Why AI Development Tools Matter?

Why do AI development tools matter? In this article, I am going to explore the reasons for that as well as to discuss some of the great tools for AI development you can leverage for building software.

In this article

1. AI development tools facilitate machine learning projects
2. Cloud-based AI development tools expedite projects
3. AI development tools optimize human resources
4. The solution: Using AI development tools/platforms to optimize the effort of your team
5. Frequently Asked Questions on AI development tools

1. AI development tools facilitate machine learning projects

AI development tools like Scikit-learn can facilitate machine learning projects. Using software development tools like Scikit-learn can expedite an artificial intelligence (AI)/machine learning (ML) project by removing the ML algorithm development work. Let’s see how.

The requirement: Using ML in conjunction with other AI capabilities

You often use ML in conjunction with other AI capabilities like computer vision, natural language processing (NLP), speech recognition, or robotic process automation (RPA). This involves creating machine learning models relevant to the business problem you tackle.

You use machine learning algorithms along with large input data sets to create an ML model. What does that mean?

The deeper requirement: Using a machine learning algorithm

Which ML algorithm should you use? What kind of machine learning methods should you use? This largely depends on your project.

E.g., you might have “labeled” data sets. These contain both the questions and answers. You can use a “supervised learning” algorithm in this case.

If you have “unlabeled” data sets, then you need to use an “unsupervised learning” algorithm. Do you plan to use a “trial-and-error” approach? You will need to use a “reinforcement learning” algorithm.

Whether you intend to solve classification, regression, or clustering problems influences your choice of the ML algorithm. A few popular machine learning algorithms are as follows:

• Naïve Bayes Classifier;
• K-Means Clustering;
• Support Vector Machine;
• Linear Regression;
• Logistic Regression;
• Artificial Neural Networks;
• Decision Trees;
• Random Forests;
• Nearest Neighbors;
• Dimensionality Reduction;
• Gradient Boosting;
• Principal Component Analysis (PCA).

What does it take to develop an ML algorithm? An involved software development process

It takes considerable effort to develop an ML algorithm. Such a project involves the following:

Get a complimentary discovery call and a free ballpark estimate for your project

Get Started

Trusted by 100x of startups and companies like

• You need to hire developers with skills in programming languages like Python and experience in ML algorithm development.
• Your team needs to learn about the algorithm thoroughly.
• The project requires thorough data collection and data preparation activities. This involves exploring the data and improving its quality. Your team needs to undertake feature engineering. Finally, you need to split the data into training and testing data sets.
• You need to design and implement a robust information security solution.
• Your team needs to create the pseudocode.
• You need to code the algorithm.
• The project team needs to train and test the algorithm.

So far, we talked about the steps for developing commonly used machine learning algorithms. You need to contend with higher complexities when developing special-purpose algorithms, e.g.:

• Generalized linear models;
• Counterfeit neural systems.

Think of creating neural net models. Alternatively, think of detecting fake currencies using counterfeit neural systems. You need to spend more effort developing complex algorithms.

Scikit-learn: Already contains many machine learning algorithms

What if we provide you with the standard implementation of important machine learning algorithms that are already tested? You don’t need to develop those algorithms from scratch anymore. It saves considerable effort.

Scikit-learn is a popular Python library for ML and AI development. It already contains the tested and standard implementation of several well-known ML algorithms. Check out the following:

• Scikit-learn Naïve Bayes Classifier;
• Scikit-learn K-Means Clustering;
• Scikit-learn Support Vector Machine;
• Scikit-learn Linear Regression;
• Scikit-learn Logistic Regression;
• Scikit-learn Decision Tree.

You can save time by using Scikit-learn since you don’t need to develop algorithms.

Scikit-learn is one example only. There are many other Python libraries for ML and AI development, e.g.:

• Numpy;
• Scipy;
• Theano;
• TensorFlow;
• Keras;
• PyTorch;
• Pandas.

Python isn’t the only language to provide such development tools either. Java offers libraries like Weka, JavaML, Deeplearning4j, etc. Programming languages like C++, R, and JavaScript also offer such ML and AI software development tools.

Summary: Important programming languages like Python offer useful libraries like Scikit-learn that already contain standard implementation of ML algorithms. They significantly expedite the software development process.

2. Cloud-based AI development tools expedite projects

Using cloud-based AI software development platforms can expedite your project significantly. You can use cloud-based AI software development tools and platforms to great advantage. Let’s see how.

The requirement: Processing vast data sets to drive AI-powered transformation of business processes

AI has much to offer in your journey to making business processes smarter. Even if you are undertaking academic research projects, AI can increase your odds of success. However, artificial intelligence tools need vast data sets to make a tangible difference.

The quality of the training data sets makes a big difference to the effectiveness of AI tools. You need to progressively find higher-quality data sets, which you will feed to your proposed artificial intelligence tool. Such tools can then progressively improve their effectiveness.

This holds true for all kinds of AI-powered software systems, e.g.:

• Customer intelligence management systems;
• Digital assistants like Google Assistant;
• A production-grade computer vision system;
• A search engine like Bing;
• An insurance analytics system;
• A self-learning knowledge base for customer support;
• AI-based advertising technology solutions;
• An optical character recognition system;
• Advanced analytics systems for risk management.

The challenge: Managing vast data sets

Managing organizational data in large enterprises is complex enough. For AI/ML development, you will likely gather data from various sources outside your organization.

You need to collect raw data. Subsequently, you need to undertake several data preparation steps. You will need to maintain different versions of vast data sets.

Your data management processes need to maintain traceability. You need to take care of data back-ups, furthermore, you need to secure data from malicious actors.

You might need a data management system that supports distributed computing. All of these can be challenging when you deal with vast data sets.

“But then, isn’t storage cheap and easy to manage?”

You hear everywhere that storage is cheap and easy to manage. That’s certainly true. However, managing the other aspects isn’t easy.

You need to manage the server and network infrastructure. This includes managing the hardware settings, patch management, back-ups, etc. You need to take care of multiple tasks related to information security.

Software engineers in your organization will require development tools, and DevOps engineers will need DevOps tools. You need to take care of containerization and orchestration. Add all of these up, and it’s no longer just managing storage!

Using a cloud-based AI platform helps to manage complex business environments

Reputed cloud-based AI platforms like Microsoft Azure AI platform, Google Cloud AI platform, and Amazon AI services make great use of their cloud computing capabilities. They offer powerful software tools to make many tasks easier. Consider the following:

• Managed Cloud Services Providers (MCSPs) use high-quality hardware like SUSE Linux Enterprise Server. You can expect higher performance.
• Cloud platforms offer self-service provisioning.
• They manage servers, networking, operating systems, and runtime environments. You can focus on application development.
• Their tools for containerization and orchestration make life easier for your software engineers.
• MCSPs support distributed computing. You can use the Apache Hadoop framework to manage big data.
• MCSPs offer integration features for web and mobile developers. Web, Android, and iOS developers can easily integrate your AI/ML applications into their apps.

Summary: You can use a cloud-based AI platform to expedite your AI/ML software development project.

3. AI development tools optimize human resources

You can optimize your human resources by using AI software development tools. Let’s understand how.

The requirement: AI/ML development projects require many team members with niche skills

By their very nature, AI/ML development projects require specialized skills. You plan to use AI and ML to optimize the use of human intelligence.

You want skilled human resources to focus on tasks that require their attention. To do that, you want AI/ML systems to take up several tasks that earlier required human intelligence. However, you need specialized skills to get to that stage.

Depending on the nature of your AI project, you might need the following roles:

• Business analysts;
• AI/ML developers;
• Testers;
• DevOps engineers.

Do you plan to develop web and mobile apps? You need the following roles for this:

• UI designers;
• Web developers;
• Native Android and iOS developers;
• Testers;
• DevOps engineers.

Are you trying to build a robust system powered by AI/ML? Perhaps, you are trying to solve real-life problems with the help of massive data available around you? You might need the following additional roles:

• Data scientists;
• Data analysts with the data science expertise;
• Big data engineers;
• Big data architects.

Many of these roles involve niche skills. You want to optimize the workload of these roles so that they can focus on what’s important.

The desired state: AI/ML developers with niche skills should focus on the tasks that require skills

You would want team members with niche skills to concentrate on tasks that require those skills. They should not have to spend time on mundane tasks. Consider the following examples:

• Data scientists should work on important data preparation tasks. They should not have to spend time on tasks like data backup.
• A big data architect should design how your team will use the Apache Hadoop framework. The architect should implement the distributed computing tools effectively.
• ML developers should iterate the deep machine learning model to improve its effectiveness. They should be able to use standard tools to create the model. E.g., the ML developers should be able to use a standard implementation of the Artificial Neural Network (ANN) algorithm.
• Data scientists in your team should analyze the outliers in the model. They should find out whether the outliers originated due to errors in the data. In that case, they should review the input data sets. They might need to revisit the data preparation process to resolve the data quality issues. Data scientists shouldn’t have to worry about adding more servers to meet the scalability demands of your project.
• AI/ML developers in your team should be able to focus on improving the ML model. They should have time to review the model and its effectiveness. They should be able to reiterate the training process with the necessary adjustments. In this process, they should not need to “reinvent the wheel”. E.g., they shouldn’t have to implement linear algebra routines from scratch. Instead of that, they should be able to use standard Python NumPy linear algebra functions.
• AI/ML developers should not have to worry about code completion for standard routines. They should be able to use standard libraries offered by programming languages like Python.

The solution: Using AI development tools/platforms to optimize the effort of your team

Numerous AI development tools and platforms offer relevant capabilities that allow you to spend time on what really matters. Consider the following examples:

A. Microsoft Cognitive Services

While the Microsoft Cognitive Toolkit is no longer actively developed, the “Azure AI Services” offers powerful features. It is a part of the Azure AI Platform.

“Azure AI Services” includes APIs for emotion recognition, computer vision, speech recognition, and understanding natural languages. Your skilled software engineers can focus on what’s important.

B. Infosys Nia

Infosys Nia is an AI platform offered by EdgeVerve, a subsidiary of Infosys. You can use it for various purposes like data management, developing ML models, and operationalizing ML models.

The platform helps you to digitize documents and images, which helps you to extract insights. Infosys Nia Prediction Server helps you to stream predictions. The cloud capabilities of Infosys Nia helps with the self-service provisioning of resources.

Summary: You can use AI development tools and platforms to optimize the effort spent by your skilled human resources during an AI development project.

Frequently Asked Questions on AI development tools