Why Should You Use Open Source Machine Learning Tools Now

Open-source machine learning tools are quickly becoming a staple of every data science team. The convenience and the fast pace of release make using open-source tools an easy decision for machine learning projects. From Google to OpenAI, there is increasing interest in making the software accessible. Since AI systems learn by training, using readily available machine learning platforms is a move that can save time and costs. In this blog post, we’ll take a look at how open-source machine learning tools can help your team get started with data.

What is open-source machine learning?

Machine learning is a branch of artificial intelligence that creates algorithms and models to understand data and make predictions. It‘s a hot topic for companies that need to process large volumes of data to glean insights about customers, products, or the world around them. Machine learning tools are available for purchase as individual software programs, but they can also be found in open-source packages, such as Tensor Flow, OpenAI, Pytorch, and so on.

Open-source means the software is free and readily available for developers to use. The benefits of open-source machine learning tools are many. Open-source tools give you flexibility and control over how your data is processed. You can easily customize these packages to suit your needs – even if you have no coding experience. You can also use them on any major operating system (Windows, Linux).

What’s the Hype Around Open-Source Machine Learning Products for Data Science?

Data science is the new business frontier. Data is exploding and data scientists are in high demand. To meet this demand, open-source machine learning tools are becoming more and more popular.

There are many reasons why these tools have become so appealing for businesses. They offer access to a wide range of different algorithms that would otherwise be inaccessible to small businesses. They’re also easier to install, maintain, and use on the fly.

Machine learning helps data scientists apply complex calculations to big data. The ability of processing and constantly learning from previous knowledge proves highly valuable for data scientists building models. Open-source packages in Python and R help to streamline workflows. Machine learning helps data science teams automatically produce models that can analyze complex data and deliver faster and more accurate results in a more efficient and time-saving way.

According to Salesforce, open-source tools are behind the data-driven and data-generating technologies. The amount of data keeps growing. These tools not only reduce the time you spend getting started with data science but also make it more likely that your team will adopt an open-source mentality in the future. Let’s review some reasons to use open-source machine learning tools.

More innovation

Open-source tools foster innovation because basically, you can tinker and customize them according to your needs, therefore expanding the usage and features of existing tools. By opening machine learning tools, tech companies are driving creativity and innovation. The more people working on machine learning tools, the more likely disrupting ideas will appear.

Companies starting their machine learning projects benefit from open-source tools to practice and train their models before moving on to enterprise-level tools.

Solves problems faster

Open-source projects have behind a wide community of data scientists and engineers sharing pre-trained models, data sets, and support. You can readily find ready-trained image recognition models to train your classification tool. An open-source machine learning thus can help your model transfer the learning from another model. For instance, you can transfer capacities from a model that learned to recognize tables to the model you want to recognize desks.

Continuous evolution

Google is one of the companies that is supporting open-source projects for machine learning, with its DeepMind technologies, and it regularly open-source machine learning projects. One of the reasons they give is to encourage ongoing research on machine learning. Depending on your ML project, you can benefit from pre-trained models and open datasets, saving you from having to build your model from scratch. Specially, you can avoid having to gather massive datasets. Because of its availability and ease of use, the community of users is constantly improving open-source machine learning tools.

Use Cases of Open-Source Machine Learning

The machine learning of today is based on the concept that computers can learn without human intervention, by being exposed to new data and adapting independently. This results in a myriad of applications you may be familiar with:

• Self-driving cars
• Online recommendation offers
• Sentiment analysis
• Fraud detection

Most industries that deal with massive amounts of data recognized the value of machine learning and were heavily transformed by using machine learning, especially open-source.

Finance: Banks and financial institutions use machine learning technology to identify trends and insights and to prevent fraud. Machine learning capabilities can identify high-risk indicators as well as investment opportunities.

Cyber Security: IoT and distributed environments are expanding the attack surface while generating large amounts of data to scan and monitor for threat indicators. Machine learning software helps security analysts identify indicators of compromise and attackers pattern behaviors. This technology also can help reduce false positives as it learns from previous knowledge and can filter and prioritize alerts.

Healthcare: Thanks to the popularity of wearable devices and sensors, machine learning is a growing trend in healthcare. The technology supports the devices assessing a person’s health in real-time, helping doctors to identify trends and issues that can improve treatment.

Why Open-Source Machine Learning Tools Are Essential for Data Science Teamwork

Data science is mainly about teamwork as it combines different skill sets (data, statistics, technology, etc). As such, open-source machine learning tools can become almost another member of the team, easing manual tasks and streamlining workflows. Here is why we think open-source machine learning is key for any data science team:

Open- source is more flexible: one of the main differences with proprietary software is the ability to customize the software giving more control over the projects you develop. Since open-source software can work with a variety of data formats, it enables your team to avoid vendor lock-in.

Empowers employee acquisition and retention: being skilled in open-source means professionals have transferable skills, and large companies contribute to open- source machine learning projects to attract and retain top talent. That is why these technologies are standard in universities and industry circles.

Open-source provides more security: the popularity and community-based approach of open-source contributes to identifying potential vulnerabilities and fixing them quickly.

Final thoughts

Machine learning is an essential technology to solve scientific and technological challenges and only can do it effectively with the help of open-source tools. Open-source machine learning tools can provide data scientists with better accuracy for reproducing results, and accelerate the research process.