Introduction

Artificial intelligence is a $23bn market. And according to the latest estimates, it could pass $125bn by 2025. That’s a CAGR of over 30%, which is phenomenal by any standards. But growth alone isn’t a reason for an enterprise to deploy AI.

So today, we’ll answer the questions, “Why should forward-thinking leaders, like yourself, explore ways to use AI or ML in your organization? And should you even care about the top ML frameworks in 2021?”

In short, we believe so: because artificial intelligence and machine learning can solve even the most complex business challenges.

To elaborate, the two technologies:

• Enable businesses to build effective, robust solutions in line with lean methodology;
• Have already crossed the chasm from hype to reality, proving themselves a worthwhile investment.

That said, the technology is still relatively young. Yet executives have increasing confidence in its power. Why so? Because industries from retail to BFSI, education to sports, marketing to finance, have piloted AI and yielded unprecedented results.

For a pertinent example: look no further than the turbocharged research and development of COVID-19 vaccines.

———

In the round, AI-ML is such a useful corporate tool because it unlocks agility in organizational DNA. Put another way: it improves operational effectiveness. It can even unlock new growth channels.

And the insights we’ll present today will show you how to leverage the right machine learning frameworks to accelerate your organizational growth. If you’d like first to step back and learn the differences between AI, ML, and data science, click here.

Once you feel up-to-speed, read on for the all-important details.

Why Do You Need Machine Learning Frameworks?

At DLabs.AI, we cash in on machine intelligence and efficiency to build scalable enterprise solutions. That means we know how to leverage technologies to solve complex business problems. 

But what if your business doesn’t have this type of in-house expertise? That’s where off-the-shelf ML frameworks come in. But their availability benefits both experienced and less experienced operators for three reasons.

Machine learning frameworks mean you can:

1. Access most of the necessary algorithms via libraries or open APIs;
2. Install the libraries as an add-on or integrate them as part of the respective ML framework;
3. Use the libraries as they stand or customize them to your needs.

ML frameworks are potent resources. They allow an organization to reduce the developer resource required to complete a project. At the same time, they significantly shorten the time to deployment.

Here are the 10 frameworks to try in 2021.

10 Frameworks To Try in 2021

TensorFlow

With 152k+ stars and 83k+ forks on GitHub, Tensorflow is the open-source powerhouse of machine learning. 

The Google Brain Team developed TensorFlow as a general end-to-end ML platform, using C++, CUDA, and Python. There are endless use cases for the framework, but to give you an idea, here’s how we’ve used it:

• Image classification
• Image segmentation
• Text classification using BERT
• Text generation using RNN
• Structured data classification
• Speech recognition

Why TensorFlow is a Good ML Framework:

• Suits both simple and complex use-cases
• Everyone from startups to SMEs to multinational enterprises can use TensorFlow to unlock insights from text, images, and audio
• Open-source and platform agnostic
• Access high-level APIs (like Keras API, model subclassing API, Distribution API) for a simpler way to get started
• Deploy on Android, web, iOS, IoT devices, cloud clusters, CPUs, GPUs, and TPUs
• Different shades suit every need:
  • Tensorflow Lite for mobile
  • Tensorflow.js for training and deploying ML models in JavaScript environments
  • Tensorflow Extended (TFX) for full production ML pipelines

Who’s Using TensorFlow?

• • • Google
    • Coca Cola
    • Airbnb
    • ARM
    • GE Healthcar
    • Lenovo
    • Paypal
    • Swisscom
    • Qualcomm
    • Twitter
    • China Mobile

When Should You Avoid TensorFlow?

The language has a steep learning curve, so it isn’t ideal for beginners. TF is best deployed in multi-GPU environments but lags in performance in single GPU environments.

PyTorch

With 45k+ stars on Github, Facebook’s open-source ML framework, PyTorch, is a strong TensorFlow rival (it can do pretty much everything TF does). PyTorch is a community-driven Python package that’s intuitive, easy-to-use, lean, and fast — empowering developers to: 

• Perform Tensor computations with strong GPU acceleration
• Extend with NumPy, SciPy, Cython, etc.

The primary differentiator of PyTorch versus other machine learning frameworks is its “reverse-mode auto-differentiation” technique, as illustrated below:

Traditionally, once neural networks are built, any modification requirements would translate into starting from scratch. But with reverse-mode auto-differentiation, PyTorch enables you to modify how your neural network behaves with almost zero overhead. 

This means more flexibility and quicker development.

If we pit PyTorch against TensorFlow, we’d say TensorFlow is used comparatively more in deployment. But PyTorch has started to outpace TensorFlow in terms of research paper count.

Could this be a signal of PyTorch’s future share in ML model deployments? We believe so.

Why PyTorch is a Good ML Framework:

• Useful in computer vision and for NLP-focused use cases
• Eager execution mode
• Strongly Pythonic
• Off-the-shelf loss and optimizer functions, easy data loading mechanism, chaining built-in transformations
• Comparatively easy learning curve
• TorchServe and TorchScript have end-to-end workflows for major platforms
• PyTorch mobile for iOS, Android, and Linux
• Facilitates distributed training with native support for asynchronous execution of collective operations, peer-to-peer communication

Who’s Using Pytorch?

• Salesforce
• NVIDIA
• Apple
• Facebook
• UDACITY
• Stanford 

When Should You Avoid using PyTorch?

This isn’t necessarily a time to avoid PyTorch but, if you can, it’s often better to use TensorFlow Lite in place of PyTorch mobile for imparting ML capabilities on edge devices. Or, if you’re a JS developer looking for a robust ML framework, then TensorFlowJS could be a better option.

SciKit Learn

With 43.7k+ stars on GitHub, SciKit Learn is the third most popular framework for machine learning. It’s Pythonic (built on SciPy, Numpy, Matplotlib) and distributed under the BSD license. 

Why SciKit is a Good ML framework:

• Provides “classic” ML learning algorithms
• One of the oldest frameworks, developed in 2007 (vs. 2015 for TensorFlow)
• Great for classification and categorization of objects using SVMs, nearest neighbors, random forest algorithms
• Regression used in accelerated drug discovery and stock price prediction
• Clustering, preprocessing, dimensionality reduction, and modal selection help with customer segmentation and improved visualization of data
• You may also easily implement k-means, grid-search, feature selection, feature extraction, matrix factorization, and metrics.

Who’s Using SciKit Learn?

• JP Morgan
• Spotify
• Aweber
• Booking.com
• Evernote
• OkCupid
• Dataiku
• DataRobot

When Should You Avoid using SciKit Learn?

SciKit Learn doesn’t suit GPU-based machine learning requirements. It’s also not built to run across clusters (Spark ML is more suited to cluster-based ML applications).

Apple’s Core ML

If you want to make predictions using data from a user’s device, that’s precisely what Apple’s core ML model lets you do. It integrates unified ML models directly into an app, using end-user data to make predictions, train the model — or improve the efficacy of the algorithm:

• Build text, image, motion, tabular, video, and audio models, then train them using the createML app that ships with Xcode
• Models built using createML are coreML compatible
• Alternatively, you can build models using your preferred machine learning library and then use core ML tools (2k+ stars on GitHub) to convert the model into a coreML format

Why Apple’s Core ML is a Good ML framework:

• Integrate ML into a phone with no privacy intrusion, and lower memory/battery requirements
• Works cohesively with Apples’ Vision framework, NL framework, Speech & Sound Analysis frameworks (these are domain-specific, functionality-focused ML frameworks)
• Using a vision machine learning framework, you can:
  • Detect objects in images (like faces in a selfie or barcodes)
  • Perform object trajectory detection & tracking in videos (like tracking players in a sports analysis app or consumer movement in a smart CCTV camera app)
• Other possibilities using natural language, speech, or sound analysis:
  • Perform text tokenization, semantic distance calculation between words, language identification, text segmentation
  • Detect and process audio, either in pre-recorded format or live, using the Speech framework (for speech-to-text conversions) 
  • Leverage the MLSoundClassifier model trainer to integrate intelligent sound processing and identification into your app (for removing breathing sounds or noise in streamed audio)

When Should You Avoid using Apple’s Core ML?

If you need a platform-agnostic ML framework, then this one isn’t for you. Expanding to other platforms could be a cost-intensive deal. For a full-stack, cross-platform machine learning framework, consider PyTorch or TensorFlow.

H2O-3

H2O is a distributed, scalable, extensible in-memory platform with 5.3k+ stars on GitHub. H2O provides off-the-shelf ML algorithms, like GLM, XGBoost, Random forest, DNN, GAM, and k-means. And it’s easily extensible with Hadoop, Spark, and other algorithms.

Why H2O is a Good ML Framework:

• Intuitive interface for Java, Python, R, and more
• Comes in two flavors: open-source and paid-for enterprise version
• Useful in fraud detection, credit scoring, price optimization, anomaly detection, KYC, HAI prediction
• Widely used in insurance, finance, retail, and healthcare

Who’s Using H20.AI?

• PayPal
• Capital One
• PwC
• Wells Fargo
• Amanda Health
• Zurich insurance
• Tech Mahindra
• Macy’s
• Booking.com

When Should You Avoid Using H2O.AI?

H20 isn’t the best for parallel running of multiple models while LDA and GMMs clusterings are also unavailable. If performance is your priority, consider open-source ML frameworks, like SciKit Learn, PyTorch, or TensorFlow.

And if you prefer a strong community, consider one of the ML frameworks listed earlier in this document.

Apache MXNET

Apache MXNET is an open-source, lightweight machine learning framework developed in partnership between Intel, the Apache software foundation, and an open-source community. 

With 19.2k+ GitHub stars, the framework enjoys vast community support.

Why MXNET is a Good ML Framework:

• Deeply integrated with Python
• Provides bindings for Java, Julia, Perl, Scala, C++, and JS
• A rich library and robust ecosystem of tools enable engineers to build and train models for CV, NLP, and time-series
• Supports distributed training — and recently introduced support for HOROVOD (Uber’s open source ML framework)
• Gluon API powers seamless transitions between an imperative and symbolic mode for boosted speed as well as flexibility

Who’s Using Apache MXNET?

• ByteDance
• Samsung
• Amazon
• Acer
• CMU
• MediaNet

When Should You Avoid using Apache MXNET?

If you need more off-the-shelf features, there are frameworks that ship ‘with batteries included.’ And consider using SciKit Learn or Keras for research and prototyping.

FastAI

FastAI enjoys 20k+ GitHub stars. Its mission is to democratize deep learning through its free MOOCs (massive open online courses).

FastAI boasts high-level components for building quick ML model prototypes — alongside low-level component access for researchers to experiment and build models with innovative approaches.

Why FastAI is a Good ML Framework:

• Built-in algorithms, with state-of-the-art configuration, mean you can avoid building and, instead, test ready-made models as a solution
• Enjoys a decoupled abstraction of DL and data processing techniques
• Boasts the robustness of Python and the flexibility of PyTorch
• Easy to migrate from sister libraries
• Start learning without downloading (using Google Colab)
• Optimized for effective GPU utilization

When Should You Avoid using FastAI?

Some claim it’s difficult to implement low-level functionalities using FastAI, so it should be preferred more for research and learning and less for production.

Apache MLlib

Apache MLlib is part of the Apache Spark project. Apache Spark is highly popular among big data engineers, earning 28k+ stars on GitHub. 

Why Apache MLlib is a Good ML Framework?

• Highly scalable
• Easy to integrate with Hadoop
• Use it with your preferred language: supports Java, R, Python, and Scala
• Models can run on standalone clusters in EC2 — or Hadoop YARN, Mesos, or Kubernetes
• Has a colossal algorithm library of classification, regression, decision trees, recommendation, k-means-clustering, GMM, LDA, and more
• Leverages iteration and claims to be 100x faster than MapReduce (which uses pass approximations)
• Workflow includes standardization, normalization, and other feature transformations
• Includes tools for constructing, evaluating and tuning, and saving pipelines
• Provides utility tools for linear algebra, handling of data, and more

When Should You Avoid using Apache MLlib?

Apache MLlib lags in evaluation and result representation as its API doesn’t support direct extraction of probabilities with multiclass (Top-k error). The confusion matrix isn’t well displayed either — while for smaller data-sets, we prefer SciKit Learn.

Pytorch-lightning

PyTorch-lightning is a lightweight PyTorch wrapper for high-performance AI research. 

It scores 11k+ stars on Github — and was designed for developing self-contained deep learning systems with highly-fragmented code to ensure maximum flexibility and minimum boilerplate code. 

Why PyTorch-lightning is a Good ML Framework:

• Great for AI researchers and equally valuable for those new to AI-ML development
• Used by professional production teams who struggle to scale ML models with PyTorch without introducing bugs
• Organizes code into “engineering, research, non-essential research and data” segments, enabling an abstraction of the details about model training and making research projects readable, iterable, and scalable
• GPU/TPU training, distributed cluster training, logging, visualizing, and checkpointing helps automate 40+ parts of machine learning projects, meaning fewer errors and faster development
• Provides out-of-the-box integration with logging/visualization frameworks like tensorBoard and Neptune

Who’s using Pytorch-lightning?

• Microsoft
• Intel Labs
• Facebook Research
• ML flow
• NVIDIA
• Allen AI  

When Should You Avoid using Pytorch-lightning?

It helps to remember that Pytorch-lightning is just a high-level wrapper for PyTorch. If your organization has Pytorch talent, it makes sense to use it. — if not, look elsewhere. For smaller datasets, consider other solutions such as Keras.

Microsoft Cognitive Toolkit (CNTK)

CNTK is a robust framework with community-driven popularity of 17k+ stars on GitHub. But be wary that this unified toolkit doesn’t support model deployment to mobile devices.

Why CNTK is a Good ML Framework:

• Integrate as a library in C#, C++, and python
• Functions as a full-blown ML framework too
• Easy to leverage DNN, CNN, RNN, and more
• CNTK Python API provides abstractions for computing and learning algorithms, with in-built scalable data readers optimized for different types of data, including text, image, and speech, eliminating the need to author any data reading code
• API abstraction makes it flexible and compact for engineers to train arbitrary neural networks (ANN)
• Other than Python, API abstraction is available for C++, Java, and C#/.Net 
• Suited for 64-bit Linux or Windows environment

Who’s Using CNTK?

• Sotong Kitchen
• Spark Cognition
• Boston Scientific
• NVIDIA
• Seattle Genetics
• Amazon

When Should You Avoid using CNTK?

The last stable release of CNTK was 19 months ago, highlighting the framework’s not-so-frequent updates. It’s also not ideal for delivering ML capabilities to mobile devices.

In that instance, Keras, IBM Watson, Azure ML, Google AI, and TensorFlow are good alternatives.

Summary

We’re sure that trends will change dynamically, so what’s top of mind this year may lose popularity the next.

If you want to read more interesting information from the world of Machine Learning — visit our blog.