… migrating essential business systems to the cloud, and augmenting existing business systems with Google Cloud AI and machine learning capabilities (ML).

Evgeniy is a specialist in software development, technological entrepreneurship and emerging technologies. In recent years, companies' growing focus on big data has led to increased digitalization demands. The avalanche of data has forced businesses to reconsider software modernization approaches. With that in mind, let's look at how enterprises use AI in intelligent analysis, hyperautomation and cybersecurity in the world of big data. Data orientation is the future of business, and the survival of companies depends on efficiently processing external and internal information.

Learn how to put your machine learning models into production. Deployment of machine learning models, or simply, putting models into production, means making your models available to your other business systems. By deploying models, other systems can send data to them and get their predictions, which are in turn populated back into the company systems. Through machine learning model deployment, you and your business can begin to take full advantage of the model you built. When we think about data science, we think about how to build machine learning models, we think about which algorithm will be more predictive, how to engineer our features and which variables to use to make the models more accurate.

Learn how to put your machine learning models into production. Deployment of machine learning models, or simply, putting models into production, means making your models available to your other business systems. By deploying models, other systems can send data to them and get their predictions, which are in turn populated back into the company systems. Through machine learning model deployment, you and your business can begin to take full advantage of the model you built. When we think about data science, we think about how to build machine learning models, we think about which algorithm will be more predictive, how to engineer our features and which variables to use to make the models more accurate.

Learn how to put your machine learning models into production. Deployment of machine learning models, or simply, putting models into production, means making your models available to your other business systems. By deploying models, other systems can send data to them and get their predictions, which are in turn populated back into the company systems. Through machine learning model deployment, you and your business can begin to take full advantage of the model you built. When we think about data science, we think about how to build machine learning models, we think about which algorithm will be more predictive, how to engineer our features and which variables to use to make the models more accurate.

"I would recommend anyone to sign up to BotXO" – Nadin Kempel Sigh, Service Operation Specialist, Tiger of Sweden. Never leave customers hanging again when they have questions or need help finding the right product. " I would recommend anyone to sign up to BotXO." Businesses and consumers are using Messenger to connect to each other in a more personal and more immediate way. Chatbots will fundamentally revolutionize how computing is experienced by everybody.

In this special guest feature, Dirk Kanngiesser, co-founder and CEO of Cryptowerk, discusses how vendors implement Verifiable AI into their products to ensure that their AI algorithms are not handling data that has been tampered with, and how companies build Verifiable AI into their systems to verify that they are using safe data. Cryptowerk is a provider of data integrity solutions using blockchain technology. With more than 25 years of technology leadership experience, Dirk has led new product and business launches at multiple companies in both the U.S. and Europe. Businesses are increasingly using AI to automate processes to gain efficiencies, be more competitive and avoid disruption in their markets. According to a recent Accenture survey, 82% of executives say that their organizations are using data and algorithms based on that data to drive critical and automated decision-making at unprecedented scale.

But using technology to optimise previously complex, time consuming processes is not a new concept. However, employing artificial intelligence (AI) for the analysis and management of traditionally unstructured information, has the potential to not just unlock more value for users but also provide insights that have not been possible before. That is why artificial intelligence for the lawyer or for legal workers is beginning to gain traction. Indeed, the future of legal profession may well be AI and the impact of technology on legal profession will be significant indeed. Artificial intelligence for the lawyer will help change contract management, with the potential to deliver such a significant transformation to this area because contracts are the foundation of a company's commercial relationships.

December means holiday parties, New Year's resolutions, and a blizzard of technology industry predictions. You'll see a ton of AI-related calls as we approach 2019. The artificial intelligence hype machine is already roaring. The potential impacts of AI are wide-ranging – as are the related forecasts, on everything from how AI will change college admissions to the role it will play in international relations and politics. We decided to focus on the trends that matter most urgently to IT leaders – you don't need another "AI is taking over the world" story; you need concrete insights for your team and business.

While the media is full of stories of retail woe, actual year-on-year retail sales are up in the UK. What does Artificial Intelligence (AI) add to this picture? It could help UK retailers reduce stock inventory by £23 billion. Or grow sales 30% faster. Or increase gross profit margins by 50%.