You must have heard about Artificial Intelligence (AI), though may not be in detail. The technology sector is advancing at a rapid pace and it is hard to keep up with all the latest AI trends evolving. Some of the fascinating initial applications of AI include facial recognition, virtual assistants, smart appliances, automatic spam filtering in your email, and self-driving cars. Well, this is just the beginning, AI is poised to go much ahead and offer even smarter products to consumers. The more sophisticated and high-end the products are, the more complex are the efforts put by AI specialists on it.
If you are just starting out or looking for a career switch, then AI and its subset machine learning may be the best field for you. There is no need to dive into the internet and look for reliable study materials in order to start learning AI. You may just take the wise step of enrolling in AI courses online which can help you gain the exact skills that employers seek for their AI-related posts. One of the important benefits of taking these courses is they help you become well-versed in using different AI tools and frameworks. AI engineers use them to develop AI-related products from scratch.
This article lets you know what are the top AI tools and frameworks the knowledge of which can help you land your dream job.
Top AI Tools and Frameworks
Today, you can watch AI at work through its important facets called machine learning and deep learning. The top AI platforms today are not only confined to offering machine learning solutions but also help scale your data science efforts.
Here is a list of top AI tools and platforms that dominate the AI market.
Developed by none other than the Google Brain Team, TensorFlow is an end-to-end machine learning platform. It has powerful tools, libraries, and community resources that allow effortless deployment of machine learning powered applications. It offers high-level APIs for model iteration and easy debugging and allows models to be trained in the browser, on-premises, or even in the cloud.
It is an open-source machine learning library for Python language built on NumPy, SciPy, and matplotlib. Developers use this simple and efficient tool for predictive data analysis, classification, regression, clustering, dimensionality reduction, selecting models, and more. The development and maintenance of ScikitLearn is financially supported by renowned names like Intel, Microsoft, Columbia University, and Fujitsu.
Google Cloud AI Platform
The AI Platform offered by Google is the end-to-end solution for data science and machine learning. Developers, data scientists, and ML engineers can use it to build, deploy, and manage machine learning models. These models can be even built without writing a single line of code using AutoML. After building the model, one can validate it through AI Explanations, What-If tool, and Vizier.
Declared a visionary in the 2020 Gartner Magic Quadrant for Data Science and Machine Learning platforms, RapidMiner is a popular tool for building ML models. One can design models in it using a visual workflow designer or automated modeling. These models can then be deployed, managed, and turned into perspective actions. RapidMiner is being widely adopted in sectors like Retail, eCommerce, financial services, healthcare, manufacturing, and more.
AWS AI Services
When you look for a cloud service provider, AWS or Amazon Web Services will be the first name you’ll come across. Its AI services are known for offering ready-made intelligence for your workflows and applications. Users don’t even need to have prior experience of machine learning to use these services. Automated code reviews, chatbots, document analysis, fraud prevention, and real-time translation are some of the top AI services that AWS provides.
Databricks Unified Analytics Platform
If you want to use data and AI together to unlock groundbreaking innovations, then Databricks is the platform for you. Powered by Apache Spark, the platform allows seamless building, training, and deployment of machine learning and deep learning models at scale. Its AutoML product automates Machine Learning pipelines from right from feature engineering, model search, to hyperparameter tuning, and inference. You can also explore its MLflow and Databricks Runtime for Machine Learning environments that are optimized for machine learning.
H2O Driverless AI
As described on the official website, H2O offers a full suite of products that companies and individuals use to accelerate their adoption of AI and machine learning. Its Driverless AI platform uses automation to accomplish major machine learning tasks within minutes or hours. It employs a library of algorithms and feature transformations to automatically engineer innovative, high-value features for a given dataset. The models that you build can be automatically deployed across a number of environment choices.
Over to You
We have tried to give you a complete overview of the top AI tools one should know about. The list isn’t exhaustive and there are other AI solutions providers as well. So, now when you have your AI interview scheduled, you would know that knowledge of using these platforms can give you a competitive advantage over your peers. You can further prove your credibility by taking an online AI course and polishing your skills.