Every business now depends on IT. Efficient IT Operations is mandatory for all businesses, especially those operating in a hybrid mode – a mix of existing data centers and multi-cloud locations.

As with any business process, IT operations can be augmented with machine learning-based solutions. IT is particularly fertile ground for AI as it is mostly digital, has seemingly endless processes requiring automation and there are gigantic amounts of data to process.

IT Operations are expensive!

According to Research and Markets data, global IT Ops & service management (ITSM) is predicted to reach $35.98 billion by 2025 with an annual growth rate of 7.5% YoY.

As the importance of IT operations has ramped up, so has the pressure on ITOps teams. A range of issues puts pressure on teams: shrinking budget for IT operations, multi cloud-based applications, dynamic scaling of infrastructure, limited availability of experienced ITOps personnel, the constant threat from outsiders given the nature of cloud applications, the extension of applications to edge locations with IoT and mobile devices.

AIOps is here to support the maintenance teams and provide AIOps tools to solve problems once thought unsolvable.

What is AIOps

AIOps supports infrastructure management and operations with AI-based solutions. It is employed mainly to automate tasks, improve process efficiency, quicken reactions — sometimes even to a real-time response rate — and deliver accurate predictions on upcoming events.

The big data revolution and machine learning technology have driven change, making it possible to process the vast amounts of information IT infrastructure generates. AI can solve the following challenges:

• Anomaly detection – despite fluctuations and the dynamic nature of data, the internal infrastructure ecosystem is a stable environment. Thus, any anomaly can signal the existence of a problem. Also, early detection of an anomaly is usually a sign of a problem that has yet to be fully understood.
• Event consolidation – An AI model can simplify huge amounts of data, dividing it into multiple layers and finding insights.
• Service tickets analytics – when fed data on tickets submitted to a service desk, an ML-based model can predict seasonal spikes and requests. This can help the service desk owner deploy help desk personnel s needed.
• Detecting seasonality and trends – when using an AI-powered solution, any trend can be divided into 3 components – seasonality, trend and residual. That increases the predictability of long-term commitments and makes managing them more effective.
• Frequent pattern mining – machine-powered analysis delivers insights that are beyond the reach of humans. Machines not only process more data but also , unlike humans, make unbiased decisions. They also find correlations that are impossible for humans to detect.
• Time series forecasting – AI-based models can forecast future values such as memory load, network throughput ticket count or other values in the future. This enables AIOps solutions to deliver early alert predictions.
• Noise reduction – AIOps solutions eliminate noise and concentrate on the real underlying problems.

AI helps ITOps run smoother

There are currently several major challenges for IT departments.

Fraud Detection/Security

According to IBM data quoted by CSO, the average time to identify a breach in 2019 was 209 days. Such a sizable delay is caused mainly by security teams being overwhelmed with work and the stealth operations of criminals. Cybercrime is a highly profitable venture, with profits reaching up to $1.5 trillion a year. Cybercriminals don’t play favorites, targeting victims of all stripes, from individuals to international corporations. In May 2019 authorities tracked down a group that had availed itself of an estimated $100 million.

Anomaly detecting AI and machine learning-based AIOps solutions can spot even the slightest signs that unexpected events are occurring in a system. AIOps can be trained to learn what “typical operations” look like and spot anything out of place. It can also send real-time notifications to the team.

Eliminate Downtime

According to an ITIC study, for 86% of companies surveyed, a single hour of downtime costs $300,000. For 34%, the cost comes in at a staggering $1 million.

AIOps comes with various tools to help keep the lights on and operations running smoothly. In addition to anomaly detection, time series prediction serves as a benchmark and a tool for designing maintenance flow. It also supports efficient resource management. Pattern mining spots inefficient components and bottlenecks to be optimized. It also enables the mapping of both seasonality and trends, so resources supporting operations can be assigned efficiently.

Capacity planning

Before the cloud, companies were forced to overpay for servers and computing power because they had to stay on top of fluctuations in their seasonal needs for computer power. Today, despite the access they have to endless power and storage in the cloud, IT teams around the world continue to struggle with capacity planning and delivering scalable infrastructure to meet the irregular demands for infrastructure.

Nearly all AIOps functionalities support the goal of delivering a stable and scalable environment. With capacity planning supported by time series forecasting and ticket analysis, IT teams can manage their infrastructure scaling and maintenance not only to avoid downtime but also to minimize costs and utilize their systems as efficiently as possible.

A great example comes from Google, whose AI-based system delivered new operational efficiency recommendations for data center cooling systems, effectively cutting costs by 40%.

Noise reduction and pattern mining deliver clear insights.  On the other hand, scraping through the data in real-time enables an AIOps platform to deliver insights faster and using those insights more actionably.

Summary

AIOps machine learning-powered solutions can significantly improve today’s data-heavy IT infrastructure management.

A good way to learn more about the AIOps strategy landscape is to meet our team during the upcoming AIOps conference in Fort Lauderdale, Florida. Our specialists will be more than happy to strategize, assist, answer your questions, and share their expertise from the field. If you’d like to meet us there, just drop us a line or stop by our booth (#1104). Contact us at AIOps@deepsense.AI to see how we can help!

With convolutional neural networks and state-of-the-art image recognition techniques it is possible to make old movie classics shine again. Neural networks polish the image, reduce the noise and apply colors to the aged images. 

The first movies were created in the late nineteenth century with celluloid photographic film used in conjunction with motion picture cameras.

Skip ahead to 2018, when the global movie industry was worth $41.7 billion globally. Serving entertainment, cultural and social purposes, films are a hugely important heritage to protect. And that’s not always easy. Especially considering the fact that modern movies are produced and screened digitally, with the technology of celluloid tape fading into obsolescence.

Challenges in film preservation

The challenge and importance of preserving the cultural heritage of old movies has been underscored by numerous organizations including the European Commision, which noted that a lack of proper devices to play aging technology on could make it impossible to watch old films.

In deepsense.ai’s experience with restoring film, the first challenge is to remove distortions. Classics are usually recorded in low resolution while the original tapes are obviously aged and filled with noise and cracks. Also, the transition process from celluloid tape to digital format usually damages the material and results in the loss of quality.

By using AI-driven solutions, specifically supervised learning techniques, deepsense.ai’s team removed the cracks and black spots from the digitized version of a film. The model we produced uses deep neural networks trained on a movie with cracks and flaws added manually for training purposes. Having some films in original and broken form, the system learned to remove the flaws. An example of generated noise put on the classic Polish movie “Rejs” and the neural network’s output is displayed below.

The example clearly shows that our neural network can process and restore even a thoroughly damaged source material and make it shine again. The networks start to produce low-quality predictions when the images are so darkened and blurred that the human eye can barely recognize people in the film.

How to convert really old movies into HD

A similar training technique was applied to deliver a neural network used to improve the quality of an old movie. The goal was to deliver missing details and “pump up” the resolution from antiquated to HD quality.

The key challenge lay in reproducing the details, which was nearly impossible. Due to technological development, it is difficult for people to watch lower quality video than what they are used to.

The model was trained by downscaling an HD movie and then conducting a supervised training to deliver the missing details.

Move your mouse cursor over the image to see the difference.

The model performs well thanks to the wide availability of training data. The team could downscale the resolution of any movie, provide the model with the original version and let the neural network learn how to forge and inject the missing detail into the film.

A key misconception about delivering HD versions of old movies is that the neural network will discover the missing details from the original. In fact, there is no way to reclaim lost details because there were none on the originally registered material. The neural network produces them on the go on with the same techniques Thispersondoesnotexist and similar Generative Adversarial Networks use.

So, the source material is enriched with details that only resemble reality, but are in fact not real ones. This can be a challenge (or a problem) if the material is to be used for forensic purposes or detailed research. But when it comes to delivering the movies for entertainment or cultural ends, the technique is more than enough.

Coloring old movies

Another challenge comes with producing color versions of movie classics, technically reviving them for newer audiences. The process was long handled by artists applying color to every frame. The first film colored this way was the British silent movie “The Miracle” (1912).

Because there are countless color movies to draw on, providing a rich training set, a deep neural network can vastly reduce the time required to revive black and white classics. Yet the process is not fully automatic. In fact, putting color on the black and white movie is a titanic undertaking. Consider Disney’s “Tron,” which was shot in black and white and then colored by 200 inkers and painters from Taiwan-based Cuckoo’s Nest Studio.

When choosing colors, a neural network tends to play it safe. An example of how this can be problematic would be when the network misinterprets water as a field of grass. It would do that because it is likely more common for fields than for lakes to appear as a backdrop in a film. 

By manually applying colored pixels to single frames, an artist can suggest what colors the AI model should choose.

There is no way to determine the real color of a scarf or a shirt an actor or actress was wearing when a film rendered in black and white was shot. After all these years, does it even matter? In any case, neural networks employ the LAB color standard, leveraging lightness (L) to predict the two remaining channels (A and B respectively).

Transcription and face recognition

Last but not least, transcribing dialogue makes analysis and research much easier – be it for linguistic or cultural studies purposes. With facial recognition software, the solution can attribute all of the lines delivered to the proper characters.

The speech-to-text function processes the sound and transcribes the dialogue while the other network checks which of the people in the video moves his or her lips. When combined with image recognition, the model can both synchronize the subtitles and provide the name of a character or actor speaking.

While the content being produced needs to be supervised, it still vastly reduces the time required for transcription. In the traditional way, the transcription only takes at least the time of a recording and then needs to be validated. The machine transcribes an hour-long movie in a few seconds.

Summary

Using machine learning-based techniques to restore movies takes less time and effort than other methods. It also makes efforts to preserve the cultural heritage more successful and ensures films remain relevant. Machine learning in business gets huge recognition but ML-based techniques remain a novel way to serve the needs of culture and art.. deepsense.ai’s work has proven that AI in the art can serve multiple purposes, including promotion and education. Maybe using it in art and culture will be one of 2020’s AI trends.

Reviving and digitalizing classics improves the access to and availability of cultural goods and ensures that those works remain available, so future generations will, thanks to AI, enjoy Academy-awarded movies of the past as much as, if not more than, we do now.