In the current times, Machine Learning is one of the fastest-growing technologies globally. Machine learning applications and custom solutions are gaining huge momentum and all thanks to the robust machine learning frameworks. Most of the big companies are considering these powerful and smart machine learning frameworks to automate their operations, optimize the process, and most importantly, get an edge over the competition.
As per a report, the total funding during the pandemic year in machine learning was around $28.5 billion. Moreover, Fortune Business Insights also predicts that the machine learning development market will reach $117.19 billion by 2027! That’s huge as compared to other tech trends of the market.
But, how to choose the best machine learning framework that could satisfy your project needs in 2022? Well, here’s the answer to it!
In this blog, we will discuss the best machine learning frameworks, along with their features and pros & cons. But, first let’s get to know the Machine Learning Framework in brief.
What is a Machine Learning Framework?
It is not new that machine learning uses several algorithms to automate the various business operations and make machines mimic human behavior. And unless you are an expert in ML, these machine learning algorithms are very difficult to understand. So, machine learning frameworks simplify these complex algorithms so that you can easily implement ML in your enterprise applications.
In other words, machine learning frameworks are an interface with which you can seamlessly deploy smart machine learning models for faster and automated working. Besides providing a faster working environment, machine learning frameworks even facilitate automation that could help you to step into a completely digital business era. What’s your thought on implementing machine learning solutions in your business?
Best 7 Machine Learning Frameworks for 2022
Tensor Flow
Developed by Google, Tensorflow is an end-to-end open-source machine learning platform. It offers comprehensive, flexible tools and libraries that make it easier for researchers to push information to the ML models and developers to build and deploy applications.
Features of Tensorflow includes:
- Easy Model Building: Leverage high-level APIs like Keras with eager execution to stimulate quick model iteration and easy debugging.
- Robust ML Production Anywhere: Tensorflow makes it easier to train and deploy models in the cloud, on-premise, in a browser, or in the device regardless of the language.
- Powerful Experimentation for Research: Facilitates a simple architecture that makes it easier to conceptualize and code them as well as publish them faster.
Pros of Tensor flow:
- Easier to use, with a lot of tutorials to get users on board.
- Tooling around TensorFlow, such as TensorBoard, is very easy.
- Backed by strong community support
- Provides robust experimentation for researching
- Flexible
Cons of TensorFlow:
- Not so suitable for beginner TensorFlow developer, hard to fix bugs.
- A graphic interface to create layers can help beginners.
- Detailed tutorials on what goes behind the scenes in each layer. Currently, the tutorials don’t focus on that.
- Better support to integrate with files on the cloud.
Apache Mahout
Apache Mahout can be described as a robust, scalable machine learning library that runs on Hadoop MapReduce. It is popularly known as a distributed linear algebra framework and mathematically expressive Scala DSL, specifically tailored for mathematicians, statisticians, and data scientists to quickly implement their own algorithms.
Features of Apache Mahout include:
- As Mahout algorithms are written on the top of Hadoop, they operate well in a distributed environment. Therefore, it uses the Apache Hadoop Library to scale effectively in the cloud.
- It offers an easy-to-use framework that makes data mining for large chunks of data easier.
- Even for large data sets, data analyses can be done effectively and quickly.
- Include many MapRudece enabled clustering implementations such as k-means, fuzzy k-means, Canopy, Dirichlet, and Mean-Shift.
- Supports distributed Naive Bayes and Complementary Naive Bayes classification implementations.
- Comes with distributed fitness function capabilities for evolutionary programming.
- Includes matrix and vector libraries.
Pros of using Apache Mahout:
- Offers the coder a read-to-use framework for mining a large amount of data
- It makes the analysis of large data sets effective and quick.
- Comes with distributed fitness function capabilities for evolutionary programming.
Cons of Apache Mahout:
- Poor visualization and less support for scientific libraries.
Sci-Kit Learns
Sci-kit learn is a free-to-use machine learning module built on SciPy to be used with Python. It is a simple but effective data mining and data analysis tool. Released under the BSD license, it can be utilized for both commercial and personal purposes. Through sci-kit-learn, users can conduct various types of tasks such as preprocessing, clustering, model selection, and a lot more. This module is equipped for completing implementations as well. Furthermore, sci-kit-learn can fulfill a wide variety of purposes such as hotel bookings, music streaming, and others. Thanks to this, users can incorporate algorithms in the system into their very own application.
Features of Sci-Kit Learns
- Data Splitting: It allows spitting of datasets for training and testing. It is also needed for unbiased evaluation of prediction performance.
- Linear Regression: Allow users to forecast sales in the next months by analyzing the data of sales of the previous months.
- Decision Trees: Allow users to do classification and regression problems. Users can easily make decisions with a tree-model to predict the output.
- Random Forest: Allow users to build hundreds/thousands of decision trees that are used to build the model.
Pros of Sci-kit Learns:
- Supervised learning algorithms: Any supervised Machine Learning algorithm that you may have heard of has a very high possibility of belonging to the sci-kit-learn library. The sci-kit-learn toolkit has a repertoire of supervised learning algorithms, which includes – Generalized linear models such as Linear regression, Decision Trees, Support Vector Machines, and Bayesian methods.
- Unsupervised learning algorithms: This algorithm collection includes factoring, cluster analysis, principal component analysis, and unsupervised neural networks.
- Feature extraction: You can extract features from text and images using sci-kit-learn.
- Cross-validation: The accuracy and validity of supervised models on unseen data can be checked with the help of sci-kit-learn.
- Dimensionality Reduction: With this feature, the number of attributes in data can be reduced for subsequent visualization, summarization, and feature selection.
- Clustering: This feature allows the grouping of unlabeled data.
- Ensemble methods: The predictions of several supervised models can be combined by using this feature.
Cons of Scikit-learn:
- Not suitable for deep learning.
PyTorch
PyTorch’s creators say that they have a philosophy – they want to be imperative. This means that we run our computation immediately. This fits right into the python programming methodology, as you don’t have to wait for the whole code to be written before getting to know if it works or not. you can easily run a part of the code and inspect it in real-time.
PyTorch is a python based library built to provide flexibility as a deep learning development platform. The workflow of PyTorch is as close as you can get to Python’s scientific computing library – NumPynumpy.
This framework was developed by Facebook, which is an open-source and scientific computing framework. Pytorch is a scripting language that is based on the LUA programming language. Pytorch offers various algorithms and scripting languages.
Features of PyTorch include:
- Its API is very simple to use.
- PyTorch uses python integrations coupled with a data science stack.
- It helps in building computational graphs whenever you want and in a simple way.
- You can change your graph if you want to use PyTorch, even at runtime.
- It offers a simple interface with APIs. Operations and execution are similar to Python.
- It leverages services and operations provided by Python.
Pros of PyTorch:
- Flexible
- Easily customizable
- Used to design neural networks
- Better than tension flow
Cons of PyTorch:
- Complex
- Required lots of coding
Google Cloud ML Engine
Google Cloud Machine Learning (ML) Engine is a managed service that allows developers and data scientists to build and deploy cutting-edge machine learning models. It offers training and prediction services that can be either used individually or in collaboration. It is a leading machine learning framework choice used by enterprises to overcome challenges such as identifying clouds in satellite images, food safety, and improving time to respond to customer emails at least four times faster.
Features of Cloud ML includes:
- HyperTune
- Cloud Datalab
- Portable Models
- Prediction Services
- TensorFlow Framework
- Machine Learning Samples Sharing
Pros of Google Cloud ML Engine:
- Huge community
- A rich documentation
- Easy to integrate with other software
- Free and open-source
- Powerful features
- Constantly getting better with community contributions.
Cons of Google Cloud ML Engine:
- The learning curve is a little bit steep; however, there are many free resources on the Internet.
- The product is getting lots of changes and contributions from the community, so it’s hard to follow what is going on with the product all the time.
H2O
AutoML or Automatic Machine Learning is the process of automating algorithm selection, feature generation, hyperparameter tuning, iterative modeling, and model assessment. AutoML makes it easy to train and evaluate machine learning models. Automating repetitive tasks allows people to focus on the data and the business problems they are trying to solve.
Features of H2O:
- Open Source Machine Learning Framework
- Distributed and In-Memory Processing
- Automated and Smart Algorithms
- Easy to Build Models with Existing Languages
Pros of H2O:
- Excellent analytical and prediction tool
- In the beginning, usage of H20 Flow in Web UI enables quick development and sharing of the analytical model.
- Readily available algorithms, easy to use in your analytical projects
- Faster than Python sci-kit learn (in machine learning supervised learning area)
- It can be accessed (run) from Python, not only JAVA, etc.
- Well documented and suitable for fast training or self-studying
- In the beginning, one can use the clickable Flow interface (WEB UI) and later move to a Python console. There is then no need to click in H20 Flow
- It can be used as open-source
Cons of H2O:
This is not really a drawback, but rather a warning – the Driverless AI is not a replacement for a data scientist yet, and will not replace data scientists in the next decade either. The Driverless AI feature delivers reliable results only if the analyst is sure about the meaning of input data. Data quality is usually a major issue, and no tool can detect the meaning of data in the input. Data scientists are also required for the business interpretation of the findings. So be careful, and do not rely on this feature without a good understanding of what it really does in each step.
Microsoft Cognitive Tool
Microsoft Cognitive Services is a purpose-built collection of AI algorithms and application programming interfaces (APIs) to help developers add AI capabilities to websites, apps, and AI agents. Cognitive Services allow enterprises lacking the necessary AI research staff, infrastructure, and budget to select AI tools for their needs. Users can choose from a library of Microsoft’s AI algorithms for Cognitive Services on Azure, Microsoft’s public cloud computing platform. Like most major cloud service providers, Microsoft has become a cloud-based provider of AI development services. Cognitive Services represent one of the largest suites of AI capabilities and include deep learning capabilities, machine vision, and sentiment evaluation.
Most tools included in Cognitive Services are compatible with Microsoft’s natural language understanding (NLU) AI based chatbot. Offered as Azure Bot Service, the chatbot is positioned as a tool to take service and reception calls and moderate websites and forums.
Features of Microsoft Cognitive Toolkit:
- Most tools are compatible with Microsoft’s natural language understanding (NLU) AI chatbot.
- The chatbot takes service and reception calls and moderates websites and forums.
- Open-source Tool Kit for Distributed Deep Learning
Pros:
It deals with multi-dimensional and large-scale data sets, uses C++ and Python.
Cons:
Slightly slower than other machine learning frameworks.
Wrapping Up
With this, we have come to the end of our best Machine Learning Frameworks list. Now you have enough options to select the right framework to leverage Machine learning development services. If you started using Machine learning for the first time in 2022, then it is the best starting point for your projects.
If you want any help in machine learning frameworks, Matellio can help you with all your digital needs. We have a team of dedicated developers with wide expertise in the field of software development. Matellio has more than a decade of experience in the field of software development. Our dedicated developers have developed several digital solutions for our customers. Our flexible engagement models keep your budget in check. Connect with our experts over a 30-min free consultation call to know more about our process and validate your idea!
