Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
IBM has a rich history with machine learning. One of its own, Arthur Samuel, is credited for coining the term, “machine learning” with his research (PDF, 481 KB) (link resides outside IBM) around the game of checkers. Robert Nealey, the self-proclaimed checkers master, played the game on an IBM 7094 computer in 1962, and he lost to the computer. Compared to what can be done today, this feat almost seems trivial, but it’s considered a major milestone within the field of artificial intelligence. Over the next couple of decades, the technological developments around storage and processing power will enable some innovative products that we know and love today, such as Netflix’s recommendation engine or self-driving cars.
Microsoft Azure provides machine learning services for all sizes. It is suitable for all artificial intelligence and data scientist beginners and experts supporting a collection of a framework, databases, programming languages, operating systems, and services. The platform also helps with cross-device experience with support for all major mobile platforms.
2 AWS Machine Learning
Amazon Web Services has a high level of automation that is helpful for beginners. It helps businesses to build machine learning models without writing the code. It makes machine learning obtainable to developers without going through a learning process of ML algorithms and technology. The AWS ML services are based on the pay-as-you-go pricing model.
3 IBM Watson Machine Learning
WML runs on IBM’s Bluemix, even the developers and data scientists can use the WML to get themselves trained. WWL is created to answer the questions of deployment, operationalization, and deriving business values from ML models. WML also skits visual modeling tools that help users to gain understanding, quickly identify patterns, and make faster decisions.
4 Google Cloud Machine Learning
Google’s scope of Software-as-a-Service is nearly endless. Google’s cloud machine learning is based on TensorFlow, this ML engine is integrated with all other Google’s services such as Google Cloud Storage, Google BigQuery, and Google Cloud Dataflow.
5 BigML
BigML is easy and flexible to use and deploy services. Many features are integrated into BigML that allow importing data from Google Drive, Microsoft Azure, Google Storage, and AWS. It is also helpful in clustering algorithms and visualizations.
6 Domino
Domino assists in the latest data analysis workflow. It supports Python, R, MATLAB, Julia, Shell Scripts, and Perl languages. Data science managers, IT executives, data scientists, and leaders can use this platform and gain knowledge management with all the projects that are searchable and stored.
7 HPE Haven
Using Haven machine learning solutions extract, analyze, and index multiple data formats such as video, audio, or email. That includes attributes like face detection, speech recognition, media analysis, object recognition, image classification, speech recognition, and scene change detection.
8 Arimo
Arimo can crunch massive amounts of data in seconds using large computing platforms and machine learning algorithms. It also can anticipate future actions by learning from past experiences. The service provider works upon time-series data to discover patterns of behavior that are based on deep learning.
9 Dataiku Data Science Studio
Dataiku supports programs such as R, Spark, Hive, Scala, Python, and Pig. It provides machine learning solutions such as H2O, MLlib, Scikit-Learn, Xgboost. It delivers, builds, explores, and prototypes data products efficiently.
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