One of the most disruptive technologies to have emerged in years is machine learning (ML). Although it is a relatively new phenomenon, this subset of artificial intelligence (AI) is revolutionizing the customer care service and supply chain management industries.
Machine learning is that branch of artificial intelligence focused on models that continuously learn and improve by recognizing patterns in large data sets and acting on these patterns.
We can apply the knowledge gained from ML models on a broad scale without human intervention. However, we must remember that the data fed to these models must be of superior quality for the machine to derive an accurate output.
While machine learning is a diverse field, it is commonly used in risk management, performance analysis, automation, and trading. Hence, this article will walk you through the key players in machine learning and provide insights into their features.
AWS SageMaker
It doesn’t come as a surprise when one of the world’s largest Infrastructure-as-a-Service (IaaS) companies is also one of the companies at the forefront of machine learning operations.
Amazon Web Services (AWS) is an early pioneer of cloud computing and stores more data for enterprises worldwide than any other service provider in the industry. Due to this, AWS has access to a significantly large amount of data that they can utilize to train machine learning models.
The company’s ML suite, known as SageMaker, provides its users with a fully managed platform that is equipped with several tools, including:
- A training data set management feature known as GroundTruth
- An Autopilot that can automatically build and calibrate machine learning models
- A tool known as Data Wrangler helps streamline the data cleansing process and visualization
- A Studio that can be utilized to manage their ML models
- A Feature Store where users can upload and share their ML features
Because of these various features, AWS SageMaker is now one of the most extensive and comprehensive machine learning platforms capable of driving multiple ML applications.
IBM Watson Studio
The IBM Watson Studio is one of the most popular AI platforms today. Users can utilize this world-class technology to build machine learning models. A prominent feature of this platform is its accessibility.
Furthermore, the platform’s AutoAI feature enables businesses to deploy ML models without coding. These ML models can be deployed on IBM’s cloud, another server, or your servers. Additionally, this platform is excellent for inexperienced programmers as they can capitalize on sophisticated ML tech.
The platform also has ready-made features that can help speed up development for specific applications, and it supports both hybrid and multi-cloud environments.
Google Cloud Vertex AI
One of the top machine learning companies in the world is Google Cloud. A comprehensive package of AI, machine learning, deep learning containers, and TensorFlow services is provided by Vertex AI.
In addition to providing tools to apply throughout a machine learning pipeline, this range is ideal for developing specialized ML models. In Vertex AI, Watson-like functionality is also included, making it accessible to companies with less coding know-how.
An easy-to-use interface and helpful visualization tools make the tool accessible to all. Furthermore, because of the technical prowess of its employees, Google is at the forefront and is the first company to develop self-driving cars on open roads.
Dataiku DSS
Although Dataiku is a machine learning platform that we should look out for this year. This company, a machine learning startup, has proven it has the mettle to contend with the world’s top tech giants and has attracted reputable companies like Sephora, Unilever, GE, Ubisoft, L’Oreal, and others.
Dataiku’s Data Science Studio (DSS) is focused on centralizing ML operations to enable swift deployment of ML models. Additionally, the platform is integrated with data preparation and visualization tools that can help teams to prepare data sets for the initial training phases.
The platform is scalable and fully equipped with self-service capabilities, dashboards, and drag-and-drop interfaces that can help streamline processes for inexperienced programmers. Additionally, collaboration is made easier with this platform as it has tools that enable easier sharing.
Databricks
Databricks is a startup that operates on third-party platforms as bases, such as AWS and Microsoft Azure. It also integrates with several business intelligence tools.
Although this company does not provide users with no-code programming, it can support several coding languages, thus increasing its accessibility. Also, data lakes and data warehouses are combined with an open cloud to make the platform flexible and cost-efficient.
Conclusion
Companies with machine learning have become one of the fastest-growing businesses today. Hence, it is without a doubt that these companies will shape the future of technology as Artificial Intelligence and Machine Learning transitions from its role as an advantage to a daily necessity. Because of this, we need to understand the trajectory of ML as it will help us navigate the future.
