Once considered to be science fiction, machine learning is now one of the most hyped-up and revolutionary technologies of the current generation.

Machine learning and Artificial intelligence have seen immense advancement in recent years thanks to the progress in computational and processing speeds of computers, the use of GPUs or graphical processing units, and also the vast availability of data.

The applications of machine learning in our day-to-day lives are only limited by human imagination. What once started with computers occupying entire rooms to the current age of handheld smart devices, machine learning now performs tasks like object detection, face recognition, product recommendation, and a lot more, all of which were once considered to be fictitious and impossible.

Machine learning has also found applications in the fields of healthcare, academics, retail, electronics, robotics, etc.

The innovations in recent years in the field of machine learning have made most of our tasks efficient and precise than ever before.

When properly trained machine learning models can complete tasks and perform better than humans with more ease and efficiency.

Understanding the recent trends and innovations in Machine Learning is important for businesses to stay competitive in the industry.

There is also an increase in Machine learning and AI-related jobs. With the increasing need to learn this growing technology that is changing our world as we see it, let us dive in and start exploring some of its trends that will impact our lives beyond our imagination.

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  1. Automated machine learning (AutoML)

AutoML or Automated Machine Learning aims to provide methods and processes to make Machine learning easily accessible to non-developers and non-machine learning experts.

This would help accelerate the research in ML and improve its efficiency. onecard fawry Since Machine learning is increasingly used in multiple disciplines, AutoML would help develop off-the-shelf solutions which are simple and do not rely on ML experts.

Currently, the industry heavily relies on Machine learning experts to preprocess and clean the data, select features, and appropriate models, optimize the hyperparameters, post-process the models and analyze the results.

These complex tasks cannot be achieved by non-ML experts. AutoML provides us with a solution for this problem by the use of simple templates.

One example of AutoML is Transmogrif, an end-to-end AutoML toolkit. It is used for structured data written in scala and it runs on Apache Spark.

It uses AutoML in five areas of workflow: feature inference, transmogrification( converting features into numeric values), feature validation, model selection, and hyperparameter optimization.

Another notable example is AutoGluon by Amazon. Released in 2020, it is an open-source, easy-to-use toolkit for ML-based application development.

It focuses on real-world applications built using deep learning for spanning image, text, or tabular data. This autoMl toolkit has helped developers, build quick deep learning solutions, improve models and hyperparameter tuning.

Some of the other well-known autoML packages are AutoWEKA, Auto-sklearn, Auto-PyTorch, H2O AutoML, MLBox, TPOT.

  1. Tiny ML

While Machine Learning applications exist on a large scale, integrating them with IoT solutions comes with its limitations. Sending web requests to process data on large servers using ML algorithms is time-consuming.

The solution is to use Machine learning on edge devices.TinyML or Tiny Machine learning is a fast-growing field of Machine Learning which includes developing hardware, software, and algorithms that are capable of performing on-device sensor data analytics using extremely low power(in mW).

This enables using Machine learning in IoT with always-on and battery-operated devices. تطبيقات لربح المال في مصر Since data need not be sent out for processing it reduces latency, power consumption and also ensures user privacy.

TinyML has applications in the field of predictive maintenance for industries, agriculture, healthcare, etc. for example Shoreline IoT makes peel and stick ultra low power sensors on a motor with a lifetime of up to 5 years. It has one milliwatt or lower power usage.

TinyML can be used in industries for audio analytics for child and elderly care, equipment, and safety monitoring.

During the Covid-19 pandemic Edge Impulse and Arduino published a project to detect the presence of specific coughing sounds in real-time. The using a highly optimized tinyML model, a cough detection system which used under 20kB of RAM. Swim.AI uses TinyML on streaming data to improve passenger safety, reduce congestion and emissions through efficient routing.TinyML also has applications in vision, motion, and gesture recognition.

  1. Quantum ML

Quantum ML or Quantum Machine Learning is the combination of Quantum algorithms and machine learning programs. The term is commonly used to refer to ML algorithms that analyze data on quantum computers (also known as quantum-enhanced machine learning). Quantum machine learning uses qubits and quantum operations to improve computational speed and data storage done by  ML algorithms.

With tech giants like IBM, NASA, and Microsoft making quantum computing resources and simulators available on cloud models, using quantum computing and machine learning together creates an unstoppable force. In 2016, IBM launched an online cloud platform for quantum software developers and researchers called the IBM Q Experience, which consists of several quantum processors available to the users using the IBM web API.

Quantum machine learning can also be defined as a field where quantum algorithms perform machine learning tasks.

The combination of quantum computing and ML could be of enormous value to industries in the near future solving the unsolvable problems of today.

Some of the fields that   QuatumML could impact the future are the study of nanoparticles, molecular modeling of new drugs in medicine, enhanced pattern recognition and classification, security of blockchain with IoT.

TensorFlow Quantum (TFQ),  is an open-source library for the rapid prototyping of quantum ML models which provides the tools necessary for bringing the quantum computing and machine learning research communities together to control and model natural or artificial quantum systems.

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  1. Machine Learning Operationalization Management (MLOps)

MLOps or Machine Learning Operations is a sub-field of Artificial intelligence Model delivery. It is the process of developing ML software solutions that focus on efficiency and reliability.

MLOps is a way of improving the pattern of developing ML solutions for businesses that can scale production capacity and deliver quick results.

Microsoft, IBM, H2O, Domino, DataRobot, Amazon, Google, and Grid.ai have all integrated MLOps capabilities into their platforms.

Machine learning

The need for MLOps arises because of the large-scale availability of data that needs automation. MLOps combines practices of Artificial intelligence & Machine Learning with principles of DevOps to form a ML Lifecycle that co-exists alongside the software development lifecycle also known as the SDLC. It results in efficient workflow and effective results. MLOps aims to support the integration, development, and delivery of ML models into production.

MLOps enables automated testing of ML models. افتتاح اليورو 2023 Eg. ML model testing, integration testing, data validation, etc. It also enables the application of agile principles to ML projects.

Some of the examples of Cloud-based MLOperations include Artificial intelligence Platform by Google Cloud, AzureML, and SageMaker by AWS.Google also initiated the Kubeflow project to manage a set of open-source tools for MLOps and assemble them on Kubernetes. Non-cloud-based systems like MLFlow, Sacred, Comet, DVC, Kubeflow, Valohai, and Neptune are also widely used.

  1. Reinforcement Learning

Machine learning is divided into three categories: supervised learning, unsupervised learning, and reinforcement learning. The reinforcement learning model is able to learn from its environment, perceive and interpret it, take relevant actions and learn by trial and error. The environment can be based on a reward /punishment system and the model will strive to achieve the maximum reward.

Currently, reinforcement learning models are not preferred for security-related applications since the model takes random action, which may be unsafe in the process of learning. When Reinforcement learning models and evolved enough to complete real-world tasks without choosing unsafe or harmful actions, it will be a boon to data scientists. Reinforcement learning garnered the attention of the world when DeepMind’s AlphaGo defeated the Go grandmaster Lee Sedol, in 2016.

One example of Reinforcement learning is Chat-bots found on websites. With the advancement in Reinforcement learning, Chatbots can be built to respond uniquely to every customer based on inputs received. Reinforcement learning also finds applications in the field of robotics for strategy planning, robot motion control, industrial automation, aircraft control, inventory management, and so on.

Reinforcement toolkits like OpenAI Gym, DeepMind Lab, and Psychlab help provide an environment for innovation in reinforcement learning.

The self-learning ability in an interactive environment from its own actions and responses makes reinforcement learning a unique technology to watch out for in the future. The future of reinforcement learning is artificial general intelligence (AGI), a single model which can master a wide variety of tasks. Hence each model will be capable of performing multiple complex tasks with ease.

The emerging trends in ML and AI have diverse business applications. However, no technology can survive or dominate the other. Collaborating with other technologies is the solution to take the world towards advanced digitization. Utilization of the technology with unique and innovative ideas and research will soon lead to technological breakthroughs in the future.  Machine learning has applications in every walk of our lives. And the idea that machines could think and perform tasks like humans is not too far from coming true.

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