Best Machine Learning Software for Your Business in 2023

Table of Contents

What is Machine Learning Software?

Machine learning software consists of tools that extract insights from data and create logical models based on them. These models can then be used by software to automate tasks. Machine learning algorithms can be embedded within applications to provide automated and artificial intelligence (AI) features. Machine learning has emerged as the most important technology of the 21st century. 

Let us have a look at the standard features of machine learning software.

Common Features of Machine Learning Software

1. Preprocesses Data 

Data mining involves transforming raw data into an understandable format. Each algorithm works differently and has specific data requirements. A machine learning software helps you prepare data for each algorithm, recognize and prepare the text and follow the best practices for data partitioning. 

2. Feature Engineering

Feature engineering is the process of altering data in order to make machine learning algorithms work better. Machine learning systems can build new features from existing categorical, text, and numerical data. They even help you generate reports and analyze them to make better decisions. 

3. Diverse Algorithms

Every dataset consists of unique information that represents the individual characteristics and strengths of a business. Due to a changing business landscape, there will be various situations and conditions where a single algorithm cannot solve every business problem. 

Interestingly, machine learning software offers you a diverse repository of algorithms to test against your data. This helps you find the best fit for your particular data. It even allows you to add new algorithms to your database. 

4. Algorithm Selection

Having hundreds of algorithms at your disposal is great, but it is not possible to try out every one of them on your data. While some may not be ideal for your data, and some may not be suited to your data size.  A machine learning software will help you make sense of your data and run only those algorithms that work the best for your system. 

5. Training and Tuning

It is quite usual for machine learning software to train the algorithm on your data. Moreover, it would eliminate the need for manually carrying out the Newton-Raphson interaction method. However, not using this method would lead to the hyperparameter tuning not being excluded. You would also need to make a feature selection to improve both efficiency and speed of the model. 

However, automated machine learning solutions use hyperparameter tuning and know which tools must be used to tune each algorithm. 

6. Ensembling

In data science, teams of algorithms are known as “ensembles” or “blenders.” Each algorithm balances out the weakness of another algorithm. Ensemble models thus typically outperform independent algorithms due to their diversity. Machine learning platforms offer the right set of algorithms that can blend to form a diverse range of algorithms. This, in turn, will help you build better systems and provide excellent customer service. 

7. Head-to-Head Model Competitions

It is not possible to know in advance which algorithms can perform the best on your data. So, you need to compare the accuracy and speed of different algorithms on your data irrespective of the programming language or machine learning library they come from. You can think of it as a competition among the models. 

Machine learning software will build and train dozens of algorithms, compare the results and then rank the best algorithms. It will segregate the algorithms based on accuracy, individual predictions, and speed. 

8. Human-Friendly Insights

Over the past few years, ML and AI have made a tremendous impact on predictive power. However, it is not enough to score well only on the accuracy and speed front. You need to trust the answers given by the algorithms. Moreover, in regulated industries, you even need to justify the model to the regulator. Furthermore, you also need to align your marketing message with the model your audience has chosen. 

Machine learning solutions allow you to explain model decisions in a manner that humans can easily understand. They even show which features are most important for each model and demonstrate the patterns fitted for each one of them. 

Some software even provides detailed modeled documentation that will help you meet your regulator’s requirements. 

9. Easy Deployment

Most of the machine learning software offers easy deployment. Some even provide one-click deployment that can be easily operated by a manager. You can test the models on your standard system hardware with several options available and check how the platform scores in pre-tests. This needs to be done to ensure that the same solutions are generated in the training period. 

Also, machine learning systems have an extensive technical support team located worldwide that provide engineering and data science support round the clock. 

10. Model Monitoring and Management

In a continually changing world, your AI and ML applications need to keep up with the latest trends. Machine learning platforms can easily identify when a model’s performance is deteriorating over time

Thus, machine learning software makes it easy to compare predictions to actual results. Moreover, it simplifies the task of training a new model based on the data. 

Benefits of Using Machine Learning Software

ML offers some great advantages to the sales and marketing sector. It simplifies product marketing and even assists in making sales forecasts. Let us look at some of the most significant benefits offered by machine learning software. 

1. Massive Data Consumption from Unlimited Sources

Machine learning solutions consume unlimited amounts of data. This data can then be used to modify your sales and marketing strategies based on consumer behavior. Once the model is trained, it can easily identify the relevant variables. Subsequently, you will be able to collect focused data feeds and eliminate long and complicated integrations. 

2. Analysis Prediction and Processing

A machine learning software can identify relevant data and make it possible for you to take appropriate actions at the right time. 

Subsequently, your customers will be able to see the right offers at any given point in time. Interestingly, you will achieve this without investing the effort to plan and make the right ad visible to your customers. 

3. Interpret Customer Behavior

Machine learning solutions allow easy analysis of data related to past behaviors and outcomes. Thus, based on the new and different data, you will be able to make better forecasts and predictions of customer behaviors. 

4. Facilitates Accurate Medical Predictions and Diagnoses

Machine learning software can be of great advantage to the healthcare industry. It helps identify high-risk patients, make diagnoses, recommend the best possible medicines, and predict readmission cases. This analysis is based on the available data set of anonymous patient records and their symptoms. 

Near accurate diagnosis and better medicine recommendations will facilitate faster patient recovery. Moreover, it will eliminate the need for extraneous medications. Thus, a machine learning solution makes it possible to improve patient health at a minimal cost. 

5. Simplifies Time-Intensive Documentation

Data duplication is one of the major issues faced by organizations wanting to automate their data entry process. Well, this situation can be improved by predictive modeling and machine learning algorithms. With this, machine learning solutions can perform time-intensive data entry tasks. Thus, leaving your resources free to focus on core business functions. 

6. Improves Precision of Financial Rules and Models

Machine learning has a significant impact on the finance sector. Some of the common benefits of machine learning platforms include portfolio management, loan underwriting, and algorithmic trading. A report on ‘The Future of Underwriting’ by Ernst and Young suggests that machine learning software facilitates continuous data assessments for detecting nuances. Thus, helping in improving the precision of financial models and rules. 

7. Spam Detection

Spam detection was the earliest problem solved by machine learning solutions. Earlier email providers made use of rule-based techniques to filter out spam. 

However, with the advent of machine learning solutions, spam filters are making new rules using brain-like neural networks to eliminate junk emails. These networks can recognize phishing messages by evaluating rules across a massive network of computers. 

8. Efficient Predictive Maintenance in the Manufacturing Industry

Manufacturing firms have corrective as well as preventive maintenance practices in place. However, it has been observed that these methods are often costly and inefficient. This is where machine learning platforms can be of great help. They can help you create highly efficient predictive maintenance plans. Following these plans will minimize the chances of unexpected failures and even reduce preventive maintenance activities. 

9. Customer Segmentation and Lifetime Value Prediction

Customer segmentation and lifetime value prediction are among the significant challenges faced by marketers today. Sales and marketing units have enormous amounts of data from various channels such as website visitors, lead data, and email campaigns. Interestingly, machine learning solutions can offer predictions for incentives and individual marketing offers.  

Marketers can use machine learning algorithms to do data-driven marketing. For instance, using the data that represents a particular set of users' behavioral patterns will help businesses predict the probability of customers that will convert. Such tools trigger customer interventions to help you better understand customers and engage with them. 

10. Recommending the Right Product

Product recommendation is an essential part of any sales and marketing strategy. Machine learning solutions can analyze the purchase history and identify the products from your product inventory in which a customer is interested. 

The algorithms will identify hidden patterns among the items and will then group similar products. This process is known as unsupervised learning. Such a model allows businesses to make better product recommendations for clients, thus boosting product purchases and sales. 

Things to Consider While Purchasing Machine Learning Software 

1. Examine the Data Training Models 

You will need to understand what data was used to train the vendor’s models. It will help you determine if the information is fit for your data and also analyze the behavior you see on your network. 

2. How Frequently Is The Data Refreshed? 

It is essential to know how frequently the training data is refreshed. Moreover, you also need to analyze how the data set learns and evolves its detection abilities over time. Another important thing to be noted is how much data is required to adequately train the AI/ML engine. 

3. Performance Metrics

If you have narrowed down on a machine learning solution, a question arises. Can your vendor share the results of controlled experiments that detected anomalies? 

Well, if you need to determine if a vendor is using machine learning or simple algorithms. Now, how can this be known? Well, this can be accomplished with the metrics used to measure the performance of the vendor’s ML model. Such metrics can also help you gauge the accuracy of the model. 

4. Real-world Demonstration

Whenever you look for machine learning platforms, you should look for hard metrics. But, what if your vendor is unable to show any such data? You should always ask for examples of how their solution can make better decisions than your analysts. If you are convinced, then only should you move forward with the solution. 

5. Customizing Capabilities 

While your vendor may claim that a proprietary AI/ML implementation will solve all your data-related problems, you still need to know if the tool can be customized or not? 

If yes, what level of training would your engineers need to make those customizations. Moreover, can different models work on the same data or your data can work on models that come with a security product? 

6. How Flexible Is The AI/ML Implementation?

Another important aspect you need to consider is the flexibility of the machine learning solution you wish to purchase. Can it work with different data types, such as audio, video, log, and transactional data? If yes, can the data sets work together, or must they be separate? 

7. Application Updates and Upgrades 

You need to confirm whether you’ll have to pay incrementally or buy a new version of the application to get updates. Also, you need to know how your vendor distributes such improvements to customers and how tough it is to integrate them. 

8. Black Box Vs One-On-One Approach

While being a black box cannot be specifically categorized as a pro or con, you would still need to know if your vendor supports the latest AI/ML toolkits. Moreover, you also need to understand how your team will work with it. The next thing to address is if the tool will help practitioners learn about how data works and expand their understanding of data engineering. Or is it a black box solution that will make you dependent on the vendor to make any changes? 

9. How Does The System Detect Attacks?

Data is as important to machine learning algorithms as water is to fishes. Can your machine learning solution identify threats that it has never encountered before? The ability to detect attacks is something that you need to ensure and should not compromise on. 

10. Who Owns the Data?

The primary goal for AI vendors is not to sell but to gain access to as much data as possible to test and improve their algorithms. Thus, it is essential to understand what level of access your vendor will have to your data and systems. Moreover, you also need to know who will own the resulting metadata. 

Top 5 Machine Learning Software

1. TensorFlow

TensorFlow lets you build both statistical machine learning solutions and deep learning tools through its extensive CUDA GPUs interface. It has an open-source toolkit that you can use to create scalable solutions to process data. 

TensorFlow even provides support functions for various applications such as NLP and Reinforcement Learning. It is an ideal tool for beginners who wish to learn machine learning. 

2. Shogun

Shogun is an open-source machine learning software written in C++. It supports various languages like R, Scala, C#, and Ruby. Support Vector Machines, Hidden Markov Models, Dimensionality Reduction, and Linear Discriminant Analysis are some of Shogun's algorithms. 

3. Apache Mahout

Apache Mahout is an open-source machine learning platform focused on collaborative filtering and classification. The platform has been implemented on top of Hadoop and makes use of Map/reduce paradigms. 

It even provides expressive Scala DSL and a distributed linear algebra framework for deep learning computations. Moreover, it offers native solvers for GPUs, CPUs, and CUDA accelerators. 

4. Apache Spark MLlib

Spark is a powerful data streaming platform and provides several advanced machine learning features through its MLlib. It also provides a scalable machine learning platform with several APIs. This allows users to implement machine learning on real-time data.

Some of the algorithms supported by MLlib included Naive Bayes, Logistic Regression, Survival Analysis, Topic Modeling, and Decision Trees. 

5. Oryx 2

Oryx facilitates end-to-end model development for collaborative filtering, regression, clustering options, and classification. It makes use of Lambda Architecture for real-time and large scale machine learning processing. Oryx has been built on top of the Apache Spark architecture and involves packaged functions for building applications and rapid-prototyping. 

Emerging Machine Learning Trends To Watch Out For

1. AI Analysis for Business Forecasts

ML-based time series analysis is becoming a favorite of data scientists. This technique analyzes a series of data over time. When used correctly, it will analyze the data so that it will help managers make decisions based on their data. 

Using a machine learning solution to process complex calculations will boost your business and is a major improvement over traditional methods. When the AI network is adequately trained, it can capture features such as seasonality and cross-correlation in demand forecasting for retail.

The coming years will see a growing trend for applying recurrent neural networks for forecasting and time series analysis. The implementation of these networks will end up replacing traditional machine learning. For example, deep learning will forecast data such as exchange rates for currency with a surprisingly high degree of accuracy. 

2. Reinforcement Learning

Reinforcement learning (RL) is a specialized application of deep learning that uses its experiences to improve itself. Interestingly, it is effective to the point that it may be the future of AI and ML. 

No predefined suggestions are given to reinforcement learning agents. Instead, the AI starts out by acting completely randomly and eventually learns to maximize its reward through repetition. RL even allows the algorithm to develop sophisticated strategies. 

It is the best way to simulate human creativity in a machine. Moreover, the model can even be adapted to complete complex behavioral tasks. It’s an ideal solution for solving optimization problems.

Self-improving chatbots are one of the finest examples of reinforcement learning’s effect. A goal-oriented chatbot helps users solve a specific problem, such as booking tickets to an event and making appointments. 

A chatbot using reinforcement learning through trial and error can quickly become a fully functional automated assistant for customers. 

3. AI-driven Biometric Security Solutions

Bio-ID is no longer something you would only expect to see in sci-fi films. This emerging machine learning trend is one to keep your eye on. Machine learning’s efficient approach to gathering, analyzing, and processing large data sets can drastically improve biometric systems' performance. 

Running a comprehensive biometric system is all about performance matching and completing tasks quickly and accurately. This is a task where machine learning networks excel at. 

The reliability of AI-based biometric security is gradually increasing. For instance, a deep learning-based face anti-spoofing system allows you to secure face recognition solutions from any attempt to fool the system. 

4. Automated Machine Learning

Automated machine learning is being used to execute tedious modeling tasks that require weeks or months of work by data scientists. 

Automated machine learning systems run systematic processes on the raw input data and then choose the model that makes the most sense. The priority of such systems is to find a pattern in the input data. 

Major cloud computing services offer automated machine learning algorithms Google AutoML and Azure Automated Machine Learning are two famous examples. Other options include the open-source tpot, AutoKeras, and AutoGluon MLaaS platforms. 

5. Convergence of IoT and AI

Industrial IoT processes have tremendous potential. However, they are not generally efficient as they could be. Interestingly, this leaves plenty of room for AI algorithms to be applied to IoT data. 

This, in turn, will increase the efficiency and reduce downtime for various businesses through methods such as defect detection and predictive maintenance. The addition of ML and AI to manufacturing processes can mitigate IoT security threats. 

The current IoT trends reveal that businesses are slowly accepting the potential of ML. For instance, Rolls Royce partnered with Azure IoT Solutions and has implemented their cloud and IoT solutions. Interestingly, even Rolls Royce is taking advantage of their IoT devices to check their aircraft engines' health and keep their uptime at a maximum.