Machine learning enables organizations to identify potential risks and explore profitable opportunities quickly. The massive amounts of data, growth of powerful processing, and affordable data storage have all fuelled the growth of machine learning jobs. According to Market Research Future, the global machine learning market is projected to grow from $7.3B in 2020 to $30.6B in 2024, attaining a CAGR of 43%. If you are searching for the top machine learning companies to work for, here is a list of the best ones out there.

1. Amazon AWS

The list of the top machine learning companies to work for begins with Amazon. Amazon Web Services (AWS) is a subsidiary of Amazon and provides on-demand cloud computing platforms and APIs. It was launched in 2006 and is currently the world's largest provider of Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and private cloud services. The AWS SageMaker is a fully managed service that provides data scientists the ability to build, train and deploy machine learning models quickly.

AWS is headquartered in Seattle, Washington and has about 25,000 employees located in 32 office locations across 19 countries. 

Notable Projects

1. Amazon S3

Amazon Simple Storage Service or Amazon S3 is used to store and retrieve large amounts of data (up to 5 TB) from the cloud. It is designed for archival and online backup of data and application programs. S3 provides users with complete control over their data’s accessibility.

2. AWS Lambda

Amazon Lambda allows users to run code without using a server. It executes the code only when the user needs it and scales itself automatically. Users only have to pay for their code computing time.

We will now look at the next machine learning company to work for.

2. Databricks

Databricks was founded in 2013 by the original creators of Apache Spark. The company is rooted in open source and provides a unified platform for data and AI on lakehouse architecture. Databricks is a fairly young company, but it has grown exponentially in just a few years. It is currently ranked #5 on Forbes Cloud 100 list.  If you are interested in a pure-play data science and machine learning organization, Databricks is the right place for you. 

Databricks is headquartered in San Francisco, California and has about 1,650 employees in 16 office locations across 11 countries.

Notable Projects

1. Databricks Unified Analytics Platform

The Databricks Unified Analytics Platform helps build reliable, performant, and scalable deep learning pipelines. It provides a single platform for data scientists to handle data preparation, exploration, model training, and large-scale predictions. Deep learning applications can be built, trained and deployed with ease using this platform.

2. AutoML on Databricks

AutoML on Databricks automates machine learning pipelines and various steps of the data science workflow to provide data scientists flexibility and control over their data. Data scientists can also track and visualize thousands of experiments using open source or Managed MLflow.

Let us look at the next Machine learning company that you can be a part of.

3. IBM

IBM is one of the oldest and most respected companies in the technology industry. It is also one of the pioneers of AI and machine learning. Due to its massive influence on technology in the early years, IBM is also known as the "Big Blue". 

IBM is headquartered in Armonk, New York, and has offices in 174 countries. IBM has one of the largest workforces in the world, with more than 350,000 employees.

Notable Projects

1. IBM Watson Machine Learning

IBM Watson Machine Learning enables data scientists and developers to accelerate AI and machine-learning deployment. With its open and extensible model operation, Watson Machine Learning provides continuous learning and automatic retraining of models.

2. IBM SPSS Modeler

IBM SPSS Modeler is a data mining and text analytics software application used to build predictive models and conduct other analytic tasks. One of its main features is that it eliminates unnecessary complexity in data transformations and eases the use of complex predictive models. 

The next machine learning company is TIBCO.


TIBCO was founded in 1997 and specializes in data integration, data management and analytics. TIBCO Data Science is the primary machine learning product of TIBCO. It comes in four different versions: TIBCO Data Science - Statistica, TIBCO Data Science - Team Studio, TIBCO Data Science for AWS, and TIBCO Data Science for Students and Academics.

TIBCO is headquartered in Palo Alto, California, and has about 4,200 employees across 22 countries.

Notable Projects

1. TIBCO Data Science Team Studio

TIBCO Data Science Team Studio is an enterprise analytics platform that allows data scientists and data engineers to build machine learning workflows using minimal code. It also allows data scientists to collaborate and share insights with the rest of the organization.

2. TIBCO Streambase

TIBCO Streambase is an event processing platform that enables organizations to build and deploy event-driven applications rapidly. It has multiple ways to integrate with machine learning to utilize models discovered during real-time processing.

5. Prolifics

Prolifics is an IT service management company focused on digital consulting, engineering and managed services. They provide a number of data analytics, cloud and quality assurance services to various industries, including retail, healthcare, insurance, banking, and the public sector.

Prolifics was founded in 1978 and was acquired by SemanticSpace Technologies in 2008. It has over 1,200 employees with offices located in North America, Asia and Europe.

Notable Projects

1. Data Science in a Box

Prolifics’ Data Science in a Box is a powerful solution for AI and machine learning analytics that provides a fully managed, turnkey offering. It creates clean, structured and governed data from any source and then applies AI/ML for advanced and predictive analytics.

2. BA360 Accelerator

Prolifics’ BA360 Accelerator features powerful machine learning capabilities to automate the entire software testing lifecycle. Testing is done more consistently, more accurately, with more added value and at a lower cost. The time taken for manual testing test cases is reduced by 98%.

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