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Mobile Machine Learning Glossary

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Mobile Machine Learning Glossary

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1. Azure ML Studio

By far the most popular data exploration and manipulation package in Python. At its core is the concept of a DataFrame which acts as an in-memory relational database. You can do basic operations on columns and rows as well as aggregations on arbitrary groups. Basic time series routines are also included. Pandas is a bit of a memory hog - so be careful.Has a visual interface for training and serving models. Every step of the process - from data transformation to prediction - is constructed by connecting different tasks in a visual computation graph. Individual operations like “select these columns from the database” and “run k-means clustering” are defined by the user and the system deals with the backend computation. You can choose from over 100 algorithms out of the box or insert your own arbitrary scripts. Beware - though developing models with Azure ML studio is an entirely different workflow than all the other ML frameworks. Right now - there is no support for deep learning - but Microsoft has an Azure Batch AI product in beta.Due to the high cost of powerful GPU instance types on major cloud platforms - many have found it economical to build their own deep learning rigs. At the time I’m writing this - it costs between $2500 and $4000 to run the smallest GPU instance continuously for a year in the cloud. For that money - you can build an impressively fast machine yourself (crank up the settings on your video games at the same time). If you’re willing to fight the crypto miners for their precious GPUs - check out this guide for help picking the right one.A high level API for building neural networks. Keras makes it simple to chain together various predefined layers or write your own. Actually computation is done using one of two supported backends: Keras or the now defunct Theano. If you’re just getting started with neural networks - don’t need to do anything crazy - and want to stay within the TensorFlow family - Keras is your best bet.

2. Bespoke FIY Hardware

A high level API for building neural networks. Keras makes it simple to chain together various predefined layers or write your own. Actually computation is done using one of two supported backends: Keras or the now defunct Theano. If you’re just getting started with neural networks - don’t need to do anything crazy - and want to stay within the TensorFlow family - Keras is your best bet.Due to the high cost of powerful GPU instance types on major cloud platforms - many have found it economical to build their own deep learning rigs. At the time I’m writing this - it costs between $2500 and $4000 to run the smallest GPU instance continuously for a year in the cloud. For that money - you can build an impressively fast machine yourself (crank up the settings on your video games at the same time). If you’re willing to fight the crypto miners for their precious GPUs - check out this guide for help picking the right one.By far the most popular data exploration and manipulation package in Python. At its core is the concept of a DataFrame which acts as an in-memory relational database. You can do basic operations on columns and rows as well as aggregations on arbitrary groups. Basic time series routines are also included. Pandas is a bit of a memory hog - so be careful.Has a visual interface for training and serving models. Every step of the process - from data transformation to prediction - is constructed by connecting different tasks in a visual computation graph. Individual operations like “select these columns from the database” and “run k-means clustering” are defined by the user and the system deals with the backend computation. You can choose from over 100 algorithms out of the box or insert your own arbitrary scripts. Beware - though developing models with Azure ML studio is an entirely different workflow than all the other ML frameworks. Right now - there is no support for deep learning - but Microsoft has an Azure Batch AI product in beta.

3. Keras

4. Pandas

A high level API for building neural networks. Keras makes it simple to chain together various predefined layers or write your own. Actually computation is done using one of two supported backends: Keras or the now defunct Theano. If you’re just getting started with neural networks - don’t need to do anything crazy - and want to stay within the TensorFlow family - Keras is your best bet.Has a visual interface for training and serving models. Every step of the process - from data transformation to prediction - is constructed by connecting different tasks in a visual computation graph. Individual operations like “select these columns from the database” and “run k-means clustering” are defined by the user and the system deals with the backend computation. You can choose from over 100 algorithms out of the box or insert your own arbitrary scripts. Beware - though developing models with Azure ML studio is an entirely different workflow than all the other ML frameworks. Right now - there is no support for deep learning - but Microsoft has an Azure Batch AI product in beta.By far the most popular data exploration and manipulation package in Python. At its core is the concept of a DataFrame which acts as an in-memory relational database. You can do basic operations on columns and rows as well as aggregations on arbitrary groups. Basic time series routines are also included. Pandas is a bit of a memory hog - so be careful.Due to the high cost of powerful GPU instance types on major cloud platforms - many have found it economical to build their own deep learning rigs. At the time I’m writing this - it costs between $2500 and $4000 to run the smallest GPU instance continuously for a year in the cloud. For that money - you can build an impressively fast machine yourself (crank up the settings on your video games at the same time). If you’re willing to fight the crypto miners for their precious GPUs - check out this guide for help picking the right one.

5. Numpy

Due to the high cost of powerful GPU instance types on major cloud platforms - many have found it economical to build their own deep learning rigs. At the time I’m writing this - it costs between $2500 and $4000 to run the smallest GPU instance continuously for a year in the cloud. For that money - you can build an impressively fast machine yourself (crank up the settings on your video games at the same time). If you’re willing to fight the crypto miners for their precious GPUs - check out this guide for help picking the right one.The foundation of scientific Python. It defines N-dimensional array objects and an entire world of methods to manipulate them. Everything from multiplying matrices to fourier transforms is included. The linear algebra is implemented in C / C++ and Fortran providing great performance. You’ll want to avoid writing loops by vectorizing your code - which can be a little tricky at first.A high level API for building neural networks. Keras makes it simple to chain together various predefined layers or write your own. Actually computation is done using one of two supported backends: Keras or the now defunct Theano. If you’re just getting started with neural networks - don’t need to do anything crazy - and want to stay within the TensorFlow family - Keras is your best bet.Has a visual interface for training and serving models. Every step of the process - from data transformation to prediction - is constructed by connecting different tasks in a visual computation graph. Individual operations like “select these columns from the database” and “run k-means clustering” are defined by the user and the system deals with the backend computation. You can choose from over 100 algorithms out of the box or insert your own arbitrary scripts. Beware - though developing models with Azure ML studio is an entirely different workflow than all the other ML frameworks. Right now - there is no support for deep learning - but Microsoft has an Azure Batch AI product in beta.

6. Michelangelo

Due to the high cost of powerful GPU instance types on major cloud platforms - many have found it economical to build their own deep learning rigs. At the time I’m writing this - it costs between $2500 and $4000 to run the smallest GPU instance continuously for a year in the cloud. For that money - you can build an impressively fast machine yourself (crank up the settings on your video games at the same time). If you’re willing to fight the crypto miners for their precious GPUs - check out this guide for help picking the right one.A high level API for building neural networks. Keras makes it simple to chain together various predefined layers or write your own. Actually computation is done using one of two supported backends: Keras or the now defunct Theano. If you’re just getting started with neural networks - don’t need to do anything crazy - and want to stay within the TensorFlow family - Keras is your best bet.Also from Uber - an entire machine learning platform. It’s goal is to provide a scalable - robust way to go through every step of the development process from accessing raw data to monitoring models in production. It integrates with data warehouses and allows users to train models with almost all of the frameworks we’ve discussed above. Michelangelo also provides automatic visualization of features and models through a web interface.Has a visual interface for training and serving models. Every step of the process - from data transformation to prediction - is constructed by connecting different tasks in a visual computation graph. Individual operations like “select these columns from the database” and “run k-means clustering” are defined by the user and the system deals with the backend computation. You can choose from over 100 algorithms out of the box or insert your own arbitrary scripts. Beware - though developing models with Azure ML studio is an entirely different workflow than all the other ML frameworks. Right now - there is no support for deep learning - but Microsoft has an Azure Batch AI product in beta.

7. NetworkX

Due to the high cost of powerful GPU instance types on major cloud platforms - many have found it economical to build their own deep learning rigs. At the time I’m writing this - it costs between $2500 and $4000 to run the smallest GPU instance continuously for a year in the cloud. For that money - you can build an impressively fast machine yourself (crank up the settings on your video games at the same time). If you’re willing to fight the crypto miners for their precious GPUs - check out this guide for help picking the right one.Has a visual interface for training and serving models. Every step of the process - from data transformation to prediction - is constructed by connecting different tasks in a visual computation graph. Individual operations like “select these columns from the database” and “run k-means clustering” are defined by the user and the system deals with the backend computation. You can choose from over 100 algorithms out of the box or insert your own arbitrary scripts. Beware - though developing models with Azure ML studio is an entirely different workflow than all the other ML frameworks. Right now - there is no support for deep learning - but Microsoft has an Azure Batch AI product in beta.An easy to use - well supported network package. It has support for almost any type of graph and has methods for computing almost every graph metric under the sun. The one downside to NetworkX is that it’s implemented in pure Python and can be slow and a big memory hog. Link Graph-tool is a faster - C++ based alternative - but isn’t as well supported and is a pain to install on some systems.A high level API for building neural networks. Keras makes it simple to chain together various predefined layers or write your own. Actually computation is done using one of two supported backends: Keras or the now defunct Theano. If you’re just getting started with neural networks - don’t need to do anything crazy - and want to stay within the TensorFlow family - Keras is your best bet.

8. Android

Has a very similar set of computer vision functions available through their Mobile Vision API. They don’t offer anything in the way of natural language processing. ARCore is Android’s answer to ARKit and has absorbed the software side of the now defunct Tango project.Due to the high cost of powerful GPU instance types on major cloud platforms - many have found it economical to build their own deep learning rigs. At the time I’m writing this - it costs between $2500 and $4000 to run the smallest GPU instance continuously for a year in the cloud. For that money - you can build an impressively fast machine yourself (crank up the settings on your video games at the same time). If you’re willing to fight the crypto miners for their precious GPUs - check out this guide for help picking the right one.A high level API for building neural networks. Keras makes it simple to chain together various predefined layers or write your own. Actually computation is done using one of two supported backends: Keras or the now defunct Theano. If you’re just getting started with neural networks - don’t need to do anything crazy - and want to stay within the TensorFlow family - Keras is your best bet.Has a visual interface for training and serving models. Every step of the process - from data transformation to prediction - is constructed by connecting different tasks in a visual computation graph. Individual operations like “select these columns from the database” and “run k-means clustering” are defined by the user and the system deals with the backend computation. You can choose from over 100 algorithms out of the box or insert your own arbitrary scripts. Beware - though developing models with Azure ML studio is an entirely different workflow than all the other ML frameworks. Right now - there is no support for deep learning - but Microsoft has an Azure Batch AI product in beta.

9. MXNet (json)

Uses json.A high level API for building neural networks. Keras makes it simple to chain together various predefined layers or write your own. Actually computation is done using one of two supported backends: Keras or the now defunct Theano. If you’re just getting started with neural networks - don’t need to do anything crazy - and want to stay within the TensorFlow family - Keras is your best bet.Due to the high cost of powerful GPU instance types on major cloud platforms - many have found it economical to build their own deep learning rigs. At the time I’m writing this - it costs between $2500 and $4000 to run the smallest GPU instance continuously for a year in the cloud. For that money - you can build an impressively fast machine yourself (crank up the settings on your video games at the same time). If you’re willing to fight the crypto miners for their precious GPUs - check out this guide for help picking the right one.Has a visual interface for training and serving models. Every step of the process - from data transformation to prediction - is constructed by connecting different tasks in a visual computation graph. Individual operations like “select these columns from the database” and “run k-means clustering” are defined by the user and the system deals with the backend computation. You can choose from over 100 algorithms out of the box or insert your own arbitrary scripts. Beware - though developing models with Azure ML studio is an entirely different workflow than all the other ML frameworks. Right now - there is no support for deep learning - but Microsoft has an Azure Batch AI product in beta.

10. OpenCV

One of the most mature - well supported computer vision frameworks. Both iOS and Android have been supported for years and there are tons of algorithms already implemented including the latest neural networks. I was really surprised at the breadth of implementations.Has a visual interface for training and serving models. Every step of the process - from data transformation to prediction - is constructed by connecting different tasks in a visual computation graph. Individual operations like “select these columns from the database” and “run k-means clustering” are defined by the user and the system deals with the backend computation. You can choose from over 100 algorithms out of the box or insert your own arbitrary scripts. Beware - though developing models with Azure ML studio is an entirely different workflow than all the other ML frameworks. Right now - there is no support for deep learning - but Microsoft has an Azure Batch AI product in beta.A high level API for building neural networks. Keras makes it simple to chain together various predefined layers or write your own. Actually computation is done using one of two supported backends: Keras or the now defunct Theano. If you’re just getting started with neural networks - don’t need to do anything crazy - and want to stay within the TensorFlow family - Keras is your best bet.Due to the high cost of powerful GPU instance types on major cloud platforms - many have found it economical to build their own deep learning rigs. At the time I’m writing this - it costs between $2500 and $4000 to run the smallest GPU instance continuously for a year in the cloud. For that money - you can build an impressively fast machine yourself (crank up the settings on your video games at the same time). If you’re willing to fight the crypto miners for their precious GPUs - check out this guide for help picking the right one.

11. XLA

Due to the high cost of powerful GPU instance types on major cloud platforms - many have found it economical to build their own deep learning rigs. At the time I’m writing this - it costs between $2500 and $4000 to run the smallest GPU instance continuously for a year in the cloud. For that money - you can build an impressively fast machine yourself (crank up the settings on your video games at the same time). If you’re willing to fight the crypto miners for their precious GPUs - check out this guide for help picking the right one.A high level API for building neural networks. Keras makes it simple to chain together various predefined layers or write your own. Actually computation is done using one of two supported backends: Keras or the now defunct Theano. If you’re just getting started with neural networks - don’t need to do anything crazy - and want to stay within the TensorFlow family - Keras is your best bet.An experimental accelerated linear algebra compiler build by the TensorFlow team. It allows for device specific implementations of various tensor operations so that execution is optimized for each devices computation and memory constraints.Has a visual interface for training and serving models. Every step of the process - from data transformation to prediction - is constructed by connecting different tasks in a visual computation graph. Individual operations like “select these columns from the database” and “run k-means clustering” are defined by the user and the system deals with the backend computation. You can choose from over 100 algorithms out of the box or insert your own arbitrary scripts. Beware - though developing models with Azure ML studio is an entirely different workflow than all the other ML frameworks. Right now - there is no support for deep learning - but Microsoft has an Azure Batch AI product in beta.

12. Algorithmia

Has a visual interface for training and serving models. Every step of the process - from data transformation to prediction - is constructed by connecting different tasks in a visual computation graph. Individual operations like “select these columns from the database” and “run k-means clustering” are defined by the user and the system deals with the backend computation. You can choose from over 100 algorithms out of the box or insert your own arbitrary scripts. Beware - though developing models with Azure ML studio is an entirely different workflow than all the other ML frameworks. Right now - there is no support for deep learning - but Microsoft has an Azure Batch AI product in beta.A high level API for building neural networks. Keras makes it simple to chain together various predefined layers or write your own. Actually computation is done using one of two supported backends: Keras or the now defunct Theano. If you’re just getting started with neural networks - don’t need to do anything crazy - and want to stay within the TensorFlow family - Keras is your best bet.Takes a broad approach to fully managed machine learning APIs. Acting like the GitHub for hosted machine learning models - they allow anyone to upload a pretrained model and access it through an API endpoint. Individual developers or organizations can then charge per request to use the model. Public and private endpoints are available.Due to the high cost of powerful GPU instance types on major cloud platforms - many have found it economical to build their own deep learning rigs. At the time I’m writing this - it costs between $2500 and $4000 to run the smallest GPU instance continuously for a year in the cloud. For that money - you can build an impressively fast machine yourself (crank up the settings on your video games at the same time). If you’re willing to fight the crypto miners for their precious GPUs - check out this guide for help picking the right one.

13. Caffe (1 and 2)

Due to the high cost of powerful GPU instance types on major cloud platforms - many have found it economical to build their own deep learning rigs. At the time I’m writing this - it costs between $2500 and $4000 to run the smallest GPU instance continuously for a year in the cloud. For that money - you can build an impressively fast machine yourself (crank up the settings on your video games at the same time). If you’re willing to fight the crypto miners for their precious GPUs - check out this guide for help picking the right one.Has a visual interface for training and serving models. Every step of the process - from data transformation to prediction - is constructed by connecting different tasks in a visual computation graph. Individual operations like “select these columns from the database” and “run k-means clustering” are defined by the user and the system deals with the backend computation. You can choose from over 100 algorithms out of the box or insert your own arbitrary scripts. Beware - though developing models with Azure ML studio is an entirely different workflow than all the other ML frameworks. Right now - there is no support for deep learning - but Microsoft has an Azure Batch AI product in beta.A high level API for building neural networks. Keras makes it simple to chain together various predefined layers or write your own. Actually computation is done using one of two supported backends: Keras or the now defunct Theano. If you’re just getting started with neural networks - don’t need to do anything crazy - and want to stay within the TensorFlow family - Keras is your best bet.A deep learning framework originally developed at Berkeley. It was an early favorite with some heavyweights like Facebook - but lately its lost some luster as other entrants like TensorFlow have gained popularity. Caffe APIs are available in C++ and Python. Facebook has continued development with Caffe 2 - adding better support for distributed training and inference - newer hardware - and mobile deployment. When you’re looking through Caffe projects - be aware of the version number (1 or 2). While converters work fairly well - it’s not a guarantee that you can translate freely between the two.

14. Amazon

Due to the high cost of powerful GPU instance types on major cloud platforms - many have found it economical to build their own deep learning rigs. At the time I’m writing this - it costs between $2500 and $4000 to run the smallest GPU instance continuously for a year in the cloud. For that money - you can build an impressively fast machine yourself (crank up the settings on your video games at the same time). If you’re willing to fight the crypto miners for their precious GPUs - check out this guide for help picking the right one.Has a visual interface for training and serving models. Every step of the process - from data transformation to prediction - is constructed by connecting different tasks in a visual computation graph. Individual operations like “select these columns from the database” and “run k-means clustering” are defined by the user and the system deals with the backend computation. You can choose from over 100 algorithms out of the box or insert your own arbitrary scripts. Beware - though developing models with Azure ML studio is an entirely different workflow than all the other ML frameworks. Right now - there is no support for deep learning - but Microsoft has an Azure Batch AI product in beta.A high level API for building neural networks. Keras makes it simple to chain together various predefined layers or write your own. Actually computation is done using one of two supported backends: Keras or the now defunct Theano. If you’re just getting started with neural networks - don’t need to do anything crazy - and want to stay within the TensorFlow family - Keras is your best bet.Its flagship API is Lex - a conversational UI builder. Lex offers automatic voice recognition and natural language processing so you can build services like Amazon’s Alexa. AWS also offers Rekognition for image and vision - Polly for text to speech - Transcribe for transcription of large audio files - and Comprehend for natural language processing.

15. iOS

Has a visual interface for training and serving models. Every step of the process - from data transformation to prediction - is constructed by connecting different tasks in a visual computation graph. Individual operations like “select these columns from the database” and “run k-means clustering” are defined by the user and the system deals with the backend computation. You can choose from over 100 algorithms out of the box or insert your own arbitrary scripts. Beware - though developing models with Azure ML studio is an entirely different workflow than all the other ML frameworks. Right now - there is no support for deep learning - but Microsoft has an Azure Batch AI product in beta.Apple gives you a few models right out of the box including their Vision Framework which has face detection - text detection (just bounding boxes not OCR) - barcode detection and object tracking. ARKit also provides computer vision models that can detection horizontal surfaces and track position in 3D space.A high level API for building neural networks. Keras makes it simple to chain together various predefined layers or write your own. Actually computation is done using one of two supported backends: Keras or the now defunct Theano. If you’re just getting started with neural networks - don’t need to do anything crazy - and want to stay within the TensorFlow family - Keras is your best bet.Due to the high cost of powerful GPU instance types on major cloud platforms - many have found it economical to build their own deep learning rigs. At the time I’m writing this - it costs between $2500 and $4000 to run the smallest GPU instance continuously for a year in the cloud. For that money - you can build an impressively fast machine yourself (crank up the settings on your video games at the same time). If you’re willing to fight the crypto miners for their precious GPUs - check out this guide for help picking the right one.

16. Microsoft Custom Vision Service

Allows anyone to upload labeled images and create a custom neural network trained to classify them. Models can be served from an API created in Azure or exported for use somewhere else - including mobile via Core ML.A high level API for building neural networks. Keras makes it simple to chain together various predefined layers or write your own. Actually computation is done using one of two supported backends: Keras or the now defunct Theano. If you’re just getting started with neural networks - don’t need to do anything crazy - and want to stay within the TensorFlow family - Keras is your best bet.Has a visual interface for training and serving models. Every step of the process - from data transformation to prediction - is constructed by connecting different tasks in a visual computation graph. Individual operations like “select these columns from the database” and “run k-means clustering” are defined by the user and the system deals with the backend computation. You can choose from over 100 algorithms out of the box or insert your own arbitrary scripts. Beware - though developing models with Azure ML studio is an entirely different workflow than all the other ML frameworks. Right now - there is no support for deep learning - but Microsoft has an Azure Batch AI product in beta.Due to the high cost of powerful GPU instance types on major cloud platforms - many have found it economical to build their own deep learning rigs. At the time I’m writing this - it costs between $2500 and $4000 to run the smallest GPU instance continuously for a year in the cloud. For that money - you can build an impressively fast machine yourself (crank up the settings on your video games at the same time). If you’re willing to fight the crypto miners for their precious GPUs - check out this guide for help picking the right one.

17. MXNet

Has a visual interface for training and serving models. Every step of the process - from data transformation to prediction - is constructed by connecting different tasks in a visual computation graph. Individual operations like “select these columns from the database” and “run k-means clustering” are defined by the user and the system deals with the backend computation. You can choose from over 100 algorithms out of the box or insert your own arbitrary scripts. Beware - though developing models with Azure ML studio is an entirely different workflow than all the other ML frameworks. Right now - there is no support for deep learning - but Microsoft has an Azure Batch AI product in beta.(yet another) deep learning framework - this time with support from Apache and Amazon. Though you won’t find quite as many examples written with MXNet as other frameworks - you get the best of both worlds with support for both a deferred computation model and an imperative option (called Gluon). MXNet also boasts an impressively large set of languages including Python - C++ - Javascript - Go - Scala - Julia - R - and Matlab. Because it’s used by Amazon - it’s plays nice with everything AWS.A high level API for building neural networks. Keras makes it simple to chain together various predefined layers or write your own. Actually computation is done using one of two supported backends: Keras or the now defunct Theano. If you’re just getting started with neural networks - don’t need to do anything crazy - and want to stay within the TensorFlow family - Keras is your best bet.Due to the high cost of powerful GPU instance types on major cloud platforms - many have found it economical to build their own deep learning rigs. At the time I’m writing this - it costs between $2500 and $4000 to run the smallest GPU instance continuously for a year in the cloud. For that money - you can build an impressively fast machine yourself (crank up the settings on your video games at the same time). If you’re willing to fight the crypto miners for their precious GPUs - check out this guide for help picking the right one.

18. Unmanaged - Distributed Clusters

Due to the high cost of powerful GPU instance types on major cloud platforms - many have found it economical to build their own deep learning rigs. At the time I’m writing this - it costs between $2500 and $4000 to run the smallest GPU instance continuously for a year in the cloud. For that money - you can build an impressively fast machine yourself (crank up the settings on your video games at the same time). If you’re willing to fight the crypto miners for their precious GPUs - check out this guide for help picking the right one.Has a visual interface for training and serving models. Every step of the process - from data transformation to prediction - is constructed by connecting different tasks in a visual computation graph. Individual operations like “select these columns from the database” and “run k-means clustering” are defined by the user and the system deals with the backend computation. You can choose from over 100 algorithms out of the box or insert your own arbitrary scripts. Beware - though developing models with Azure ML studio is an entirely different workflow than all the other ML frameworks. Right now - there is no support for deep learning - but Microsoft has an Azure Batch AI product in beta.Large deep learning models - though - can take days or weeks to train and chances are - you don’t want to leave your laptop running untouched for that long. When you’re ready to move things off of your laptop - you’ve got options. We’ll start with the least managed ones.A high level API for building neural networks. Keras makes it simple to chain together various predefined layers or write your own. Actually computation is done using one of two supported backends: Keras or the now defunct Theano. If you’re just getting started with neural networks - don’t need to do anything crazy - and want to stay within the TensorFlow family - Keras is your best bet.

19. General Machine Learning

Before neural networks came back into vogue - it was all about boring old machine learning. Support vector machines - random forests - even linear regression are still useful techniques you should try first before jumping straight into deep learning. Many of these tools will use Numpy and Scipy routines under the hood and all of them come with a bunch of algorithms right out of the box.Due to the high cost of powerful GPU instance types on major cloud platforms - many have found it economical to build their own deep learning rigs. At the time I’m writing this - it costs between $2500 and $4000 to run the smallest GPU instance continuously for a year in the cloud. For that money - you can build an impressively fast machine yourself (crank up the settings on your video games at the same time). If you’re willing to fight the crypto miners for their precious GPUs - check out this guide for help picking the right one.Has a visual interface for training and serving models. Every step of the process - from data transformation to prediction - is constructed by connecting different tasks in a visual computation graph. Individual operations like “select these columns from the database” and “run k-means clustering” are defined by the user and the system deals with the backend computation. You can choose from over 100 algorithms out of the box or insert your own arbitrary scripts. Beware - though developing models with Azure ML studio is an entirely different workflow than all the other ML frameworks. Right now - there is no support for deep learning - but Microsoft has an Azure Batch AI product in beta.A high level API for building neural networks. Keras makes it simple to chain together various predefined layers or write your own. Actually computation is done using one of two supported backends: Keras or the now defunct Theano. If you’re just getting started with neural networks - don’t need to do anything crazy - and want to stay within the TensorFlow family - Keras is your best bet.

20. Salesforce Einstein

An AI tool that claims to automatically generate insights about your business and customers from your Salesforce data. This is really just a feature of Salesforce - but it’s one of the few completely automated business insight tools available.Due to the high cost of powerful GPU instance types on major cloud platforms - many have found it economical to build their own deep learning rigs. At the time I’m writing this - it costs between $2500 and $4000 to run the smallest GPU instance continuously for a year in the cloud. For that money - you can build an impressively fast machine yourself (crank up the settings on your video games at the same time). If you’re willing to fight the crypto miners for their precious GPUs - check out this guide for help picking the right one.Has a visual interface for training and serving models. Every step of the process - from data transformation to prediction - is constructed by connecting different tasks in a visual computation graph. Individual operations like “select these columns from the database” and “run k-means clustering” are defined by the user and the system deals with the backend computation. You can choose from over 100 algorithms out of the box or insert your own arbitrary scripts. Beware - though developing models with Azure ML studio is an entirely different workflow than all the other ML frameworks. Right now - there is no support for deep learning - but Microsoft has an Azure Batch AI product in beta.A high level API for building neural networks. Keras makes it simple to chain together various predefined layers or write your own. Actually computation is done using one of two supported backends: Keras or the now defunct Theano. If you’re just getting started with neural networks - don’t need to do anything crazy - and want to stay within the TensorFlow family - Keras is your best bet.

21. SystemML

Has a visual interface for training and serving models. Every step of the process - from data transformation to prediction - is constructed by connecting different tasks in a visual computation graph. Individual operations like “select these columns from the database” and “run k-means clustering” are defined by the user and the system deals with the backend computation. You can choose from over 100 algorithms out of the box or insert your own arbitrary scripts. Beware - though developing models with Azure ML studio is an entirely different workflow than all the other ML frameworks. Right now - there is no support for deep learning - but Microsoft has an Azure Batch AI product in beta.Due to the high cost of powerful GPU instance types on major cloud platforms - many have found it economical to build their own deep learning rigs. At the time I’m writing this - it costs between $2500 and $4000 to run the smallest GPU instance continuously for a year in the cloud. For that money - you can build an impressively fast machine yourself (crank up the settings on your video games at the same time). If you’re willing to fight the crypto miners for their precious GPUs - check out this guide for help picking the right one.A high level API for building neural networks. Keras makes it simple to chain together various predefined layers or write your own. Actually computation is done using one of two supported backends: Keras or the now defunct Theano. If you’re just getting started with neural networks - don’t need to do anything crazy - and want to stay within the TensorFlow family - Keras is your best bet.A former IBM - now Apache project aimed at making it easier to create optimized machine learning and analytics pipelines. It comes with a bunch of ML algorithms out the box that can be used for classification or regression problems and has high level APIs to primitives that let users write their own. SystemML uses Spark as a backend by default - but can be used with others like Hadoop. SystemML is most closely related to MLLib that is included with Spark - but with some additional abstractions and optimizations.

22. OpenAI

Due to the high cost of powerful GPU instance types on major cloud platforms - many have found it economical to build their own deep learning rigs. At the time I’m writing this - it costs between $2500 and $4000 to run the smallest GPU instance continuously for a year in the cloud. For that money - you can build an impressively fast machine yourself (crank up the settings on your video games at the same time). If you’re willing to fight the crypto miners for their precious GPUs - check out this guide for help picking the right one.Elon Musks AI initiative - has open sourced some fairly unique tools that do a good job of staying platform agnostic. OpenAI Gym helps developing reinforcement learning algorithms - RoboSchool is a simulation framework for learning control algorithms for Robots - and Universe contains tools for training and measuring the performance of AI agents on extremely complex - real-world tasks.Has a visual interface for training and serving models. Every step of the process - from data transformation to prediction - is constructed by connecting different tasks in a visual computation graph. Individual operations like “select these columns from the database” and “run k-means clustering” are defined by the user and the system deals with the backend computation. You can choose from over 100 algorithms out of the box or insert your own arbitrary scripts. Beware - though developing models with Azure ML studio is an entirely different workflow than all the other ML frameworks. Right now - there is no support for deep learning - but Microsoft has an Azure Batch AI product in beta.A high level API for building neural networks. Keras makes it simple to chain together various predefined layers or write your own. Actually computation is done using one of two supported backends: Keras or the now defunct Theano. If you’re just getting started with neural networks - don’t need to do anything crazy - and want to stay within the TensorFlow family - Keras is your best bet.

23. Pomegranate

A high level API for building neural networks. Keras makes it simple to chain together various predefined layers or write your own. Actually computation is done using one of two supported backends: Keras or the now defunct Theano. If you’re just getting started with neural networks - don’t need to do anything crazy - and want to stay within the TensorFlow family - Keras is your best bet.Due to the high cost of powerful GPU instance types on major cloud platforms - many have found it economical to build their own deep learning rigs. At the time I’m writing this - it costs between $2500 and $4000 to run the smallest GPU instance continuously for a year in the cloud. For that money - you can build an impressively fast machine yourself (crank up the settings on your video games at the same time). If you’re willing to fight the crypto miners for their precious GPUs - check out this guide for help picking the right one.Has a visual interface for training and serving models. Every step of the process - from data transformation to prediction - is constructed by connecting different tasks in a visual computation graph. Individual operations like “select these columns from the database” and “run k-means clustering” are defined by the user and the system deals with the backend computation. You can choose from over 100 algorithms out of the box or insert your own arbitrary scripts. Beware - though developing models with Azure ML studio is an entirely different workflow than all the other ML frameworks. Right now - there is no support for deep learning - but Microsoft has an Azure Batch AI product in beta.A new kid on the block providing a high level API for building probabilistic models from data. This includes things like mixture models - bayes classifiers - and Markov models.

24. TensorFlow Lite (.tflite)

For TensorFlow Lite Google created another storage format called a FlatBuffer which saves extra space over protobufs.Has a visual interface for training and serving models. Every step of the process - from data transformation to prediction - is constructed by connecting different tasks in a visual computation graph. Individual operations like “select these columns from the database” and “run k-means clustering” are defined by the user and the system deals with the backend computation. You can choose from over 100 algorithms out of the box or insert your own arbitrary scripts. Beware - though developing models with Azure ML studio is an entirely different workflow than all the other ML frameworks. Right now - there is no support for deep learning - but Microsoft has an Azure Batch AI product in beta.Due to the high cost of powerful GPU instance types on major cloud platforms - many have found it economical to build their own deep learning rigs. At the time I’m writing this - it costs between $2500 and $4000 to run the smallest GPU instance continuously for a year in the cloud. For that money - you can build an impressively fast machine yourself (crank up the settings on your video games at the same time). If you’re willing to fight the crypto miners for their precious GPUs - check out this guide for help picking the right one.A high level API for building neural networks. Keras makes it simple to chain together various predefined layers or write your own. Actually computation is done using one of two supported backends: Keras or the now defunct Theano. If you’re just getting started with neural networks - don’t need to do anything crazy - and want to stay within the TensorFlow family - Keras is your best bet.

25. Clarifai

Due to the high cost of powerful GPU instance types on major cloud platforms - many have found it economical to build their own deep learning rigs. At the time I’m writing this - it costs between $2500 and $4000 to run the smallest GPU instance continuously for a year in the cloud. For that money - you can build an impressively fast machine yourself (crank up the settings on your video games at the same time). If you’re willing to fight the crypto miners for their precious GPUs - check out this guide for help picking the right one.Clarifai was one of the first startups to offer image analysis APIs. They have a number of proprietary models with excellent results for specific image recognition tasks like identifying apparel - celebrities - or food in photos. If you’re trying to do something slightly more specific than what Google or AWS will allow - check Clarifai before building from scratch. They recently announced a mobile SDK as well.Has a visual interface for training and serving models. Every step of the process - from data transformation to prediction - is constructed by connecting different tasks in a visual computation graph. Individual operations like “select these columns from the database” and “run k-means clustering” are defined by the user and the system deals with the backend computation. You can choose from over 100 algorithms out of the box or insert your own arbitrary scripts. Beware - though developing models with Azure ML studio is an entirely different workflow than all the other ML frameworks. Right now - there is no support for deep learning - but Microsoft has an Azure Batch AI product in beta.A high level API for building neural networks. Keras makes it simple to chain together various predefined layers or write your own. Actually computation is done using one of two supported backends: Keras or the now defunct Theano. If you’re just getting started with neural networks - don’t need to do anything crazy - and want to stay within the TensorFlow family - Keras is your best bet.

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❤ If you liked Fatskills, consider supporting us by checking out The Life Manuals You Never Got.

About | Explore | User Guide | Topics | Subjects | Doubt Solver | Career Aptitude Test | Answers | Free Tools | OSHA Basics Quiz | What Should We Know? Privacy | Terms |

Without work one finishes nothing. - Ralph Waldo Emerson© 2026 Fatskills.com

All trademarks, logos and brand names are the property of their respective owners. All company, product and service names used in this website are for identification purposes only. Use of these names, trademarks and brands does not imply endorsement.

About | Explore | User Guide | Topics | Subjects | Doubt Solver | Career Aptitude Test | Answers | Free Tools | OSHA Basics Quiz | What Should We Know?
Privacy | Terms |

Without work one finishes nothing. - Ralph Waldo Emerson
© 2026 Fatskills.com