Not many people miss having to manually sort files, label papers, or search for lost forms in huge filing cabinets. That's because all these tasks have become way easier, faster, and more enjoyable since they've become digitized – computers and the internet have revolutionized the way businesses approach organization and task management. Similar to how computers and the internet made monotonous tasks faster and easier in every department, AI will transform work in every industry in the 21st century. Machine learning will automate away the most time-consuming and repetitive tasks across a company, along with offering predictions that will allow businesses to make better decisions ahead of time. Introducing these revolutionary processes takes time and specialized knowledge.

Work as a data scientist follows a cycle: log in, clean data, define features, test and build a model, and make sure the model is running smoothly. Sounds straightforward enough, except not all parts of the cycle are created equal: data preparation takes 80% of any given data scientist's time. No matter what project you're working on, most days you're cleaning data and converting raw data into features that machine learning models can understand. The monotonous hole of data prep blends hours together and makes each day of work feel identical to the one before it. Why can't you do this tedious process more effectively?

A Feature Store is a collection of organized and curated features used for training and serving Machine Learning models. Keeping them up to date, serving feature vectors, and creating training data sets requires a combination of transactional (OLTP) and analytical (OLAP) database processing. This kind of mixed workload database is called HTAP for hybrid transactional analytical processing. The most useful Feature Stores incorporate data pipelines that continuously keep their features up to date through either batch or real-time processing that matches the cadence of the source data. Since these features are always up to date, they provide an ideal source of feature vectors used for inferencing.

Splice Machine develops a machine learning-enabled SQL database that is based on a closely engineered collection of distributed components, including HBase, Spark, and Zookeeper, not to mention H2O, TensorFlow, and Jupyter. Customers use it to build complex AI apps that include transactional, analytical, and ML components. The company just announced a Kubernetes operator for customers running in private cloud environments. Zweben said during a demo of Splice Machine's Kubernetes Ops Center. "When you pause on Splice Machine, it drains Kubernetes nodes and makes them available for other applications to use." Support for Kubernetes is not new at Splice Machine.

Sign in to report inappropriate content. Take a look at Splice Machine, an operational AI data platform for digital transformation. Unlike other Big Data platforms that provide offline, batch analysis, Splice Machine powers intelligent applications that are woven into the operational workflows of companies. We are a scale-out SQL RDBMS, data warehouse and machine learning platform in one.

Accenture has purchased a minority stake in artificial intelligence-based data analytics platform vendor Splice Machine as part of a broader strategy aimed at bolstering its new data analytics services. Splice Machine is open source and is built upon the popular Apache Hadoop, HBase, and Spark distributed platforms. Moreover, its solutions can be deployed on-premise or as a fully managed cloud service. Accenture already has a relationship with the San Francisco-based company. Last year, Accenture integrated Splice Machine's intelligent application platform into its analytics-as-a-service solution.

Before you open up the presents under the tree, I've got some geekier gifts. In response to execs and luminaries from across the world of data and analytics sharing their predictions for the next year, I've dutifully compiled and stitched them together. Gather round, and soak up this year's batch, which focus on artificial intelligence, data regulation, data governance, the state of the Hadoop market, open source and "the edge." Predictions about artificial intelligence (AI) are all over the map. They range from optimistic and starry-eyed to a bit more skeptical and jaded.

Growth of the artificial intelligence market for healthcare is expected to reach $6.6 billion by 2021, according to Accenture, and the combined deployment of key clinical health AI applications could potentially create $150 billion in annual savings for the U.S. healthcare economy by 2026. In line with that growing trend, Rockville Centre, New York-based Precision Innovation Network, a physician-centric group purchasing organization focused on strengthening the patient-physician relationship by making it easier to practice, has tapped Splice Machine's big data and artificial intelligence data platform to develop its new Treatment Advisor application. The Treatment Advisor app will leverage multi-dimensional data – such as medical records, quantified measured performance obtained from digital devices, patient perspectives from questionnaires and demographic information – and use machine learning to help clinicians learn the trajectory of a disease and gather predictions for what may be the best treatment for each individual. It also will enable physicians to analyze the data to target disease-modifying therapies and better understand how a patient might feel – a patient-reported outcome – in the future. Splice Machine will allow Precision Innovation Network to apply machine learning to the data and gain insights that will help provide more precise medical treatment to patients, officials said.

The push for operational analytics that seeks to eliminate the requirement for constantly moving data between storage and databases to support transaction and analytical workloads is fueling the growth of translytical data platforms. The framework uses a single data tier that can serve both transactional and analytical workloads. The requirement to reduce data movement between data silos and technology stacks along with rise of machine learning and streaming analytics has fueled the rise of a maturing translytical data platform sector. Market watcher Forrester released a market survey earlier this month that identifies and ranks a dozen key players in the nascent analytics sector. The market researcher also identified the top translytical database workloads, including: real-time applications; Internet of Things (IoT) analytics operational data; connected data apps; and continuous learning in which translytical databases are used to train and retrain machine learning models. "The translytical data platform market is growing because more enterprise architecture pros see translytical as critical for their enterprise data strategy," Forrester said.

Machine learning isn't a new concept, and you don't need to tell that to Monte Zweben. He's been involved in artificial intelligence research for 30 years and calls himself an "old school AI person." However, recent developments and a flood of new companies offering machine learning-powered applications have made the technology more accessible than ever. Zweben has previously worked as co-manager of NASA's principal artificial intelligence laboratory and is now CEO of Splice Machine, a SQL-on-Hadoop database company in San Francisco, working on a machine learning platform. As DevOps is slowly taking over the IT landscape, its vital that IT pros understand it before jumping right into the movement.