Top Machine Learning Websites Useful For Business

Machine learning companies have risen to prominence in enterprise IT during the last few years. Why? The necessity for software that can learn on its own without human intervention has been recognized by business leaders. Many corporate applications now have machine learning (ML) capabilities. Artificial intelligence is used in everything from recommendation engines to medical diagnostic software to cybersecurity tools to self-driving cars (AI).

As machine learning has become more common, many companies form in-house data, science teams. Many of these teams analyze business data to get valuable insights, while others build machine learning capabilities into their products or utilize complicated algorithms to solve industry-specific problems.

Organizations usually want a general-purpose machine learning platform that can meet various requirements for these applications. The ten companies on this list offer machine learning solutions and should be evaluated if you need ML software.

How to select a machine learning vendor?

Machine learning systems have a wide range of capabilities, and no single ML tool will be ideal for every use scenario. The selection process should involve a comprehensive examination of your company’s present and future needs and the identification of the best fit. Here are a few questions to consider:

• Who will use artificial intelligence software? Is your team made up of skilled business analysts or expert data scientists? Or maybe both? What languages and technologies do they know, and which platforms are they most likely to master quickly?
• How do you plan to put your solution into action? Some machine learning tools are hosted on public cloud platforms, while others are delivered as software as a service and may be deployed on your servers. Choose the option that best meets your security and governance needs while also providing the lowest total cost of ownership.
• What is the location of your data? Make sure the machine learning platform you choose can ingest data from your sources. If you presently store a significant percentage of your data on a particular public cloud, employing an ML service that operates on the same cloud could be a good fit.
• Do you require data cleansing, preparation, and administration functionality? Some platforms cover the entire process, while others focus on machine learning. Consider the tools you now use and the platform that will work best with your current workflow.
• Do you need continuous integration (CI), deployment (CD), or MLOps capabilities? Some of the technologies listed below are more suited to a modern DevOps environment than others. Look for a system with processes that match the way your team operates.
• Are you willing to pay a more excellent price for quicker results? Machine learning platforms with sophisticated automation tools, templates, and user-friendly interfaces are often more expensive, but they may be worth it if your ML project yields substantial insights faster. You must figure out what the ideal cost-to-productivity ratio is for your company.

With these queries in mind, here are the top 6 machine learning providers today, along with their advantages and disadvantages:

6 Best Machine Learning Websites

• TIBCO

TIBCO is a software company that specializes in data integration, data management, and analytics. It was formed in 1997. Among the company’s clients are Caesars Entertainment, Mercedes-AMG Petronas Formula One Team, Bayer, Campari Group, General Mills, JetBlue, NASA, Panera Bread, and United Airlines. It is a Palo Alto, California-based privately held corporation.

The company’s principal machine learning solution is TIBCO Data Science. Tools for data preparation, model creation, pre-built templates, version control, auditability, AutoML, integrated Jupyter Notebooks, etc. It also combines with TIBCO Spotfire, the company’s core analytics platform, which features machine learning.

Statistica, Team Studio for AWS, and Students & Academics are the four editions of TIBCO Data Science. Statistica and AWS both provide free trials. Online pricing for the Students & Academics edition is available, and pricing for the other editions is available upon request.

Pros

• TIBCO’s data integration expertise makes importing data from other sources simple.
• The platform has excellent data preparation capabilities.
• The tools are commended for being simple to use.

Cons

• The TIBCO tools can be costly, particularly for smaller firms.
• Some clients complain that TIBCO’s software is not updated as regularly as they would like.
• TIBCO does not support CI/CD or MLOps.

• Amazon Web Services

Amazon Web Services (AWS), which was launched in 2006 and is the world’s top provider of infrastructure as a service (IaaS), platform as a service (PaaS), and private cloud services, was an early pioneer of cloud computing. According to analysts, the company, owned by Amazon, had $10.8 billion in the second quarter of 2020 and $21.0 billion in the first half of the year, accounting for about one-third of the cloud infrastructure business. Many prominent organizations store a percentage of their data on AWS, providing them with an advantage when looking for a machine learning provider.

The core of Amazon’s machine learning services is its SageMaker product line. The SageMaker Ground Truth tool for organizing data sets, the SageMaker Studio IDE, SageMaker Autopilot for designing and training models, Augmented AI for human evaluation of predictions, and many more features are included. Some of the most well-known machine learning users are among its clients, including Intuit, CapitalOne, Siemens, FICO, Kia, Formula One, PWC, Tinder, Yelp, the NFL, Netflix, and Pinterest.

AWS offers a free tier for enterprises just getting started with machine learning for the first two months. The pricing is then decided by the services utilized, the data center’s location, the instance size (the computing resources used), and the number of hours. All pricing is available on the website, and AWS offers a tool for calculating total costs.

Pros

• Companies who already store data in AWS or use other AWS services (as many do) may find that using AWS for machine learning is more convenient.
• AWS offers a one-stop-shop for data scientists, allowing them to accomplish everything they need in one place.
• The upfront pricing lets you know exactly how much everything will cost, and you just pay for what you use.

Cons

• You must track how much you utilize AWS services or risk being charged an unforeseen price.
• Some users believe SageMaker’s documentation isn’t as thorough as it might be.
• The service requires substantial data science knowledge and coding experience; it lacks the drag-and-drop interface offered by numerous other ML providers.

• SAS

SAS Institute, sometimes known as SAS, is a significant analytics software provider worldwide. It is, in fact, the world’s largest privately held software seller of any kind. It began as a statistical analysis programme at North Carolina State University in 1976 and is presently headquartered in Cary, North Carolina. In 2019, it produced $3.1 billion in revenue and employed over 14,000 employees.

Although several SAS products may benefit from machine learning, SAS Visual Data Mining and Machine Learning software may be the best suitable. Automated discernment and interpretability, automated attribute engineering and modeling, a public API for mechanical modeling, easy analytics, network analytics, deep understanding with Python and ONNX support, integrated data preparation, and in-memory processing are some of its primary features. It is part of the more extensive SAS Viya package.

A 30-day trial period is offered. Pricing is available upon request.

Pros

• SAS offers some of the complete solutions available.
• SAS makes ample use of automation, which can help to streamline the machine learning process and reduce time to value.
• SAS software receives good reviews from both analysts and users.

Cons

• Because SAS offers so many different machine learning and analytics tools, deciding which one(s) is ideal for your needs can be difficult.
• SAS goods are much more expensive than rivals in some situations.
• Some developers claim that working with SAS takes longer than other machine learning systems.

• Databricks

Databricks is a data science and machine learning company created in 2013 by several Apache Spark project creators. Clients include Comcast, Conde Nast, H&M, Regeneron, Nationwide, and Showtime. It is found in San Francisco, California, and has raised an estimated $897 million in funding.

The Databricks Unified Data Analytics Platform comprises the MLflow-based Data Science Workspace, the Apache Spark-based Unified Data Service, and the Redash visualization and dashboarding tool. Qlik, Power BI, Mode, TIBCO Spotfire, and ThoughtSpot are the leading business intelligence solutions it connects with.

The company offers several AWS and Microsoft Azure solutions, each with different levels of complexity. Pricing and a free 14-day trial are available on the company’s website. There is also a free Community Edition with fewer functions.

Pros

• If you’re considering using Apache Spark in production, Databricks is a great way to acquire Spark’s features while also obtaining the services and support organizations want.
• Python, R, and Scala are among the programming languages it supports.
• Databricks’ customer service is well appreciated.

Cons

• Because you must pay for AWS or Azure cloud instances in addition to the Databricks service, Databricks might be expensive depending on the plan you choose.
• Relying on how much data you have and if your computer instances are adequately scaled for your operations, the service might be slow.
• The company’s documentation isn’t excellent, and its search features are inferior.

• Microsoft Azure

Microsoft Azure, which debuted in 2010, is currently the second-largest cloud infrastructure provider, with over 18% of the market share. Microsoft recorded $13.4 billion in revenue from its Intelligent Cloud division, including Azure, in its most recent quarterly filing. It did not offer exact data for Azure, but it states that revenues increased by 47% year over year.

Azure Machine Learning supports automation and MLOps and code-based and drag-and-drop interfaces. It uses open-source software, including MLflow, Cubeflow, ONNX, PyTorch, TensorFlow, Python, and R. It also has techniques for recognising and addressing bias.

Basic and corporate versions of Azure Machine Learning are available. Although Microsoft offers a price list and a pricing calculator, cost varies substantially depending on the type of instance utilized. Microsoft also offers a free account with a $200 Machine Learning credit and a year of free Cognitive Services.

Pros

• The Microsoft service is created to meet the needs of both advanced and inexperienced users.
• The upfront pricing enables precise cost prediction and is frequently relatively inexpensive.
• Businesses that use other Microsoft Azure services will benefit from the service.

Cons

• Azure Machine Learning lacks some connectivity to BI tools and other apps that certain businesses demand.
• Some customers have expressed an interest in seeing more R-based models on the service.
• To prevent a surprise cost, you’ll need to keep track of your usage like other ML services from large cloud providers.

1. IBM

IBM is one of the most aged and most prominent technology companies, formed in 1911. Its headquarters are in Armonk, New York, and its most recent quarterly report showed revenues of $18.1 billion. Its total cloud revenue was $6.3 billion for the quarter, including IaaS and software as a service (SaaS).

With its Watson AI platform, IBM, a pioneer in artificial intelligence and machine learning, made headlines right away. Under the Watson name, it continues to sell several AI and machine learning services. Watson Machine Learning is compatible with hybrid and multi-cloud systems and works with other Watson solutions. It’s also possible to install it on your servers.

Watson services are available at various prices, including a free tier. The website provides price information for pay-as-you-go, but you must contact the business for subscription pricing (which may be more subordinate than pay-as-you-go) or deploying on your servers.

Pros

• IBM’s Watson ML is one of the few machine learning systems used on IBM’s cloud, another cloud service, your servers, or any combination.
• IBM believes that its machine learning services may help firms reduce the time to value on ML efforts by 40%.
• The group has long been a pioneer in artificial intelligence and offers various related services.

Cons

• Some clients have complained that the solution’s deployment took longer than intended.
• Some data science skills are required to fully utilize the service’s capabilities, like with many ML tools.
• When installing on their servers, several clients encountered issues.

Machine Learning makes computers intelligent and capable of learning from data by employing statistical methodologies. Amazon Web Services, IBM, Microsoft Azure, TIBCO and SAS are our top five recommended Machine Learning service providers.

According to the evaluations, most organizations charge $25 to $49 per hour or $50 to $99 per hour for their services. Hiring or using services from any of these services may take your business a step further! 

If you are interested in learning Artificial Intelligence and Machine Learning you can have a look at the AI-ML Beginners E-degree offered by Eduonix. Eduonix is a training and skill development organization. If you want to take it upon yourself to upgrade your business with Machine Learning, the first step is to learn more about it. The E-Degree pretty much covers everything you need to know about Machine Learning and also applying it to your business. The E-degree is currently on a 50% discount so right now seems like a great time to go ahead and upgrade yourself and your business. The E-degree has around 6 modules with 200+ lectures from experts. And it comes with hands-on projects which will give you a more holistic learning experience. 

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