Now, after many computing tasks being taken over by cloud systems, it is time for the data analysis and intelligent data-driven algorithms.
Yes, thanks to a few top-notch cloud services, now we can enjoy smart analytics equipped with artificial intelligence and machine learning to reach us as standalone services.
By the cloud services capable of delivering machine learning inputs finally made remote data analytics a reality.
Many reasons explain the effective role of the cloud for data analysis. Cloud is ideal for utilizing large data sets on rented hardware and accomplishes larger tasks at a much faster pace.
Moreover, cloud services come as cheaper and affordable solutions besides being the least time-consuming. The only worry concerning cloud services is privacy.
But as cloud services are getting popular and big players have already made their presence, an industry-wide best practice protocol can come as a safeguard against such worries concerning privacy.
Finally, if you want to utilize the potential of your data sets with the latest and intelligent analytics, the cloud-based services working with machine learning algorithms can be the best solution to opt for.
4 Top Cloud Services to Boost Your Machine Learning-Based Solutions
Let us introduce here four top cloud services based on AutoML solutions to deliver your machine learning inputs
1. Amazon SageMaker
Amazon built SageMaker with the objective of providing easy to use machine learning tools for diverse data analytics tasks.
Amazon SageMaker has the combined power of a variety of AWS storage options and capability to channelize into robust and well-known machine learning libraries such as TensorFlow, Chainer, etc.
Without getting mired in the process related concerns, the SageMaker users can give attention to the algorithms working with data stored in Amazon’s machines.
Let’s have a look at the key highlights of the Amazon Sagemaker,
- Amazon Sagemaker offers environments that most developers are habituated with. You get Jupyter Notebook, python, and the likes.
- The developers also can work with well-known and widely used ML frameworks such as Apache MXNet, TensorFlow, etc.
- One can also incorporate other frameworks and libraries of his or her choice.
- Sagemaker offers a zero-configuration workflow for the purpose of training.
- Sagemaker comes equipped with out-of-the-box support system training modules.
- With Sagemaker you can deploy the trained models straight to production.
2. Azure Machine Learning Studio
Microsoft’s Azure Machine Learning Studio, which is created as a really sophisticated and future-ready toolset to decipher relevant signals hidden inside data, has quickly gained traction among data scientists and algorithm developers around the world.
Azure Machine Learning offers a very user-friendly drag-and-drop interface to build sophisticated flowcharts and present your numbers in a visually meaningful way.
Though Azure says coding is unnecessary to utilize the service, a programming approach can only help you get more out of your data.
Let’s see the key highlights of the Azure Machine Learning Studio.
- Recently, they equipped the service with a full-blown training infrastructure to run training jobs for the local machines.
- When you are capable of scaling up, the automated Machine Learning of Azure will help you to run parallel multiple training jobs by using Azure Databricks clusters or Azure Machine Learning Compute.
- The automated ML of Azure now allows the data scientists to train local computers with the data completely remaining confined within the local storage and security protocols.
- The automated ML of Azure now takes care of an array of pre-processing tasks like new feature generation, data cleaning, transformation, etc.
- Automated ML now offers richly layered visual charts for easier comparison of models and various metrics.
- Azure Machine Learning now supports Forecasting for different business models taking time and performance optimization into consideration.
- The new ability to explain the relationship between model output and input variables will give a deeper understanding of the impact of models.
3. Google Cloud Machine Learning Engine
Google, after putting heavy money into the most acclaimed Machine Learning library of TensorFlow, is now allowing us to do data-analytics experiments with the TensorFlow through Google’s new cloud-based Machine Learning engine.
Many tools offered through the Google Cloud Machine Learning Engine come as open source and free for the software and app developers. This makes Google’s cloud-based machine learning engine a relatively low-cost solution for enterprises looking for more insights from their data.
Let’s have a look at the key highlights of the Google Cloud Machine Learning Engine.
- Besides full support for the acclaimed frameworks such as TensorFlow, you are allowed to run other frameworks on Cloud ML Engine.
- Distributed training of Cloud ML Engine allows setting up an environment with multiple machines while trying to train the machine with large data sets.
- Being a fully managed service, it ensures automatically provisioning resources by utilizing distributed training infrastructure supporting CPUs, GPUs, and TPUs. This helps you concentrate on developing models without the worry of infrastructure.
- HyperTune is a key feature of Google Cloud Machine Learning Engine that ensures faster and better results through tuning the machines with deep learning hyperparameters.
- Portable models help locally utilizing ML frameworks on sample datasets before scaling up.
4. IBM Watson Studio
Watson Studio embodies the real and most ambitious push of IBM into the arena of artificial intelligence and machine learning algorithms.
IBM Watson Studio has emerged as one of the leading toolsets to explore data and model training either in the cloud or on-premises. It can deliver the most sophisticated visual charts and graphics data presentations with relevant insights that businesses can readily use.
Here are the key highlights of the IBM Watson Studio.
- AutoAI of Watson is capable of automated data preparation, engineering features based on data-driven insights, optimization of the hyperparameters optimization, and bringing together the best models and deploying them easily.
- Watson makes use of collaborative data science through open source tools such as Jupyter Notebook and an array of other collaborative project features.
- You can easily create models by using the drag and drop modeler and explore data in a customized way.
- Watson allows creating a visual program with drag and drops for deep learning.
- Lastly, Forrester already ranked IBM Watson Studio as the leading cloud-based predictive analytics and machine learning solution.
The popularity of machine learning algorithms is steadily on the rise and this is the right time for leading tech companies to step in with competitive machine learning solutions.
The cloud services for machine learning mentioned above are just pushing the last barrier to make intelligent analytics available for every business entity.
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Juned Ghanchi is an IT consultant by profession and is working with IndianAppDevelopers, a leading Android app development company. He has more than 5 years of experience in developing and implementing marketing strategies.