What Are the Most Popular AI and ML Tools?

When you choose a tool for AI or Machine Learning, you have to make sure that it is the best tool for the specific project you are working on.
By Boris Delovski • Updated on Nov 16, 2022

If you are looking to implement AI within your company, you may have some questions about some of the tools available. The good news is that I'm here to help and offer some insider information on the most popular Artificial Intelligence (AI) and Machine Learning (ML) tools. For more introductory information on Artificial Intelligence and Machine Learning, you can check out our previous article explaining the difference between AI and ML. 

 

Throughout this article, I'll cover tools for data storage, cleaning, analysis, scaling, and modeling, as well as tools for version control and documentation. I'll also cover tools that can help you with the testing and monitoring of your Machine Learning system. Don't forget that when you choose a tool for AI or ML, you have to make sure that it is the best tool for the specific project you are working on.

 

What Are Popular Tools for Data Storage? 

Data is stored in databases which, depending on your organization and projects, could be relational databases, document stores, key-value stores, column stores, data warehouses, or data lakes.

 

What Are Relational Databases?

You can only use relational databases if your data is fairly complete and is highly structured.

For example, when you are working on a small project like the automation of internal processes. Some great examples of relational databases include MySQL, PostgreSQL, and SQL Server

Relational databases are great for structured data and data that can be easily stored and retrieved via SQL queries. Additionally, relational databases follow the ACID model, which stands for Atomicity, Consistency, Isolation, and Durability as guiding principles, making data actions more reliable.

On the other hand, relational databases are not very useful if your data is unstructured. The reason for this is they can be complicated to scale and the fixed schema is difficult to change.

 

What Are Document Stores?

Now, you may wonder what solutions there are for your unstructured data. Document stores may be your answer. These are non-relational databases that store unstructured data in formats like JSON. Document stores have very high availability with a fast writing speed and, compared to relational databases, they are very flexible.

You can use document stores when you need a flexible schema. However, keep in mind that these databases are typically not ACID compliant like relational ones. MongoDB, which is actually ACID compliant, and Couchbase are both examples of document stores.

 

What Are Key-value Stores?

Key-value stores are also non-relational databases that store data in key-value pairs as an associative array. You can use key-value stores if you need a flexible schema as well as if you are expecting your data to be frequently accessed and rarely updated.

Great examples of this may include reviews, user profiles, or recommendations. This type of database is highly flexible and scalable with a high read performance. However, it can be difficult to query and edit values in a key-value store. It is also important to keep in mind that not everything can be represented as key-value pairs.

Some great examples of key-value stores are Redis and Memcached.

 

What Are Column Stores?

Column stores are non-relational databases that are dynamic and column-oriented. You may use column stores when dealing with data warehousing or large scale projects.

 

Similar to other non-relational databases, column stores are also easy to scale. The advantage of column stores over other non-relational databases is that they make big data easier to explore because it is simple to update in bulk. One of the disadvantages of column stores is the difficulty of trying to make updates to individual records. Therefore, they are not appropriate for transactional data.

Some examples of column stores tools include Cassandra and HBase.

 

What Are Data Warehouses?

Data warehouses are relational databases that function as a central repository of integrated data from one or more sources. You may wish to use data warehouses when you are running larger queries for reporting or analysis.

Compared to transactional databases, data warehouses are more suitable to run big queries. However, they are much less useful if you are simply handling small updates or inserts.

Redshift and Snowflake are both examples of data warehouses.

 

What Are Data Lakes? 

Data lakes are centralized repositories that grants you storage of all your structured and unstructured data at any scale. You can utilize data lakes when you want to have a central, unified view of your data from multiple external and internal sources.

Data lakes can significantly increase data productivity because they enable you to harness and analyze more data from a greater number of sources in much less time. However, they are very complicated to properly implement as they have defined mechanisms to classify and secure data.

 

What Are Popular Tools for Data Cleaning, Analysis, and Modeling? 

The processes of data cleaning, analysis, and modeling are performed in order to gain insight from your data in order to make optimal decisions within your company and is a critical part.

 

First of all, the environment that you work in is important, as it may enable the reproduction of documentation that can be easily shared within your organization. Currently, Jupyter notebook is considered the default environment for building, visualizing, and collaborating on machine learning projects. This is largely due to the fact that Jupyter notebook is able to combine live code with text, outputs, and other media.

 

For multi-user notebooks, you can use JupyterHub. There are several MLaaS platforms that offer Jupyter notebook-based interfaces. A great example of this is Google Colab.

 

What Are Popular Tools for Data Cleaning and Analysis? 

Once you have decided on an environment to work in, you will have to get to the next step of data cleaning, analysis, and exploration. This part carries a lot of weight due to the fact that data analysis, done correctly, has been shown to increase business performance significantly by separating signal from noise. You can learn more about the weight of data analytics in our recent article that covers the differences between data analytics, predictive analytics, and data science.

 

In terms of tools, Python is a widely popular open source programming language for analytics and machine learning. At this point, you have probably heard about the big debates surrounding Python or R.

R is a programming language that is similar to, but older than, Python. R is best known for its use in academia. R has a smaller user base than Python, worse memory management, and a much less flexible end-user license.

 

While there are a variety of advantages and disadvantages to both Python and R, Python is by far the most popular choice for the vast majority of business use cases. Python also offers access to tools like Pandas and NumPy, which are open source Python libraries used mostly for data processing and numerical computing.

 

Once you have extracted key insights from your data, you will need to communicate it with others too. The best way to do this is through data visualization. The human brain functions in a way that makes charts and graphs much easier to understand as opposed to statistical reports and cluttered spreadsheets.

Matplotlib is a visualization tool in Python that is very flexible, if not so user-friendly. Seaborn is another tool based on Matplotlib that has more user-friendly APIs around common tasks. There is also Bokeh, which offers web browser friendly visualizations in Python. Finally, there is D3.js which represents the gold-standard of web-visualization and, because of its high flexibility, it is considered to be the most popular at the moment. However, one downside is that using it requires writing Javascript code.

 

What Tools Are Popular for Modeling? 

Next in line is the modeling process. Once you have extracted everything from your data, you may want to try to predict future outcomes. For classic machine learning, the most popular tools include Scipy and Scikit-learn.

Scipy is a scientific computing library that is especially useful when it comes to image manipulations. On the other hand, Scikit-learn is the most popular ML library in Python because it has implementations for a variety of popular algorithms. Scikit-learn was built on top of Scipy and NumPy

For deep learning modeling, some of the most popular tools are TensorFlow, Keras, Theano, and PyTorch. TensorFlow is a low-level frame that was created at Google. Its primary use is creating and training deep neural networks. It is an open source software that is highly flexible and machine learning enthusiasts know it well, however, it is quite complicated to use.

Another tool that you can use is Keras, which is a high-level API that can be used with either TensorFlow or Theano. Unlike TensorFlow and Theano, Keras is significantly easier to use as a beginner. Unfortunately, it is much less flexible. Theano is mostly used in research but has not been maintained since 2017. Due to the hype around TensorFlow, Theano has not been very popular recently. Lastly, there is PyTorch, which is an open-source deep learning framework based off of Torch, that uses a lower level API than Keras

Once you have decided on a data modeling tool, you are ready to unlock the true power of your data!

 

What Popular Tools Are Used for Data Scaling? 

Data scaling is an essential step you need to take before building a machine learning model. Due to its significance, data scaling deserves a separate section because it can often determine whether you will have a good machine learning model or a weak one. After all, a machine learning algorithm just sees numbers. Where you and I can see a distinct difference between 10 milliliters and 10 dollars, an algorithm often cannot. Furthermore, unscaled data can result in the machine learning process failing. ML models are usually trained on very large amounts of data that require specific data processing tools. For example, Apache Spark, or Databricks, Apache Hadoop, Amazon Kinesis, and AWS Glue.

Apache Spark is an open source and distributed, general purpose cluster-computing framework. Apache Hadoop is a distributed computing framework which was designed with the goal to split up, store, and query data across multiple low-budget servers. Amazon Kinesis is an AWS service for processing streaming data. While AWS Glue is a service for doing extract-transform-load, or ETL, operations at scale. Whichever tools you have the intention to use, keep in mind that data scaling is a critical step in ML pre-processing.

 

What Popular Tools Are Used for Version Control and Documentation? 

Why do you need version control and documentation? For example, let us say your data scientists may want to go back to a previous code version because the current version decreased in performance. Or, let’s say that someone else on your team wants to troubleshoot the code while the person who wrote it is on vacation. These are both great examples of why you need version control.

Combined with documentation, version control helps with reproducibility and communication within any project. Git is by far the most popular distributed version control system, or DVCS, that currently exists. You can use Jupyter Markdown for your documentation, as well as independently for client-facing projects.

 

What Popular Tools Are Used for MLops: Model Deployment, Monitoring, and Management? 

MLOps is about managing any risks associated with machine learning. On the one hand, an ML system with no testing or monitoring is very risky, even though making changes to it is very easy. On the other hand, an ML system that is intensively tested and monitored is very safe, but trying to make changes to it is time-consuming and complicated.

MLOps is a very new field, so you should expect many vendors to enter the market in the next few years. For now, some MLOps tools to consider are TensorBoard and MLFlow. TensorBoard profiles only TensorFlow programs by tracking and visualizing model metrics. MLFlow is an open source platform for machine learning life cycles. Other MLOps tools that you may want to look into include Algorithmia, and all-in-one solutions like Amazon SageMaker and Azure ML.

In this article, I've shown you some of the more popular tools that, once mastered, will allow you to play with your data and train the right models to get the most valuable insights from your data at any time. This will, in turn, empower your forecasting power. All of these AI and ML tools have the final aim to upgrade your business-related decisions, potential, and revenue.

 

 

Boris Delovski

Data Science Trainer

Boris Delovski

Boris is a data science trainer and consultant who is passionate about sharing his knowledge with others.

Before Edlitera, Boris applied his skills in several industries, including neuroimaging and metallurgy, using data science and deep learning to analyze images.