Machine learning is all the rage right now. It claims to revolutionize the way computers will work. Top tech companies are hiring washed-up statisticians for millions of dollars specifically to build the machine learning programs. Universities offer machine learning classes, machine learning majors, and machine learning departments. Governments and militaries are crafting labyrinth plans to adjust to the threat of machine learning and use the new technology to gain tactical advantages.
Machine learning is set to change the way we do business. Machine learning combines mathematics, statistics, and artificial intelligence into a new discipline of study. Big data and faster computing power are opening up new capabilities for this technology that seemed impossible only a decade ago. It is being used to drive cars, recognize faces, trade stocks, and invent lifesaving medicines.
Machine Learning as a Service
Seeing the need for their massive infrastructure, the largest providers of cloud computing have cashed in on this new trend to use huge amounts of data and power and provide these machine learning capabilities as a service to smaller businesses and companies. This Machine Learning as a Service (MLaaS) is opening up opportunities for entrepreneurs and problem solvers previously only available to tech giants. Machine Learning as a Service is expected to reach $20 billion in revenue in 2021.
These MLaaS services that include predictive analytics, data mining, facial recognition, natural language processing, recommendation providers, and expert systems. These businesses can get started right away without purchasing huge servers or developing complicated algorithms. They can also benefit from the services of teams of the world’s top engineers without adding any new hires. This means implementing new systems, streamlining operations, and developing the newest innovative technology.
Understanding the Offerings in Machine Learning as a Service(MLaaS)
At ISHIR, we provide both of the leading, state-of-the-art providers of MLaaS. These are AWS Machine Learning and Microsoft Azure Machine Learning Studio. Both of these technologies have the potential to transform your organization but choosing the right one can make all the difference. To a hammer, everything looks like a nail, but to get the most out of these platforms, engineers must appreciate the wide variety of tools offered by each.
AWS Machine Learning
Amazon Web Services has quickly become the world’s largest cloud-computing platform. To build this infrastructure, Amazon maintains some of the biggest server farms in dozens of countries across the globe. Amazon Web Services(AWS) offerings include Analytics, Application Integration, AR & VR, AWS Cost Management, Blockchain, Business Applications, Compute, Customer Engagement, Database, Developer Tools, End User Computing, Game Tech, Internet of Things, Machine Learning, Management & Governance, Media Services, Migration & Transfer, Mobile, Networking & Content Delivery, Quantum Technologies, Robotics, Satellite, Security & Compliance, and Storage.
AWS Machine Learning is composed of two game-changing offerings, Amazon ML for predictive analytics and Amazon SageMaker.
Amazon ML for predictive analytics is highly user friendly and quick to implement. It can be added to a project under almost any deadline. Currently, Amazon ML for predictive analytics works only for supervised learning, and not for unsupervised learning. It can perform classification and regression. For example, the Amazon Rekognition service works on images without any training for more general off-the-shelf objects. Gradient descent algorithms can allow for hyper-detailed data mining. One great advantage of Amazon ML compared to in house solutions is that the AWS cloud computing platform allows this software to grow as your needs do.
SageMaker was launched in 2017 for data scientists. It is a highly scalable software development platform to build and train machine learning models. It provides Jupyter to allow for streamlined data analysis. SageMaker also offers many cutting edge algorithms such as an image classification algorithm for use in computer vision. Their object detection algorithm tasks this image classification ability a step further by outlining the boundaries and shapes of the objects they classify. A built-in algorithm called Single-Shot Multi-Box Detector allows for this recognition to work on multiple objects in the same picture. The Neural Topic Model algorithm defines the topics of documents based on top ranking words. The Blazing Text algorithm uses Word2Vec for natural language processing.
Microsoft Azure Machine Learning
Microsoft Azure Machine Learning is the biggest competitor to Amazon’s offerings. Azure Machine Learning is seen as more flexible compared to AWSThey offer one powerful platform that works for both novice developers and expert data engineers. The platform is composed of two parts, Azure Machine Learning Studio, and Bot Service.
Microsoft Azure ML Studio contains over 100 methods for classification, regression, detection, and text analysis. This all works with a Python SDK and a graphical interface. The Azure ML Studio gets you access to the Cortana Intelligence Gallery. These offer great reusable solutions for data scientists. The Azure ML workspaces offer a great user interface for machine learning programming. This makes it easy to track model development over time.
Azure Machine Learning Bot Service is a newer tool. It builds scalable models for end-to-end lifecycle management. It’s a great service for both IoT devices and on-premise computing needs. This works on any tool or framework. It’s used in scientific computing and custom model building. Unlike ML Studio, it offers popular frameworks like TensorFlow and works with third-party services like Docker. Users can build bots with Node.js and .Net software frameworks.
Interesting Read: What Machine Learning can do and cannot do?
How can Machine Learning as a Service helps your business?
1. Machine learning software understands natural language
Natural language processing is the holy grail of machine learning. This technology is being used to perform customer service, “talk” to new clients, speed up transactions, and serve as personal assistants. These technologies have become ubiquitous in human resource management and will only get better.
2. Machine learning helps you automate tasks and save time
Machine learning is being used to automate tasks such as product recommendation, manual data entry, and detecting spam. In the past, these processes would have been tedious and laborious work for large teams of employees. Machine learning can cheaply and effortlessly perform these simple tasks.
3. Machine learning helps in making expert decisions
Expert decision making often requires a complex tree of knowledge bases utilizing numerous rules. These processes can often be programmed into machine learning systems. These machine learning systems can find hidden correlations between variables and outperform experts in their domains.
4. Machine learning software makes your transportation and logistics more efficient
Transportation and logistics require synchronizing dozens of complex supply chain processes. The situations involved in these decisions often change too rapidly for human supply chain engineers to adapt to. Machine learning can instantly and automatically innovate complex supply chain processes for particular situations.
5. Machine learning predicts customer churn
Customer churn is one of the biggest challenges for business, especially growing startups. Machine learning algorithms can examine complex risk factors for churn and help identify which customers need the most support. These customers can be targeted for advanced help or special deals.
6. Machine learning prevents equipment breakdown using predictive analysis
Machine learning technology is being added to the factory floor. These innovations can monitor the machines used in manufacturing processes to provide insights into the best and most cost-effective maintenance procedures. These insights can double the lifespan of valuable business equipment.
7. Machine learning helps you learn more about your customers
AI is being used to analyze customer profiles and learn more about your client’s desires and habits. This software can offer insights valuable for product development and recommendation. The insights can help determine the winning, data-driven strategies for C-suite executives and business developers.
8. Machine learning makes your infrastructure more secure
The latest generation of cyber intelligence depends on machine learning. Machine learning can find hidden risks in data and analyze millions of transactions per second. This technology is a crucial piece of any cybersecurity plan.
9. Machine learning helps in recognizing images
Image recognition is powering the next generation of robots. Factories and manufacturing plants use these algorithms to cut down on accidents and automate processes. Organizations have already found thousands of uses for human-level image recognition algorithms.
10. Machine learning provides financial analysis
AI can predict market trends years in advance. Large data sets have insights into spending habits and future accounting needs that experts often fail to recognize. Companies can save millions with state-of-the-art financial services powered by machine learning.
Organizations ranging from Fortune 500 companies to one-person startups are adding AWS Machine Learning and Microsoft Azure MLS to their value proposition. The bar to entry in the machine learning space has been lowered from impossible to reach to necessary to join. For most companies, it’s not a question of if machine learning is needed but what sort of machine learning is needed and how much. AWS and Azure both offer great services and frameworks but only a skilled designer can weigh the costs and benefits to figure out which service is right for you. In many cases, complicated projects will require a mixture of both of these cutting edge platforms. These quickly adapting programs might be easy to use but they need to be incorporated into stable and reliable software which is no easy task.
Only an expert can help you leverage these complicated and fast-changing innovations. The difference between a good programmer and a great programmer is the ability to quickly adapt to the fast-changing pace of these frameworks and design technology that pushes the limits of innovation. This new technology can change your business and you won’t want to miss out!