Welcome to my blog on Machine Learning Pipelines! In today's world, data is generated at an unprecedented rate, and the amount of data being produced is expected to continue to grow exponentially. As a result, the field of data science has become increasingly important in recent years, and Machine Learning (ML) has emerged as a key technique for analyzing and extracting value from data. ML is a process of training algorithms to learn patterns and insights from data. However, building an ML model is only a small part of the entire data science process. The real challenge lies in creating a robust ML pipeline that can handle data ingestion, preprocessing, model training, and deployment.

Predictive Analytics is a division of advanced analytics which is used to forecast uncertain future events. Predictive analytics make use of an array of technologies from different domains such as data mining, statistics, modeling, machine learning, and artificial intelligence to analyze historic data to forecast the future. It uses a stack of data mining, predictive modeling and analytical methodologies to bring together the management, information technology, and modeling business process to forecast the future. The historical and transactional patterns of data can be used for risk identification and future opportunities. Predictive analytics models detect correlation among many factors to assess risk with a particular set of conditions to give a score or weightage.

Video has become a perfect communication, remote consulting, collaboration, entertainment, and even monitoring and control tool for companies from different business sectors as well as public institutions. Companies have even started to use video as a data collection tool within robotic process automation (RPA), which has made this type of content a valuable asset. Let me emphasize this idea: Every business, whether it's a factory, a marketing agency or a restaurant, generates an enormous amount of video content today. Using video analytics, businesses can extract the most valuable data out of enormous amounts of video and preserve this information instead of terabytes of video. It is possible to track any spatial/temporal parameters and get instant notifications about discrepancies and violations.

Enterprises are increasingly pushed for faster insights from their ever-increasing data volumes. A TDWI senior analyst looks at how some vendors are responding. Accelerating speed to insight from data is critical to nearly all types of organizations, especially as managers seek to develop strategies for responding to unexpected and rapidly changing circumstances such as the global coronavirus outbreak. TDWI's recently published Best Practices Report, Faster Insights from Faster Data, takes an in-depth look at practice and technology issues that matter most in reducing delays in data life cycles and putting well-prepared and relevant data in the hands of users sooner. Not long after the publication of the report, I had the opportunity to visit with some technology providers exhibiting at the TDWI Las Vegas Conference and Strategy Summit in February. It was interesting to see how issues brought up in the report are being addressed by vendors.

These three trends can help your enterprise transform data into information on demand that empowers every person, process, and system to be more agile and intelligent. As we bid farewell to 2019, many organizations now have hundreds of SaaS apps that increase the burden of data integration, especially when looking for a single view of your customer or identifying how well a product is delivered across your business. Although the application fabric has changed over the years, businesses are going through their own revolution as they struggle to manage the exponential increase in demand for data and insights from these apps across the enterprise. In 2020, companies that can prepare datasets quickly and accurately with the help of built-in intelligence and smart algorithms will come out on top. By enabling IT professionals to maintain the scale of data volumes and variety across both enterprise and cloud data sources, they can focus on supporting data democratization scenarios for immediate and repeatable self-service data needs.

Learn how blockchain, control towers, and recognizing the strategic value of your data can enhance your supply chain. There are interesting shifts and trends in the market as organizations grapple with the abundance of data at their disposal. This year we saw the growth and maturation of various technologies designed to help businesses harvest useful information from all relevant sources. Enterprises had to make sure that data was available to other processes so it could derive business insights. As we prepare for the coming year, we anticipate this trend to continue particularly in the following areas.

These four recommendations can help prepare you -- or the novice analyst on your team -- for a career in this burgeoning field. When Aurora Peddycord-Liu started as an analytical education intern at SAS in the summer of 2017, she came with a solid educational background from Worcester Polytechnic Institute and NC State's computer science Ph.D. program. These programs prepared her well for her current position at SAS, where she uses data to derive actionable insights on the design and use of SAS e-learning courses, but she's had to adapt her skill set to face the challenges of a real-world analytics position. To learn how newbie analysts can prepare for their work in this hot new age of machine learning, I spoke with Peddycord-Liu and senior executive, Dan Olley, global CTO at Elsevier. Recommendation #1: Don't be overwhelmed -- just get started Don't be intimidated by the powerful tools at your disposal; find a point to start and dive in.

AI was big news in 2018, but we overlooked the two distinct aspects of AI -- how it can augment human decision making or automate decision making. Here's why the distinction is important. The past year has seen virtually every BI and analytics vendor claiming enhancement of their tools with various flavors of artificial intelligence (AI). Others are little more than a thin veneer of AI terminology applied to planned or existing features. I have two questions regarding the emergence of AI for BI in 2018: have there been some real breakthroughs and what happens next? I wrote a series of three articles in March 2016 posing the question: "Where is Cognitive Decision Making in BI?" I had already been asking similar questions as early as 2011 but was bold enough to predict that cognitive would replace analytics within another year or two.

Imagine you're on a cross-country road trip and you aren't sure how to get to your final destination. You have a paper map in the passenger seat, but you keep getting lost because you have to continuously pull over to study this very large and confusing map. Eventually, you arrive at your destination, albeit a bit frustrated, but getting there wasn't enjoyable, and it certainly wasn't efficient. Now imagine you're on the same road trip, but instead you're driving a car with a built-in GPS navigation system guiding you the entire time. The journey to your final destination will take considerably less time and make for a much more pleasant experience.

The noise around artificial intelligence is deafening. Everyday new stories of leading edge companies showcasing amazing breakthroughs leveraging AI fill our new feeds. But how can you harness the power of AI? How can AI drive competitive advantage within your organization? Cut through the hype and learn pragmatic applications of AI across major industries by spending a day focusing on the realities and practice of AI in the enterprise. During this daylong series, you'll learn frameworks for implementation, be able to ask questions of AI experts, and hear valuable insights on where the market is headed.