaaS) or Platform as a Service (PaaS), or Infrastructure as a service (IaaS).

IaaS allow clients to outsource physical or hardware infrastructure to the cloud. PaaS is a domain for software developers helping to build, test, deploy, maintain, and manage each phase of the development life cycle of applications. SaaS is a domain for end-users or business customers which provides on-demand, pay-per-use service of an application.

Similarly, MLaaS is a cloud computing service facilitating enterprises and people to access ML tools and algorithms via a cloud-based platform.

These platforms help ML teams with the following.

• Pre-built predictive analysis for different needs
• Data preparation
• Model training
• Workflow management
• Model deployment

This brings in benefits such as curtailing the need for businesses to invest in costly hardware or employ data scientists to build and train their own ML models. MLaaS allows enterprises to access pre-built ML models and APIs to resolve intricate business challenges without internal expertise. This helps enterprises to realize real-time decision-making, enhanced insights, and high-quality services without requiring substantial initial investment or infrastructure.

What to Expect from ML Platform as a Service?

The demand for machine learning services is skyrocketing as businesses seek to use the power of AI. Here are some key examples of what you can expect from an ML platform as a service.

Data Management: With the shift from on-premises to cloud storage, MLaaS platforms help organize and manage your data efficiently. They streamline data access and processing for machine learning experiments. This makes life significantly easier for data engineers.

Access to ML Tools: MLaaS providers offer APIs for tasks like sentiment analysis, face recognition, and more, so you don’t have to handle the complex computations yourself. Some platforms even feature drag-and-drop interfaces for easy experimentation.

Ease of Use: ML platform as a service eliminates the need for complex software installations. By handling complex algorithms for operations like searching, sorting, or insertion, these platforms provide a user-friendly environment that allows data scientists to focus on improving models.

Cost Efficiency: Building and maintaining an ML workstation can be expensive. MLaaS, in this regard, offers significant cost savings by allowing you to pay only for the computing power you use. It eliminates the upfront costs of hardware and software, as well as ongoing expenses like maintenance.

Now that you have an idea about the capabilities of the ML platform as a service, let’s explore how to use ML as a service.

How Do Machine Learning Models Work?

Machine learning follows a systematic approach to make predictions and ensure high accuracy based on the learning from the data. Each step in the process is crucial and must be executed properly.

Read on to learn what makes ML work and how it can be implemented

Step 1: Data Collection

• Identify the problem you want to solve and accordingly gather and collect relevant data for training the model.
• Please note that ethical use of data is of utmost importance.

Step 2: Data Preprocessing

Before modeling, data must be prepared which involves removing duplicates and missing values, standardizing formats, among other things.

Step 3: Model Training

• The next step is to feed the training data into the appropriate algorithm selected based on problem type.
• The predictions are then compared with the actual results.

Step 4: Evaluation

• At this step, the model’s predictions are matched with the actual results to check for accuracy.
• If the predictions are inaccurate, the algorithm is trained multiple times until the desired outcome is achieved.

Step 5: Deployment

• Once the model meets desired criteria, deploy it for real-world applications for addressing specific issues.
• Don’t forget to monitor and update the model as required to maintain accuracy.

How to Use ML as a Service?

The use of ML as a service extends to many industries, including healthcare, telecommunications, finance, retail, and more. You’d be glad to know that almost any organization can find a way to benefit from MLaaS in some form.

Want to learn how to use ML as a service for your organization? This section has you covered.

Step 1: Outline Your Goals

• Begin by outlining the objectives of your project clearly.
• Research the subject thoroughly.
• Assess your resources and desired outcomes.

Step 2: Choose the Right MLaaS Solution

• This step involves searching for an MLaaS provider. Alternatively, you can look for a ready MLaaS solution that matches your project goals, timeline and budget.
• You may consider outsourcing to an MLaaS partner if you don’t have an ML team. Alternatively, if you plan to manage the model yourself, you can explore flexible MLaaS platforms that cater to different skill levels.
• Collaborate with a third-party MLaaS provider that shares your vision and can help set up necessary conditions before development starts.
• When selecting a pre-built MLaaS solution, you must cross-check that it meets your organization’s exact needs in terms of features and capabilities.

Step 3: Integrate and Deploy

• Integrate the chosen model into your existing system or workflow.
• Post-deployment, ensure you have tools and support to monitor and manage the ML algorithm’s performance.

Step 4: Ongoing Management

Remember, your work doesn’t end with deployment. Prepare for continuous monitoring, maintenance, and optimization.

What is ML as a Service Used For?

ML as a Service provides instant access to advanced tools via the internet, eliminating the need for investment or expertise to develop them yourself. It can be applied to a range of tasks across different industries.

Read on to learn some exciting ways in which MLaaS can make a difference.

Use Case	Description
Natural Language Processing	Gather insights and automate responses by analyzing text data. Automatically categorize support tickets into billing and technical issues.
Forecasting	Predict future trends and outcomes based on historical data.
Data Exploration	Improve efficiency in visualizing and interpreting complex data.
Anomaly Detection	Ability to identify unusual data patterns that could signal fraudulent activities or other irregularities.
Searching and Understanding Datasets	Enables non-technical users to interact with large datasets by converting natural language queries into SQL queries.
Regression Analysis	Helps to analyze and understand the relationships between variables. For instance, it can determine how factors such as location, square footage, and the number of bedrooms affect property prices.
Image Recognition	Helps to recognize and interpret objects in images. This technology is ideal for mobile apps and has various applications. Pinterest, for example, makes use of image recognition to improve user experience by identifying and categorizing visual content.
Recommendation Engines	Predict and suggest products or content based on user behavior and preferences. For instance, recommended movies on Netflix, products on Amazon, or songs on Spotify are based on user history.

When Should You Use ML?

ML as a service should be used whenever you need to solve a business problem that can be solved with ML. Here are a few examples to help you better understand how MLaaS is a good fit for various business problems.

• Fraud Detection: For finance teams to identify fraudulent transactions.
• Customer Churn Prediction: For marketing teams to forecast customer retention.
• Lead Scoring: To prioritize potential leads.
• Employee Attrition: To predict and reduce staff turnover.

However, it is important to note here that data is an important component of machine learning.

Read on to know why it matters and what are the common issues you should watch out for.

High-Quality Data: Ensure you have relevant and high-quality datasets for your business problem. Good data is essential for training effective machine learning models.

Common Data Issues

• Bias: This can result from data collection methods, sample demographics, or preparers’ personal biases.
• Missing Data: This may occur due to errors, hardware failures, or intentional removal. Proper handling is necessary to avoid inaccuracies in model results.
• Data Impurity: Issues such as incorrect labeling or outliers need to be addressed and cleaned to ensure the model’s predictions are reliable.

Benefits of Machine Learning

ML offers several benefits by automating data-extensive processes and improving accuracy, thus freeing up ample time for more critical roles. Want to learn more?

Limitations of Machine Learning

While Machine Learning offers impressive capabilities, it also has several limitations. Here are some key ones.

Data Dependency

• Requirement: ML algorithms need extensive datasets to learn and make accurate predictions.
• Challenge: Waiting for sufficient data or generating quality data can be time-consuming and may delay model development.

Bias in Data

• Impact: Algorithms that are trained on biased data tend to generate biased results.
• Solution: Ensuring data quality is crucial for eliminating biased results.

Initial Investment

• Requirement: Significant time and effort are needed to train ML models effectively.
• Challenge: This initial investment may be huge, especially for organizations with limited resources.

Understanding the limitations of ML, let us discover how it differs from AI.

AI vs ML: Know the Difference

Wondering how exactly AI differs from ML? AI is about enabling a machine to adapt like a human, while ML is about teaching those machines to learn from data. Here is a comparison between AI and ML that will help you further understand the difference based on multiple aspects. Also read the blog Understanding AI to learn more about AI.

Aspect	AI	ML
Definition	AI is a broad field that focuses on creating systems that can perform tasks requiring human-like intelligence.	ML is a subset of AI that enables a machine to learn and improve anonymously from past data.
Methods	Includes ML as one of its methods, along with rule-based systems, expert systems, and other approaches.	A subset of AI, utilizing algorithms and models to enable learning from data.
Common Use Case	Often used broadly in marketing and by cloud providers to describe various technologies that involve smart behavior.	Used more precisely to describe systems that involve training models on data to improve their performance over time.

Best Machine Learning Tools

North America currently leads the global machine learning market, largely due to its strong research and development sector. Additionally, Europe is projected to experience significant growth soon, driven by a growing skilled workforce and rising demand for artificial intelligence across both the services and goods sectors.

With that overview of market trends, let’s now explore the best ML tools available in the market.

Tools	Features
Google Cloud	Comprehensive MLaaS offerings cover data visualization, preprocessing, model training, and deployment.
Microsoft Azure	Includes tools like Azure CLI for managing machine learning tasks.
IBM Watson	Offers a range of ML tools for different stages of the AI workflow.
Amazon Machine Learning Tools	Provides tools such as Amazon SageMaker and Amazon Rekognition for model training, image recognition, and real-time deployment.

Conclusion

ML as a service stands out as a transformation solution for organizations looking to use the power of machine learning while avoiding the complexities of setup and management.

As you generate more and more data, investing in ML as a service for your company is a smart move for the future. When it comes to using machine learning effectively, Calsoft can be your ideal partner.

Discover how we can transform your data into actionable intelligence and improve productivity – Analytics ML Offerings to explore the possibilities.