Today organizations are carrying out more and more data projects that promise great opportunities to drive agility and competence. But they are facing a growing pressure to extract meaningful insights from data. Most of them realize the potential of data science to deliver business value, even some are already investing heavily in data science programs. There is no wonder that the landscape of data is growing rapidly and processing and analyzing that data requires a vital approach. This is where data scientists step in performing data visualization, data mining, and information management.

As most companies view a significant return from data science investments, most data science implementations are high-cost IT projects. Meanwhile, they often not generate value for businesses. Therefore, experts are now talking about DataOps, a new and independent approach to delivering data science value at scale. DataOps arises from the need to productionalize a rapidly increasing number of analytics projects and then to manage their lifecycles.

With the introduction of DataOps, data scientists and data engineers can work together and can bring a level of collaboration and communication to generate actionable insight for a business.

Significantly, DataOps is driven by data lifecycles and insights. It basically applies the DevOps process to data pipelines, using automation and Agile methodology to cut the time spent fixing issues in pipelines as well as get data science models into production quicker. Despite this, both are carrying distinct features and capabilities. While DevOps is the collaborative process between two technical teams, DataOps simplifies collaboration between data analysts, engineers, and data scientists, among others within an organization who use data. This essentially makes DataOps a much more multifaceted process than DevOps.

DataOps for Data Science Success in an Enterprise

Translating structured or unstructured data into business and operational insights, and subsequently incorporating them into a data monetization value chain is a very complex task. Even data analysis by companies doesn’t produce much value for them. According to Gartner, 80 percent of analytics is likely to not deliver business outcomes through 2020, and only 20 percent of data insights will deliver business outcomes through 2022.

In this regard, DataOps emerges as an agile way of developing, deploying and operating data-intensive applications, helping in fostering a data factory mindset. This is also orchestrating, monitoring and managing the data pipeline in an automated way for everyone handling data.

For a majority of organizations, DataOps currently is slowly becoming a crucial practice to endure in an evolving digital world, where coping with real-time business intelligence is necessary to gain a competitive edge over peers. Instability of data, rapidly evolving technology landscape, and increasing demand of the Agile business ecosystem are few reasons surging the need of DataOps.

IBM DataOps, for instance, enables agile data collaboration to accelerate speed and scale of operations and analytics throughout the data lifecycle. This also assists in creating a business-ready analytics foundation by offering market-leading technology that works together with AI-powered automation, infused governance, data protection, and a robust knowledge catalog to operationalize relentless, high-quality data across the business.

Comprehensively, applying DataOps practices in all data activities, from data management and integration to data engineering and data security, enterprises can simplify the process of Data Science across an organizational level.

The post WHY DOES DATAOPS FOR DATA SCIENCE PROJECTS MATTER? appeared first on Artificial Intelligence.

Microsoft announced July 8 a new artificial intelligence tool for its cloud platform Azure that allows developers to analyze unstructured medical data, including clinical notes, clinical trial protocols and medical publications. 

Microsoft’s Text Analytics for health allows researchers, data analysts and medical professionals to detect words and phrases from unstructured text and connect them to relevant healthcare and biomedical concepts, such as diagnoses, medication names and treatments. Users can extract more than 100 types of personally identifiable information, including protected health information, in unstructured text. 

The AI tool also links entities to medical ontologies and coding systems, such as the Unified Medical Language System, to detect connections between medical concepts mentioned in text. For example, the tool can be used to find the relationship between a medication name and the dosage associated with it.  

Medical researchers and data analysts can use the tool to create analytics on historical medical data, develop prediction models and match patients to clinical trials. Seattle-based Allen Institute for AI used it to develop a COVID-19 search engine allowing researchers to more quickly analyze coronavirus information. The institute, founded by Microsoft co-founder Paul Allen in 2014, partnered with the University College London to review medical research reports. 

“We have been partnering with engineers at Microsoft and data scientists to build a ‘living’ reviews system – that automatically identifies relevant research for reviews as they are published,” James Thomas, a professor at University College London, stated in a news release. “Text Analytics for health provides a powerful tool for extracting insights from clinical literature, with rich support for a wide range of healthcare terminology so that we can more quickly and accurately identify relevant information.” 

The post Microsoft Azure launches medical data-mining tool appeared first on Artificial Intelligence.

The uncertainties induced by COVID-19 have created great havoc across different industries. Across the technology sector, the positive impacts are slightly more than negative ones. The pandemic has provided a stage for new-age technologies like data-driven technologies and Artificial Intelligence to prove their value. Owing to the same reason and rising adoption of AI across various industries, we are most likely to see an upsurge in the demands of AI skills.

According to World Economic Forum, “While it was a microscopic invader rather than the rise of the robots that led to the current collapse of the labor market, it has become clear that the fallout of the pandemic will accelerate digitization and automation across a range of industries and sectors. This calls for new investments and mechanisms for upskilling and reskilling, for both deeply human skills as well as digital skills.”

In fact, the COVID crisis also exposed some yawning gaps in AI capabilities. The sudden shift in consumer and business demand brought on by the crisis threw many AI systems out of whack, MIT Technology Review reports. Some observers feel “automation is in a tailspin,” while “others say they are keeping a cautious eye on automated systems that are just about holding up, stepping in with a manual correction when needed.”

Earlier this year, WEF reported that AI and related digital technologies are poised to generate large numbers of jobs and related opportunities. The WEF authors say both “digital” and “human” skills will be critical to organizations in the months and years to come. The WEF projected demand will increase this year by 16% in data and AI, along with 12% for engineering and cloud computing skills.

The demand for AI skills varies by specialty. “While the role AI intelligence specialist is the fastest-growing new economy role, the absolute number of opportunities for this profession is relatively low,” the WEF analysts point out. “On the other hand, data scientist positions have slower annual growth rates but form the third-largest opportunity among the set of growing professions.”

The top AI-related skills noted in the WEF report will be artificial intelligence specialists, data scientists, data engineers, big data developers, and data analysts.

“AI is a rocket ship that is taking off,” relates Satya Mallick, founder of Big Vision LLC, in a recent career overview at the BuiltIn career site. “Even entry-level jobs are insanely lucrative, paying two times or more compared to regular programming jobs. The reason is a huge demand for AI talent and not enough people with the right expertise.”

While Malick cautions that soaring salary levels may not be sustainable, he adds that “people who get on this rocket ship in the next five years or so will have amazing careers financially as well as in terms of the quality of work.” Understanding the human side of AI is just as important as the technology side. “Understanding that the tech is the easiest part of AI,” says Jana Eggers, CEO of Nara Logics. “The data and the results are both more critical. And those are both driven by the organization.”

The post DATA AND AI SKILLS TO WITNESS RISE IN DEMAND AFTER COVID-19 appeared first on Artificial Intelligence.

Don’t look now, but object stores – those vast repositories of data sitting behind an S3 API – are beginning to resemble databases. They’re obviously still separate categories today, but as the next-generation data architecture takes shape to solve emerging real-time data processing and machine learning challenges, the lines separating things like object stores, databases, and streaming data frameworks will begin to blur.

Object stores have become the primary repository for the vast amounts of less-structured data that’s generated today. Organizations clearly are using object-based data lakes in the cloud and on premise to store unstructured data, like images and video. But they’re also using them to store many of the other types of data, like sensor and log data from mobile and IoT devices, that the world is generating.

The object store is becoming a general purpose data repository, and along the way it’s getting closer to the most popular data workloads, including SQL-based analytics and machine learning. The folks at object storage software vendor Cloudian are moving their wares in that direction too, according to Cloudian CTO Gary Ogasawara.

“We’re moving more and more to that,” Ogasawara tells Datanami. “If you can combine the best of both worlds – have the huge capacity of an object store and the advanced query capability of an SQL-type database – that would be the ideal. That’s what people are really asking for.”

Past Is Prologue

We’ve seen this film before. When Apache Hadoop was the hot storage repository for big data (really, less-structured data), the first big community efforts was to develop a relational database for it. That way, data analysts with existing SQL skills – as well as BI applications expecting SQL data – would be able to leverage it without extensive retraining. And besides, after running less-structured data through MapReduce jobs, you  needed a place to put the structured data. A database is that logical place.

This led to the creation of Apache Hive out of Facebook, and the community followed with a host of other SQL-on-Hadoop engines (or relational databases, if you like), including Apache Impala, Presto, and Spark SQL, among others. Of course, Hadoop’s momentum fizzled over the past few years, in part due to the rise of S3 from Amazon Web Services and other cloud-based object storage systems, notably Azure BLOB Storage from Microsoft and Google Cloud Storage, which are universally more user-friendly than Hadoop, if not always cheaper.

In the cloud, users are presented with a wide range of specialty storage repositories and processing engines for SQL and machine learning. On the SQL front, you have Amazon RedShift, Azure Data Warehouse, and Google BigQuery. On top of these “native” offerings, the big data community has adapted many existing and popular analytics databases, including Teradata, Vertica, and others, to work with S3 and other object stores with an S3-compatible API.

The same goes for machine learning workloads. Once the data is in S3 (or Blob Store or Google Cloud Storage), it’s a relatively simple manner to use that data to build and train machine learning models in SageMaker, Azure Machine Learning, or Google Cloud AutoML. With the rise of the cloud, every member of the big data and machine learning community has moved to support the cloud, and with it object storage systems.

As the cloud’s momentum grows, S3 has become the defacto data access standard for the next generation of applications, from SQL analytics and machine learning to more traditional apps too. For many new applications, data is simply expected to be stored in an object storage system, and developers expect to be able to access that data over the S3 API.

A Hybrid Architecture

But of course, not all new applications will live on the cloud with ready access to petabytes of data and gigaflops of computing power. In fact, with the rise of 5G networks and the explosion of smart devices on the Internet of Things (IoT), the physical world is the next frontier for computing, and that’s changing the dynamics for data architects who are trying to foresee new trends.

At Cloudian, Ogasawara and his team are working on adapting its HyperStore object storage architecture to fit into the emerging edge-and-hub computing model. One of the examples he uses is the case of an autonomous car. With cameras, LIDAR, and other sensors, each self-driving car generates terabytes worth of data every day, and petabytes per year.

“That is all being generated at the edge,” he says. “Even with a 5G network, you will never be able to transmit all that data to somewhere else for analyses. You have to push that storage and processing as close to the edge as possible.”

Cloudian is currently working on developing a version of HyperStore that sits on the edge. In the self-driving car example, the local version of HyperStore would run right on the car and assist with storing and processing data coming off the sensors in real time. This computing environment would constitute a fast “inner loop,” Ogasawara says.

“But then you have a slower outer loop that’s also collecting data, and that includes the hub where the large, vast data lake resides in object storage,” he continues. “Here you can do more extensively training of ML models, for example, and then push that kind of metadata out to the edge, where it’s essentially a compiled version of your model that can be used very quickly.”

In the old days, object stores resembled relatively simple (and nearly infinitely scalable) key-value stores. But to support future use cases — like self-driving cars as well as weather modeling and genomics — the object store needs to learn new tricks, like how to stream data in and intelligently filter it so that only a subset of the most important data is forwarded from the edge to the hub.

To that end, Cloudian is working on a new project that will incorporate analytics capabilities. It has a working name of the Hyperstore Analytics Platform, the project would incorporate frameworks like Spark or TensorFlow to assist with the intelligent streaming and processing of data. A beta was expected by the end of the year (at least that was the timeline that Ogasawara shared in early March before the COVID-19 lockdown.)

Object’s Evolution

Cloudian is not the only object storage vendor looking at how to evolve its product to adapt to emerging data challenges. In fact, its not just object storage vendors who are trying to tackle the probolem.

The folks at Confluent have adapted their Kafka-based stream processing technologies (which excel at processing event data) to work more like a database, which is good at managing stateful data. MinIO has SQL extensions that allow its object store to function like a database. NewSQL database vendor MemSQL has long had hooks for Kafka that allow it to process large amounts of real-time data. The in-memory data grid (IMDG) vendors are doing similar things for processing new event data within the context of historic, stateful data. And let’s not even get into how the event meshes are solving this problem.

According to Ogasawara, adapting Cloudian’s HyperStore offering is a logical way to tackle today’s emerging data challenges. “You’ve done very well at building this storage infrastructure,” he says. “Now, how do you make the data usable and consumable? It’s really about providing better access APIs to get to that data, and almost making the object storage more intelligent.”

Object stores are moving beyond their initial use case, which was reading, writing, and deleting data at massive scale. Now customers are pushing object storage vendors to support more advanced workflows, including complex machine learning workflows. That will most likely require an extension to the S3 API (something that Cloudian has brought up with AWS, but without much success).

“How do you look into those objects? Those types of APIs need more and more [capabilities],” Ogasawara says. “And even letting AI or machine learning-type workflows, doing things like a sequence of operations — those types of language constructs, everyone is starting to look at and trying to figure out how do we make it easier for users and customers to make that data analysis possible.”

The post Object Stores Starting to Look Like Databases appeared first on Artificial Intelligence.

What is a cloud-native enterprise and how does an enterprise achieve that designation? A cloud-native enterprise is one that specializes in cloud-native development, or development that is optimized for distributed infrastructures. 

Examples of distributed infrastructures include hybrid clouds — on-premises applications that use products and services from a multitude of sources and applications that leverage a multitude of containers. 

Cloud-native development is optimized for distributed infrastructures because of its ability to bring the automation of the cloud directly to the application stack in the form of automated scalability, elasticity and high availability. By automating the operational management of application infrastructures, cloud-native development enables enhanced development velocity and agility in ways that empower enterprises to produce, disseminate and consume software and application-related services on an unprecedented scale.

The automation specific to cloud-native development is important because it enables the development and maintenance of ecosystems of digitized objects such as connected homes, appliances, automobiles, laptops, mobile devices and wearables. Technology suppliers that are seeking to gain market share in the rapidly emerging landscape of digitized ecosystems would do well to embed cloud-native development practices in their development methodologies by taking the following three steps: (1) embracing platform as a service; (2) cultivating developer familiarity with cloud-native technologies; and (3) creating a developer-centric culture in which everyone is a developer.

Platform as a service is a key component of an enterprise’s transition to cloud-native development because it provides developers with self-service access to developer tools as well as the ability to provision infrastructure. This ability to self-serve accelerates development cadences and empowers developers to work independently of a centralized IT authority. By having access to an integrated platform of products and services, developers enjoy increased developer agility in ways that predispose enhanced responsiveness and participation in collaborative decisions.

Another key step for enterprises in their transition to cloud native involves cultivating developer familiarity with cloud-native technologies such as microservices, containers, container orchestration frameworks and processes such as DevOps. The universe of cloud-native technologies also includes functions as a service, APIs, serverless technologies, service mesh and a multitude of others. That said, cultivating developer familiarity with microservices and containers marks a significant step in an enterprise’s journey to becoming cloud native that is likely to initiate familiarity with adjacent technologies.

To become truly cloud native, enterprises need to create a developer-centric culture in which everyone is a developer. This means that professional resources such as business analysts, project managers, HR, business partners, market intelligence analysts and data scientists all variously participate in application development in one form or another, whether it be through the development of net-new applications by using low-code or no-code development tools, or otherwise through configuring dashboards and widgets in pre-existing application templates. This democratization of development is a key component of an enterprise’s path toward cloud-native development, because it increases the digital literacy of business resources that collaborate with IT resources, which are more directly in charge of developing and maintaining applications.

The increased digital literacy of business stakeholders enables them to more richly inform application developers about the requirements for the digitization of business operations. In addition, the participation of business resources in application development empowers business professionals to contribute to application development and subsequently augment and extend the digitization efforts that are led by professional application developers.

The key takeaway here is that the transition of an enterprise to cloud native transcends the acquisition of developer familiarity with technologies such as microservices, containers, container orchestration frameworks and DevOps. The transition requires the confluence of the adoption of a platform, proficiency with cloud-native technologies and the democratization of development to professional resources who do not have the job title of a developer. This confluence paves the way for an enterprise to perform high velocity, hyperscale development that empowers enterprises to create and maintain ecosystems of digitized objects that serve the intensified needs of the digital economy for increased digitization.

The post Analyst Watch: 3 steps to becoming cloud native appeared first on Artificial Intelligence.

They beat us at chess and trivia, supplant jobs by the thousands, and are about to be let loose on highways and roads as chauffeurs and couriers.

Now, fresh signs of robot supremacy are emerging on Wall Street in the form of machine stock analysts that make more profitable investment choices than humans.

At least, that’s the upshot of one of the first studies of the subject, whose preliminary results were released in January.

Buy recommendations peddled by robo-analysts, which supposedly mimic what traditional equity research departments do but faster and at lower costs, outperform their flesh-and-blood counterparts over the long run, according to Indiana University professors.

“Using this type of technology to make investment recommendations or to conduct investment analyses is going to become increasingly important,” Kenneth Merkley, an associate professor of accounting and one of the authors, said by phone.

Whether getting stock calls right is a critical mission of human analysts is debatable.

‘Analytical rigour’

Wall Street research departments serve a variety of functions, among them connecting investors with company executives and gathering earnings and other corporate data.

While their buy, sell and hold recommendations still garner attention and can move stocks, the number of clients premising investment decisions off them is probably limited.

The study looked at a small and still largely experimental branch of fintech, firms founded on the premise that digital technology does a better job than humans in making equity recommendations.

While all analysts use computers, a handful of start-ups has been seeing if programs can handle every aspect of the stock-picking process.

One firm whose data was in the study, New Constructs, uses robo-analysts to collate everything from income statements and balance sheets to the footnotes at the bottom of reports to push out picks, according to its chief executive officer, David Trainer.

“We’re not doing sentiment, we’re not doing momentum, we’re not doing what I would call all these sexy, short-term metrics,” Trainer declared in a phone interview. “We can give them good, old-fashioned analytical rigour to insert in their process at little to no cost.”

Trainer’s clients – quant funds, consulting firms, IRAs and asset managers, among others – can buy access to the services for anywhere from about US$10 to US$15,000, he affirmed, depending on the type of research.

The Indiana University study analysed more than 76,000 reports issued by seven different robo-analyst firms between 2003 and 2018.

Among the findings were that automated services are more likely to produce sell ratings (as opposed to holds and buys) than traditional firms.

They also revise their reports more frequently and may be better at accounting for large and complex corporate disclosures, including filings with the Securities and Exchange Commission.

The authors compared the robo-analyst recommendations to those of traditional analysts part of the Institutional Brokers’ Estimate System. They omitted holds in their long-term analysis as those recommendations are less actionable, according to Merkley.

Because robo-analysts don’t exhibit any biases and aren’t subject to conflicts of interest, they produce a more balanced distribution of ratings, the authors, who also included Braiden Coleman and Joseph Pacelli, claimed.

Whereas traditional analysts actively work on maintaining relationships with company management, robots aren’t beholden to the same conventions.

Their calls may not get the same pop as humans’ at first, but the recommendations can generate “substantial returns for individual investors,” they claimed.

Besides New Constructs, the researchers analysed recommendations from firms including Minkabu, Rapid Ratings and TheStreet.com that overlapped with those of traditional analysts.

Under pressure

Out of the total pool of outstanding robo-analyst recommendations, more than 30 percent represented buy ratings compared with 47 percent from traditional analysts (the overall number of outstanding recommendations from traditional analysts was five times the robots’).

About a quarter of recommendations from the machines fell into the sell category, compared with six percent from humans.

Traditional analysts have been under pressure recently – machines have been doing a bigger share of the work while investors increasingly pile into passive funds.

In the next decade, automation could reduce headcount on Wall Street and the banking industry by about 200,000, according to Wells Fargo Securities.

“What we’re seeing is that research budgets at these banks are being constrained, the value proposition of sell-side equity research is being reconsidered – for us, this industry is ripe for disruption,” pointed Merkley.

“Technology is one of those disruptions because you can do things probably at lower cost and greater scale.”

Robo-analysts differ from their better-known counterparts, robo-advisers – shops like Betterment that use algorithms to provide automated financial planning services.

But whatever their form, scepticism abounds on Wall Street. Opponents argue that machines are unable to parse nuanced discussions on earnings calls or have conversations with company management.

New Constructs’ Trainer says the opposite is true – robots can analyse huge amounts of data, including transcripts of conversations, at much faster paces.

Nonetheless, “as long as there’s still people that need human interaction, that need to talk to management and talk about the industry and perform that function for the buy-side, the sell-side will still be around,” nuanced Merkley.

The post Robot analysts outwit humans on investment picks, study shows appeared first on Artificial Intelligence.

NEW YORK : They beat us at chess and trivia, supplant jobs by the thousands, and are about to be let loose on highways and roads as chauffeurs and couriers.

Now, fresh signs of robot supremacy are emerging on Wall Street in the form of machine stock analysts that make more profitable investment choices than humans. At least, that’s the upshot of one of the first studies of the subject, whose preliminary results were released in January.

Buy recommendations peddled by robo-analysts, which supposedly mimic what traditional equity research departments do but faster and at lower costs, outperform their flesh-and-blood counterparts over the long run, according to Indiana University professors.

“Using this type of technology to make investment recommendations or to conduct investment analyses is going to become increasingly important,” Kenneth Merkley, an associate professor of accounting and one of the authors, said by phone.

Whether getting stock calls right is a critical mission of human analysts is debatable. Wall Street research departments serve a variety of functions, among them connecting investors with company executives and gathering earnings and other corporate data. While their buy, sell and hold recommendations still garner attention and can move stocks, the number of clients premising investment decisions off them is probably limited.

The study looked at a small and still largely experimental branch of fintech, firms founded on the premise that digital technology does a better job than humans in making equity recommendations. While all analysts use computers, a handful of start-ups has been seeing if programs can handle every aspect of the stock-picking process.

One firm whose data was in the study, New Constructs, uses robo-analysts to collate everything from income statements and balance sheets to the footnotes at the bottom of reports to push out picks, according to its chief executive officer, David Trainer.

“We’re not doing sentiment, we’re not doing momentum, we’re not doing what I would call all these sexy, short-term metrics,” Trainer said in a phone interview. “We can give them good, old-fashioned analytical rigor to insert in their process at little to no cost.”

Trainer’s clients — quant funds, consulting firms, IRAs and asset managers, among others — can buy access to the services for anywhere from about $10 to $15,000, he said, depending on the type of research.

The Indiana University study analyzed more than 76,000 reports issued by seven different robo-analyst firms between 2003 and 2018. Among the findings were that automated services are more likely to produce sell ratings (as opposed to holds and buys) than traditional firms. They also revise their reports more frequently and may be better at accounting for large and complex corporate disclosures, including filings with the Securities and Exchange Commission.

The authors compared the robo-analyst recommendations to those of traditional analysts part of the Institutional Brokers’ Estimate System. They omitted holds in their long-term analysis as those recommendations are less actionable, according to Merkley.

Because robo-analysts don’t exhibit any biases and aren’t subject to conflicts of interest, they produce a more balanced distribution of ratings, the authors, who also included Braiden Coleman and Joseph Pacelli, claimed. Whereas traditional analysts actively work on maintaining relationships with company management, robots aren’t beholden to the same conventions. Their calls may not get the same pop as humans’ at first, but the recommendations can generate “substantial returns for individual investors,” they said.

Besides New Constructs, the researchers analyzed recommendations from firms including Minkabu, Rapid Ratings and TheStreet.com that overlapped with those of traditional analysts.

Out of the total pool of outstanding robo-analyst recommendations, more than 30% represented buy ratings compared with 47% from traditional analysts (the overall number of outstanding recommendations from traditional analysts was five times the robots’). About a quarter of recommendations from the machines fell into the sell category, compared with 6% from humans.

Traditional analysts have been under pressure recently — machines have been doing a bigger share of the work while investors increasingly pile into passive funds. In the next decade, automation could reduce headcount on Wall Street and the banking industry by about 200,000, according to Wells Fargo Securities.

“What we’re seeing is that research budgets at these banks are being constrained, the value proposition of sell-side equity research is being reconsidered — for us, this industry is ripe for disruption,” said Merkley. “Technology is one of those disruptions because you can do things probably at lower cost and greater scale.”

Robo-analysts differ from their better-known counterparts, robo-advisers — shops like Betterment that use algorithms to provide automated financial planning services. But whatever their form, skepticism abounds on Wall Street. Opponents argue that machines are unable to parse nuanced discussions on earnings calls or have conversations with company management. New Constructs’ Trainer says the opposite is true — robots can analyze huge amounts of data, including transcripts of conversations, at much faster paces.

But, “as long as there’s still people that need human interaction, that need to talk to management and talk about the industry and perform that function for the buy-side, the sell-side will still be around,” said Merkley.

The post Robot analysts outwit humans on investment picks, study shows appeared first on Artificial Intelligence.

Part of the failings and shortcomings of AI concern the ramp or process for getting there. For an AI project to succeed, it is critical to have your data fully deployed, available, structured and cleaned. You also must have some algorithms (ML/deep learning/natural language processing) ready to pull the insights and focused data, effectively putting the intelligence into AI.

This requires a very specific skill set and talent pool.

Empower Your Data Analysts

With so many different technology fields colliding, machine learning is a difficult area to master. Deep reinforcement learning (DRL), natural language processing (NLP), AutoML tools, ML Ops, neural networks and model-based reinforcement learning are just some of the subjects required to master ML.

With a shortage of talent and shortfall of data scientists, this is easier said than done. Part of this is because the demand far outweighs the supply, with Bloomberg estimating job postings for data scientists rose 75% from 2015 to 2018. Almost every organization shares this same urgent need.

When faced with a skills gap in their teams and stiff competition in recruiting, how can organizations effectively build a data science team?

One way is to start by looking at your data analysts. They are often swimming in disparate, federated data sources, operating on legacy databases, working with mixed data sources and using Excel to do things it was never intended to do. There is an enormous opportunity to empower data analysts to become data scientists.

From Data Analyst to Data Scientist

Companies looking to hire data scientists from within need to think about how to upskill, reskill or preskill their data analysts to perform the roles needed to implement AI and ML fully. I’m a believer that if you provide prescriptive and progressive curricula around the essential topics a budding data scientist needs, you can equip them with the skills and knowledge to make a difference and move them forward in the organization.

However, it’s not enough to simply equip these employees with a curriculum; the program should have numerous twists and turns. Why? When thinking about how you can train the prototypical ML expert of the future, you must consider all the deep tech skills needed. You also want the business and social skills that are required to attain the expert moniker. A well-rounded ML architect will see the business imperatives, understand how to communicate with engineering and business, and have the tech skills to deploy ML models that benefit your business and customers.

When organizations take roles commonly found in the workplace, such as data analysts, provide them with a sequenced path of instruction that moves them toward an aspirational role, they’ll be well-positioned to realize the full potential of AI and ML.

The post Building Your Data Science Team from Within appeared first on Artificial Intelligence.

When businesses identify a problem that can be solved through machine learning, they brief the data scientists and analysts to create a predictive analytics solution. In many cases, the turnaround time for delivering a solution is pretty long.

Even for experienced data scientists, evolving machine learning models that can accurately predict the results is always challenging and time-consuming. The complex workflow involved in machine learning models have multiple stages. Some of the significant steps include data acquisition, data exploration, feature engineering, model selection, experimentation and prediction.

There are multiple teams that get involved in arriving at the solution. Data engineering team works on data acquisition and preparation. Data scientists focus on experimentation and optimization of models. DevOps teams own the development environment, tooling, and hosting the inference models in production.

One trend that’s going to fundamentally change the face of ML-based solutions is AutoML. It is going to enable business analysts and developers to evolve machine learning models that can address complex scenarios.

AutoML focuses on two aspects – Data acquisition and prediction. All the steps that take place in between these two phases will be abstracted by the AutoML platform. Essentially, users bring their own dataset, identify the labels, and push a button to generate a thoroughly trained and optimized model that’s ready to predict.

When dealing with an AutoML platform, business analysts stay focused on the business problem instead of getting lost in the process and workflow. Most of the platforms prompt users to upload the dataset and then labeling the categories. After that, most of the steps involved in preparing the data, choosing the right algorithm, optimization and hyperparameter tuning are handled behind the scenes. After a while, the platform exposes a REST endpoint that can be used for predictions. This approach significantly changes the traditional workflow involved in training machine learning models.

Source: Janakiram MSV

Traditional ML vs. AutoML

Some AutoML platforms also support exporting the fully trained model compatible with mobile devices running Android and iOS. Developers can quickly integrate the models with their mobile applications without having to learn the nuts and bolts of machine learning.

When AutoML models get exported into Docker containers, DevOps teams would be able to deploy them at scale for inferencing in production environments. They can host the containers in scalable clusters managed by Kubernetes and DC/OS.

The industry is gearing up to deliver AutoML as a Service. Google Cloud AutoML, Microsoft Custom Vision and Clarifai’s image recognition service are early examples of automated ML services.

AutoML perfectly fits in between cognitive APIs and custom ML platforms. It delivers the right level of customization without forcing the developers to go through the elaborate workflow. Unlike cognitive APIs that are often considered as black boxes, AutoML exposes the same degree of flexibility but with custom data combined with portability.

With every platform vendor attempting to democratize machine learning, AutoML is evolving as the future of artificial intelligence. It puts the power of AI in the hands of business analysts and technology decision makers.

The post Why AutoML Is Set To Become The Future Of Artificial Intelligence appeared first on Artificial Intelligence.

Driving sales takes a lot of hard work. It’s a very time-consuming process, where you have to research potential clients, pitch to prospects, and keep following up with them before you close a deal.

Anything that can automate this process will certainly help salespeople close more deals and grow their business faster. The recent developments in artificial intelligence (AI), coupled with the availability of big data and deep learning, have given rise to a few interesting ways that can make it easier to grow your sales.

1. Schedule Meetings

AI-based calendar management tools like x.ai and tevorai.com can help your sales team manage their calendars and appointments automatically. They analyze the calendars of other employees to find free time slots when you can set up a meeting, and they also suggest alternative times, in case of unexpected delays. This saves a lot of time every day and fosters efficient collaboration. You don’t have to waste human hours trying to get the right people together at the right time.

2. Make Notes

One of the most important, yet tedious, aspects of every sales meeting is to take notes. Whether you’re meeting your prospects in person or through an audio call or a videoconference, it’s essential to make detailed notes, so you can effectively follow up later. Tools like clarke.ai and wrapup.co provide a powerful AI-based listening and note-making system that enables you to confidently preside over your sales meetings without the fear of losing any important points. Some of these tools even analyze client behavior and engagement to help you personalize your future communications.

3. Predict Customer Behavior

Did you know that AI systems can even create a customer profile for your business, based on user habits and behavior? By taking into account factors such as gender, age, location, demographics, and sentiments, they can predict what people need or are looking to buy. Salespeople can use these insights to refine their sales pitches and convert more leads into customers.

4. Target Customers Efficiently

If your salespeople don’t target the right prospects, they won’t be able to close many deals. AI-based platforms like Albert.ai use big data to help businesses target a specific group of potential customers, instead of going after a large pool of people. This allows you to execute marketing campaigns more effectively. While a data analyst would take hours to sift through big data sets and find the right prospects, an AI-based tool can do it in a matter of seconds. By empowering data analysts with AI systems, you can quickly and accurately sort through this information and improve your sales performance.

5. Increase Engagement

Today, instead of showing news in chronological order, companies are using AI to show people what they’re interested in. By learning user behavior and interests, companies like Facebook are using AI algorithms to make news feeds smarter and more personalized. What does this mean for your salespeople? This provides businesses more opportunities to put themselves in front of their target audience by posting updates that are related to people’s interests, resulting in more sales opportunities.

6. Provide More Selling Time

AI-driven, voice-controlled digital sales assistants like People.ai make it easy to manage your sales data, while your salespeople can spend more time on the actual process of selling. From managing leads to updating your CRM to improving forecasting, a digital assistant can take care of it all. By eliminating the need to manually enter data, AI-based systems not only ensure better data accuracy, but also leave your sales representatives more time to convert leads.

7. Close More Deals

With AI, you can improve every step of your sales process. You can start by identifying the right prospects. Furthermore, you can use AI-powered CRM systems like Salesforce’s Einstein to get detailed insights, smart recommendations, and predictions. Such powerful information will empower your salespeople to target the right people, personalize their sales pitch, add more value to every sales meeting, and close more deals faster. As data management is completely handled by AI, it will reduce errors and boost efficiency. With the availability of error-free data and actionable insights, your sales team can develop more effective strategies that drive conversions.

AI systems have changed the way businesses operate, and can improve the sales team performance by intelligently automating many of your day-to-day tasks, while adding a personal touch to your prospect interactions. By combining human behavior and AI-based tools, you can certainly organize and even optimize your sales processes.

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