by Anika D | Aug 12, 2022 | Machine Learning
Machine Learning is all over the place; therefore, it is important to capture best practices and solutions to solve common ML problems. One of the simplest ways to catch these problems and provide answers is to design patterns.
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“Design patterns” are the best practices used by programmers to solve common problems while designing a system or an application. Below are a few design patterns that you must try:
Problem Representation Design Pattern: Rebalancing
The common scenario in classification problems like Fraud Detection, Spam Detection, or Anomaly Detection is Imbalanced datasets. But typical Machine Learning models for such classification work by assuming that all the classes are balanced and result in poor predictive performance. Some of the common strategies to handle imbalanced datasets are as follows:
- Choose Right Performance Metric: AUC or F1 scores are efficient for performance evaluation as the goal is to maximize precision and recall.
- Sampling Methods: Resampling is widely adopted to balance the dataset classes’ samples.
- Weighted Classes: It involves penalized learning algorithms to increase the misclassification cost in the minority class.
Reproducibility Design Pattern: Transform
The idea of this reproducibility design pattern is to separate input from features. You have to extra features from raw input to train a model. But in most ML problems, you can not use input directly as feature. Instead, you must apply various transformations like scaling, standardization, encoding, and others to reproduce them at the prediction time. So, it is vital to separate the inputs from the features, encapsulate the preprocessing steps, and include them into the model to ensure reproducibility.
Model Training Design Pattern: Checkpoints
The major attributes of a scalable system are resilience and fault tolerance. The snapshot of the model’s internal state is the checkpoint. So, you can resume training from this state to another later. However, during the activity, power outages, task preemptions, OS faults, and other unforeseen errors can happen, resulting in time and resource loss.
Reproducibility Design Pattern: Workflow Pipeline
The goal of the design pattern is to isolate and containerize the individual steps of a machine learning workflow into an organized workflow. It ensures scalability and maintainability. Generally, the machine learning development workflow is generally monolithic, containing a series of tasks from data collection to model training and evaluation. But the machine learning tasks are iterative.
Tracking the small changes in the workflow during the development process becomes complicated as the process iterates many times. It introduces the concept of MLOps, similar to DevOps concepts like continuous integration and continuous delivery. The key difference between MLOps and DevOps is that it is not only the code. It is also the data that must be continuously tested and validated in MLOps.
Responsible AI Design Pattern: Explainable Predictions
Generally, machine learning models are black boxes. But it is important to clearly understand the model behavior to diagnose the errors and identify potential biases. The major factor in Responsible Artificial Intelligence is introducing explainability in machine learning. Hence, the key idea is to interpret the machine learning models to understand why and how the model made the predictions in a certain way.
Final Words
You can implement the above-mentioned techniques in most machine learning practices, but defining design patterns helps to create general reusable solutions for common problems. In addition, they help to communicate with engineers and solve problems by providing off-the-shelf solutions.
by Anika D | Jun 25, 2022 | Data Science, Machine Learning
Machine learning and data science are a combination of future tech because they will revolutionize every aspect of how enterprises work. It will make data center management more robot, efficient and amazing. So, dive in here to know how machine learning revolutionizes data center management. Let’s have a look!
Machine Learning is Revolutionizing Data Center Management
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Machine learning will significantly change the data center economy and pave the way for an improved future. The goal is to build applications that can carry data-driven decisions and outgrow the present capabilities. In addition, it will use data management and cloud services to make the application and process independent.
For example, Digital Realty Trust, a leading global colocation provider, recently started executing machine learning. As a result, the tech of ML with data management was limitless and very efficient. It helps to ease the workforce, dedicated time, and infrastructure. Its amazing real-time processing, evaluation, and results are only and only benefiting enterprises. In addition, there was $28.5 billion in global funding to Ml alone in 2019. So, let’s have a look at its amazing benefits.
Some of the machine learning and data center management uses are:
Making Data Centers More Independent
Businesses can automate machine learning with data management to create an environment where it autonomously manages the physical surroundings. Instead of spending on software alerts, data centers can become software making changes to the architecture and physical layout of the data center in real-time. With the future of data management, data fabrics can become more powerful with this joint venture.
Minimizing Operational Risk
The most crucial situation is an operational emergency where your system faces downtime. But, thanks to machine learning, power data management center, and machine learning. Fortunately, it can track performance data from crucial components and helps to cool, manage power, and give necessary forecasts. As a result, you can precautionarily maintain software and hardware while working on data center operators. It will reduce overhead costs and help keep your workflow.
Managing Finances
Do you know 92% accuracy was demonstrated when using machine learning to predict the mortality of COVID-19 patients? So, with such great predictions, the machine learning, operational, and performance data from data centers with financial data give some valuable information. Fortunately, you can evaluate applicable taxes and the cost of renovating and buying new IT equipment.
Effective Planning
Data centers and machine learning can come together to predict problems in space, power, cooling, and other resources beforehand. Luckily, this is great for any business. Not only this, but it can evaluate the costs, possible outcomes, and other factors in case the data center has to shift. These data-driven decisions with the help of machine learning in data management are a blessing for companies. Lastly, it will maximize the outcome and reduce time spent on these valuations.
Wrapping Up
This is how machine learning is revolutionizing data center management. A promising future lies ahead as real world examples of MI are amazing. It holds some amazing technologies that will revolutionize data science and its working. In addition, these techs are becoming more accurate, affordable, and very advantageous to humans. So, keep reading here for all the updates!
by Anika D | Feb 16, 2022 | Artificial Intelligence, Machine Learning
Various competitive pressures boost the AI companies in making suitable investments to have safe and reliable systems. Companies need to prevent and solve action problems to ensure the responsible development of technology and AI systems. There are various factors that improve the cooperation prospects in action problems.
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When it comes to innovation, research, and standardization, the AI landscape is synergetic. There are many reasons to sustain and enhance cooperation. Some of them are listed below:
AI Development is Resource-Intensive
Cooperation between government and AI researchers and implementing teams across the globe can maximize the scale advantage. The absence of international cooperation leads to duplicative and competitive investments in AI capacity. As a result, it creates unnecessary costs and leaves the government deprived of AI outcomes. Various inputs used in the AI development, like high-quality data access, large-scale computing knowledge benefit from scale.
- International cooperation based on democratic principles can help to focus on building trust and responsible AI development.
Though much progress is made on AI, there are differences between FCAI participants.
AI governance needs to interpret the AI principles to policy, standards, and regulatory frameworks. These need a deep understanding of AI in practice or working through the operation of principles in certain contexts. Also, in the face of unavoidable trade-offs.
- For regulation, divergent approaches create barriers to diffusion and innovation.
Government initiatives to inspire domestic AI development across digital sovereignty concepts can have cynical spillovers. For instance, restrictions on data access, discriminatory investment, data localization, and other requirements.
Similarly diverging risk categorization regimes and governing requirements can boost the costs for seeking businesses to serve the AI market globally. Varying AI regulations can necessitate variations of AI structures that can improve the work important to develop AI systems. It leads to high compliance costs that affect small firms.
Regulating AI Enables Organizations in AI Development
Companies produce business by creating expertise in AI systems, then licensing these to other companies. As AI gets global, a complex assembly of AI systems emerges in various sectors.
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The more open international market will enable a company to make benefit from digital supply chains. Moreover, boosting international competition by specialized firms will inspire more AI innovation and healthier markets.
- Enhanced trade cooperation is vital to prevent unjustified restrictions of flowing data and goods.
It substantially minimizes the prospective gains of AI Diffusion. The strategic importance of sovereignty and data has given a boost to legitimate industrial policy actions directed at mapping. But, protectionist actions can jeopardize international cooperation and impact consumer choice.
- Cooperation between like-minded countries is essential to assert the main principles of openness and democracy protection.
The risks linked with the corrupt use of AI systems by techno authorities expose citizens to potential human rights violations. It also intimidates them to split cyberspace into incompatible technology heaps and fragment the international AI development process.
Final Words
International cooperation is important for most governments. So, AI strategies show that governments respect the connection between AI development and collaboration around borders.
by Anika D | Jan 3, 2022 | Artificial Intelligence, Machine Learning
The capabilities of machine learning increasingly influence the world. Machine learning is expanding the boundaries of what was once thought to be possible. It has permeated our daily lives through the apps we use to automate our daily schedule.
Machine Learning is a big part of pulling information from datasets. Thanks to the volume of data the algorithm is exposed to, they’re used in predicting patterns. This algorithm identifies emerging trends and translates data into consumers’ behavior information.
Let’s look at five real-world examples of machine learning in 2021.
Product Recommendations
This is the easily identifiable element of machine learning. If you’ve used Netflix, then you’re aware of the ‘Since you’ve watched ***’ feature. The Netflix algorithm makes recommendations about the shows you might like. This is also the case with Amazon. It shows you ‘customers who bought this item also bought.
This is possible due to the efforts of machine learning. You can also learn about machine learning through various courses.
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Speech Recognition
Voice-activated virtual assistants like Alexa, Siri, and Google Assistant have fine-tuned speech recognition. Machine learning can also facilitate speech-to-text functions. This has real-world applications in voice search, increasing the accessibility options for disabled people.
Another facet of machine learning is Neural Machine Learning, which can seamlessly translate from one language to another. Moreover, the ability of a computer program to understand spoken and written language is known as Natural Language Processing. It facilitates language-related tasks.
Medical Diagnostication
Thanks to machine learning, some chatbots can identify symptoms. You have algorithms to build models predicting 3D molecule drugs that discover life-saving medicine. Also, identifying patterns can help formulate diagnoses or create treatment plans for patients. It has a strong link with oncology and predicting if cells are cancerous or calculating the potential to be cancerous.
Self Driving Cars
Many can now experience the benefits of autonomous driving thanks to machine learning. Combining machine learning, sensors, and dynamic software has brought this experience to life and the market. This is thanks to the predictive analytical capability of machine learning.
In addition, as a promising field to lean into, it has the potential to change every industry from identifying fraudulent transactions to creating self-learning robotic process automation.
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Automated Arbitrage
Machine learning finds a new niche in finance. Arbitrage is buying or selling assets to generate profits from the price difference. The automation element comes into play when machine learning is used to analyze the data generated by trade. This helps in creating a trading algorithm.
Moreover, this trading algorithm can identify the patterns created in the market to identify profitable trades. You can use this algorithm to make real-time trading decisions leveraging these advantageous arbitrage opportunities.
Machine learning is changing the face of industries in many subtle but impactful ways. We’re only beginning to scratch the surface of what is possible with the use of machine learning. It has the potential to transform everything from healthcare to the economy. In addition, machine learning is going to have a hand in shaping the possible technology of the future.
by Anika D | Feb 21, 2021 | 5G, General, Industrial IoT, Machine Learning
The industrial internet of things (IIoT) has become very popular in industries across the globe. With robotic automation systems in the automotive sector, IIoT offers a great way to vehicles’ connectivity and new capabilities.
According to GlobeNewswire, the automotive IoT market will reach $541.73 billion by 2025.
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Also, the Iot connected devices reach 75.4 billion in 2025. With the advancement in 5G, the IIoT has transformative potential in the automotive industry.
What exactly is the IIoT?
The Industrial Internet of Things (IIoT) is the next level of innovation to connect the world and to optimize the machines. It also refers to the connection of billions of devices like sensors, gateways, electronics, actuators, and much more.
Further, these can interact and make a connection with one another over a wireless network. Further, the connected machines can also share data with each other with no intervention of humans.
The use of sensors, advanced analytics, and artificial intelligence will transform the connection between field assets and enterprises in a big way.
Benefits of IIoT in the Automotive Industry
The potential of IIoT for the automobile industry is really immense. Here are the benefits of IIoT for the automotive industry.
One of the latest trends shaping the future automotive industry is connectivity in vehicles. These cars or trucks can communicate with other devices on the road or in the vehicle through the internet.
Further, a connected vehicle can accomplish a myriad of tasks and makes driving easy for a novice too. Also, it warns you about the traffic on the road and helps find a safe route as well.
Some automotive industries are working on providing amazing apps for automatic car parking in a tight spot or a narrow garage.
Autonomous vehicles are the future of the automotive industry. With the arrival of 5G and AI advance technology, companies are working on the creation of autonomous cars and trucks.
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It is assumed that autonomous cars will reach 60 billion US dollars by 2030 and will become a large market worldwide.
Day by day pollution is increasing which causes big harm to the environment. Therefore, fossil fuels are not good for the environment. Also, everyday increasing prices of it can put immense pressure on your pocket.
This is why the demand for electric vehicles is increasing. IIoT can transform electric cars in a big way and make them future-proof vehicles.
AI Complements the IIoT, along With 5G
Another advantage of the high-tech IIoT automotive robots is that many of them offer predictive maintenance. You will get advance alerts about any problem and can get them to fix before major shutdowns happen.
Check out some interesting facts about 5G!
Artificial intelligence (AI) is often a primary part of detecting potential problems in your smart vehicles. 5G will make AI even more capable and smarter than ever. It is due to the low latency and high speed.
However, AI and 5G alone are not enough. Automotive companies need to merge AI and 5G with IIoT technologies to use the information they gather in the best possible way.
Using IIoT data, AI allows the machine to learn as well as adapt, enabling continuous system development. Therefore, when merging AI technology with IIoT, auto-driving vehicles become a reality on roads.
Conclusion
The Industrial Internet of Things (IIoT), along with other disruptive technologies like 5G, is revolutionizing the complete automotive industry.
by Anika D | Jan 28, 2021 | Artificial Intelligence, Business, General, Machine Learning
The invention of AI has proved to be a bliss for business operating in almost all industries. Statistics demonstrate that an increasing number of organizations are employing this advanced technology within their business operations. So, the applications of artificial intelligence is increasing rapidly.
The market size of AI was valued at approx. $27 billion in 2019, and it is projected to reach about $267 billion by 2027. Businesses worldwide are leveraging the power of AI to optimize their processes and earn higher profits.
Applications of Artificial Intelligence
Though AI is applicable to all industries, some major ones are explained below:
#Human Resource (HR) Management
An Oracle/Future Workplace report showed that 64% of participating HR practitioners would trust a robot over the advice of their manager.
The combination of AI and machine learning (ML) is slowly reinventing the HR world. The sector can benefit from these technologies for two main reasons:
- There are surprising amounts of top quality data in the field.
- HR departments in companies feel essential as well as the pressure of time.
AI can automate some aspects of the recruiting and HR job, freeing professionals to focus on other things like onboarding, training, etc. This can also streamline and improve processes for betterment.
#Intelligent Cybersecurity
Cyber attacks on businesses are massively growing and evolving. Depending on the size of your company, there are countless signals that must be analyzed to evaluate the risk.
Unfortunately, this is not a human-scale problem anymore. In response, AI-powdered tools have emerged to help intelligence and cybersecurity teams to work efficiently and effectively.
Using AI, companies can easily detect vulnerabilities in business applications like Financial or ERP systems. For instance, some of the world’s most protected airports use this technology to analyze your identity when you are on your way to it. This saves security personnel time while making them proficient.
#Healthcare
A research report by Healthcare IT News showed that 63% of research subjects agreed that AI and ML would provide excellent value in all specialty care departments.
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In the coming few decades, AI is going to reinvent the healthcare industry. The technology helps the industry leverage the data at their disposal and improve patient outcomes to a greater extent.
Due to its growing use, the global market size for AI in healthcare is projected to reach about $28 million.
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AI enhances predictability, reliability, and consistency when it comes to service quality and patient safety. It’s true that technology cannot replace doctors, but it makes them more efficient and effective by taking their cognitive burden.
# eCommerce
AI provides a competitive edge to eCommerce businesses, especially because it fits these companies’ size and budget.
The technology uses ML to automatically tag, organize and visually search content by labeling features of videos and images.
At the same time, AI helps buyers quickly discover products based on their preferences for colors, sizes, shapes, and even brand.
Its visual capabilities ease shopping online, thus improving customer experience with eCommerce stores that leverage this evolving technology.
Final Words
Today, every business uses this technology in one or another way. Besides above applications of artificial intelligence, its uses cover businesses operating in industries like retail, hospitality, gaming and betting, manufacturing, to name a few.
