Why Should Hedge Funds Embrace the Public Cloud?

With public cloud providers updating their offerings to meet the needs of different businesses, feature enhancements such as robust security controls have shed the public cloud’s image as an unstable environment.


Public clouds offer a simple way for hedge funds to reduce costs by eliminating on-premises hardware and software expenses. They also provide instant scalability so that hedge funds can grow or shrink resources in real time without having to worry about capacity constraints. Finally, they allow hedge funds to focus on what they do best – investing – while leaving infrastructure management tasks like patching and upgrading up to the experts at AWS.

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• Vast Network of Partners and Service Providers

Amazon Web Services has more than 2,400 service partners and 650 AWS-certified technology providers. These businesses offer tools that help hedge funds optimize their workloads in the cloud. For example, they can help run high performance computing jobs at scale to generate artificial intelligence algorithms or forecast daily market moves using advanced statistical models. Best of all, the majority of AWS partners offer free trials so that hedge funds can try out products for a few weeks before making an investment decision.

Low Startup Costs

Many hedge funds start out as startups because it’s often easier to raise money from small investors when you don’t have the overhead costs associated with running a brick-and-mortar You know the benefits of moving to the public cloud, but don’t have the time or knowledge to implement it

You’re managing application downtime and security incidents on a regular basis. Why not move your apps to a cloud provider? 

Reduce infrastructure costs with scalable virtual machines. Leave expensive hardware purchases behind!

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Click this ad right now and sign up for a free trial of Amazon Web Services!

Cloud computing has been the buzzword for a while now, but it seems like public cloud providers are finally stepping up their game in order to meet businesses’ needs. With increased security features and more robust offerings, public cloud is no longer seen as an unstable environment. You can still take advantage of this trend by partnering with one of our experts today!

What is Data Science and Why do we Need it?

Data science is often billed as the future of Artificial Intelligence (AI), which means it has become more important than ever to understand the concepts and purpose behind it. But first, we need to define it. According to “What Is Data Science?” on Edureka, data science is a blend of various tools, algorithms, and machine learning principles with the ultimate goal of discovering hidden patterns in the raw data.

Data Science

It’s easy to argue that scientists have been using data for years, and while that’s true, the difference between data and data science comes down to explaining vs. predicting. A data scientist uses analysis to discover insights, along with machine learning algorithms to predict future occurrences of the data. This is far more in-depth than what a data analyst does, which is to process the history of the data.
Because of this distinction, data science is most often used for predictive casual and prescriptive analytics, as well as machine learning for making predictions and pattern discovery. With data analysis and machine learning, data scientists are able to determine if something that has already happened is likely to happen again in the future. For example, a data scientist can analyze payments made to a health insurance company to determine if future payments will continue to be made, whether or not those payments occur on time. Another example, using pattern discovery, a data scientist is able to find clusters of users for user segmentation, according to the users’ interests.

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Phases of the Data Science Life Cycle

Data Science can be divided into a seven step life cycle.There are six main phases and, as with any process, the phases need to occur in a specific order in order to be successful. In phase one, discovery, the scientist needs to determine what information they’re looking for and gain an understanding of the projects requirements and priorities. During this phase, hypotheses are formed.

In phase two, data preparation, scientists create an analytical sandbox so they have somewhere to store the data they’re researching and will eventually distill into more formal information. During this phase, scientists can establish the variables they are looking for that will help them answer the hypotheses.

In phase three, model planning, scientists determine methods to connect variables to one another. These variables are implemented via algorithms in phase four, model building. This is the testing and training phase, when models are run to determine if the tools that have already been created are sufficient or if stronger environments need to be created.

In phase five, operationalize, a pilot project is often created in a real-time environment. This step helps determine if the project is strong enough to be deployed or if more work needs to be done. In the final phase, it’s time to communicate the results obtained from research done by the data scientists.

All of these phases work in tandem with one another to achieve well-researched data that helps scientists determine what the future will look like within specific businesses.

Required Data Scientist Skills

In order to be successful as a data scientist, there are eight skills that should be honed and maintained. It’s vital to have programming skills, especially knowing a programming language such as R or Python, along with a database query language like SQL. It’s also important to have an understanding of statistics, especially when it comes to figuring out which techniques are valid or invalid.

In order to best understand data, especially at companies such as Netflix, it’s important to have more than a basic understanding of machine learning methods, such as k-nearest neighbors and random forests. Data scientists should also have an understanding of multivariable calculus and linear algebra. Knowing about predictive performance can also be a big win for companies in this field.
Data wrangling and data visualization and communication are also very important. These three methods are very important for communicating the data produced from the research scientists have done. Data visualization tools, such as matplotlibggplot, and d3.js can all aid data scientists in organizing their research for presentation.

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Why Should Developers Learn Data Science?

There is scarcity in the data science field so jobs are plentiful in most places, particularly if candidates possess the above skills. By learning these skills and using the many resources available to data scientists it becomes much easier to break into the field and find steady work.

When it comes to data science, things are growing by leaps and bounds. By acquiring the necessary skills, people can go on to have very successful careers in the field. There is also a lot of room for growth in the field as scientists learn more and get more skills under their belts.


The tireless procession of new technologies is unfurling on numerous fronts. Pretty much every technology is charged as advancement, and the rundown of “next huge things” becomes evermore. Not every emerging technology will change the business or social scene, however, some really can possibly disrupt the state of affairs, adjust the manner in which individuals live and work and rework value pools. It is subsequently important that business and policy leaders comprehend which technologies will matter to them and get ready in like manner.


The expansion of connected devices and sensors, combined with a thousand-fold increment in computing power over the previous decade, is opening up better approaches to deliver services and interact with customers. For example, the IoT (extensively characterized as a combination of sensors, analytics, and connectivity) permits industrial organizations to screen equipment health remotely and grow new commercial offerings, for example, outcome-based contracts in industries with high downtime costs. Industrial organizations have begun building technology empowered capacities to make the most of these opportunities.

Numerous different organizations are beginning to apply advanced analytics (AA) and digital tools to determine instantaneous insights into field activities and use them to streamline deployment in real-time through strategies, for example, dynamic field dispatching and remote servicing. These advancements are allowing industrial organizations to convey a stage change in impact through improved technician productivity, decreased interim to fix and higher consumer satisfaction.

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Advanced robotics, that is, progressively competent robots or robotic tools, with improved “senses,” dexterity and intelligence can take on tasks once thought excessively fragile or uneconomical to automate. These technologies can likewise create huge cultural advantages, including robotic surgical systems that make the procedure less intrusive, as well as automated prosthetics and “exoskeletons” that reestablish elements of amputees and the elderly.

Customarily, the trouble of foreseeing and managing customer demand has prompted high equipment downtime and poor service. Two drivers of this eccentrics are the constrained utilization of scheduled servicing and the low infiltration of condition-based monitoring, in which equipment is checked while in activity. Enterprises fluctuate in their way to deal with scheduled servicing, yet out-of-guarantee resources commonly endure lower adoption and more unplanned repairs. A couple of enterprises, for example, aviation, renewable energy, and mining have begun to adopt IoT-empowered condition monitoring to forestall resource breakdowns, however, few OEMs up ’til now have the infrastructure and technology to offer their clients monitoring services.

With the headway of disruptive technology, consumers can book food, taxis at the click of a mouse, without leaving their rooms. Consumers expect services like ‘Apple Easy’ and ‘Google Fast’ in various parts of their lives, demanding quick and seamless experiences always. It’s normal that Customer experience management will keep on driving accomplishment in all segments this year.

It may imply that a lot of companies would need to return to the planning phase and begin consolidating customer-centricity into their business models. Genuinely, as and when the e-commerce market gets saturated, customer experience would be the central factor that would enable occupant brands to slice through the noise in the market.

Disruptive technologies can change the game for organizations, making completely new products and services, just as moving pools of significant value between producers or from producers to consumers. Companies will regularly need to utilize business-model innovations to catch some of that value. Leaders need to get ready for a scope of situations, deserting presumptions about where competition and risk could emerge from and not be hesitant to look beyond long-established models. Companies will likewise need to stay up with employees’ skills up-to-date and balance the potential advantages of rising technologies with the dangers they sometimes present.

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With the adoption of Artificial Intelligence (AI) taking control over the worldwide business, days ahead may be a serious tech empowered. According to a report by PwC, AI’s potential contribution to the worldwide economy could reach $15.7 trillion by 2030. Additionally, the market for the Internet of Things (IoT) and Industrial Internet of Things (IIoT) is probably going to develop exponentially in 2020, since use cases for the innovation would keep on rising across sectors. The days ahead would see an expansion in the commercialisation of IoT data, hence starting the information economy for IIoT. All things considered, throughout the following year, IIoT platform services will keep on going to public cloud providers. Supply chain can likewise utilize the information gathered from IoT, from research and development to suppliers providing goods, through different phases of manufacturing.

It is normal that the work environments in 2020 will see ‘augmented collaboration’ with people and robots working one next to the other. This blend of people and robots is as of now noticeable in companies like Amazon and Google. However, individuals have been working collaboratively with PCs and mobiles for a considerable time. In any case, with the appearance of human-machine convergence, things will turn out to be a lot of worthwhile. Starting with cutting edge robotic technology, from ‘smart glasses’ to intelligent assistants. Additionally, autonomous machines would be equipped for taking on and finishing more undertakings, consequently empowering people to concentrate on value-added work.

How Tech Could Rescue the Awful Democratic Debates

Debates Like many of you, I watched eight-plus hours of Democratic debates last week, and they seem to be getting worse over time. The last effort made it look like CNN was trying harder to create drama than to help people make a choice among the candidates.


We have a ton of technology — some new, some in place for decades — that could make this process far more informative and help people to make a decision that’s right for them. It also might get people excited about the election, so they actually would vote.

I’ll offer some ideas, as I did after the first round, on how we could use technology to make the debates more meaningful and improve the quality of our choices (we really need to improve the quality of our choices). I’ll close with my product of the week: an interesting new smart tablet from Lenovo that thinks it’s an Echo Show and arguably is a better value.

The Problem

The Democrats fielded a buttload of candidates who largely seem to be clones. This makes choosing among them difficult. Were it our job to make the choice (which it is), the debates so far haven’t helped much.

The No. 1 thing that the Democrats want (and I expect not an insignificant number of independents and even moderate Republicans) is a replacement for Donald Trump. I mean, if the Democrats lose, it really doesn’t matter what their candidate intended to do — it won’t get done.

Now that should make it pretty simple: Run largely on a platform of removing Trump from office, fixing what is perceived as broken, and not breaking stuff that isn’t. Instead, the Democratic candidates seem to be focused on issues that will polarize the voters and have many thinking that the president is the better of two bad choices.

For instance, once again on healthcare (this was the problem with Obamacare), they are focusing on getting the government to pay for it rather than focusing on the real problem — the excessive costs. We are the source of government funding, so just shifting the payee doesn’t fix the real problem, given the money comes from us regardless.

There is also the issue of reparations, which would take money from folks who did no wrong and give it to folks who weren’t harmed directly, and no one is talking about amounts. Rough calculations suggest that at US$500 million, it would only give between $10 and $20 per person (around 40 million black people in the U.S. = $12.50 per person at $500 million).

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It would cost less than that per taxpayer (some of whom would be the people getting the money; a tax exclusively on white people would be, well, racist). The tax per person would be around $6.25 (assuming 80 million taxpayers) netting those who got the reparations around $6.25 each. Don’t spend it all at once… . That assumes the collection and distribution of the money would be free, which it wouldn’t be.

Yet if that money, all $500 million, were to go toward fixing education or reducing racial bias in the legal system or promoting more diversity in politics (note I’m saying “or” not “and,” so it isn’t diluted so much it can’t make a difference) the impact would be far more meaningful. Without detailing what people will get, or what people will pay, any proposal is likely to create more conflict between the races (not enough paid out, too much paid in).

The candidates seem to have no idea that all the “Day One” stuff is B.S. For the first several weeks they’ll be learning the job. Getting anything done, other than being sworn in, is unlikely, given how that first day goes.

In reality, folks don’t care whether something is done on Day One or Day 300, as long as it gets done. Much of what is being promised won’t get done at all, let alone on the first day. (The history of presidents keeping promises suggests most were just blowing hot air during their campaigns.)

Given the platforms, it really looks to me like the Democrats are trying to lose — but be that as it may, here is how we could apply technology to help improve the process.

A BS Meter Application

It wouldn’t be hard for a news service to create a B.S. meter. Using machine learning and working off campaign promises and the cumulative coverage of the candidates, an outlet could put up a running B.S. meter, much like The Washington Post‘s Pinocchio score (and it would be useful both for the primaries and the general election).

The system wouldn’t be limited to showcasing whether what was said was true. It also could gauge whether promises made were achievable by probability. For instance, a lot of candidates made promises that Congress, which is dysfunctional and has been the bane of both parties’ presidents, would have to enact — and likely wouldn’t.

The nice thing about a machine learning or deep learning artificial intelligence would be that it would learn over time, and that also would force the candidates to be more honest and realistic. I don’t know about you, but I’m a tad tired of politicians who seem to get into office due to their ability to lie to us. Or, put another way, this could help change “honest politician” into something that isn’t an oxymoron.

Virtual Debates

A few years back IBM demonstrated its Watson AI verbally debating a professional debater. While the system lost, it performed incredibly well. Most interesting was that the system was more entertaining than the human and arguably more accurate.

You could get that same system to emulate Trump and you could have a virtual debate between each of the prospective candidates and the virtual president. The result would provide a far better idea of which candidate could best counter Trump’s unique style of manipulated facts and ad hominem attacks.


This also could help offset the perception problem with minority and woman candidates. Voters have locked into their heads what a president should look like, and that clearly is an old white guy. If done right, this approach could help broaden that perception to include a woman of color who, on paper, might be the strongest natural foil for President Trump. It would focus on performance first, however, better ensuring the intended outcome.

I’m actually surprised this isn’t being done for debate preparation — even if it weren’t broadcast, the training would be invaluable.

Completed Thoughts

One of the real problems with the last debate was that the format was too tight, and candidates often were unable to complete a thought. Now the post-debate coverage could deal with that, but after five hours of the debate (with commercials), I’m not sure how many people would be hanging in. However, with the addition of active links, people could go and read what the candidate wasn’t allowed to finish saying.

In addition, candidates could record complete answers after the fact, and much like an extended movie with added footage, there could be a post-debate experience that would give viewers more information on issues that concerned them. The 15-second rule was insane, in that it had no real connection to reality. One would need a ton of specialized training to be able to articulate complex thoughts consistently in 15 seconds.

An AI application could do it in real time, though, and if the AI could feed a display on the podium, the candidate then could pass on learning the 15-second skill and leave it to the AI, which could perform better. This also would be a great example of how to use AIs to help humans advance the technology, which is in an area the U.S. currently dominates (China is coming on fast though).

Instrument the Audience

One of the things I’m surprised hasn’t happened yet is the creation of an app that would allow the audience to provide feedback on what they are seeing. The surveys on how people will vote aren’t reliable, given there is just too much time between now and the election. However, it would be possible to monitor how perceptions were changing and report that in real time.

That way candidates could better hone their messaging. With results pooled by interests, people could see how their like-minded peers were trending, which might help them realize a candidate they liked actually wasn’t right for them, and help them identify a candidate who would be better on merit, rather than on sex or race.

Aggregated Impact

One thing the current-generation system can do is take and parse a lot of information quickly and provide voters with an individual context at scale. What I mean is that a properly trained AI could consider your personal information and provide a ranking of the candidates based on which one proposed the path most favorable to you personally, based on your interests and investments. It also could adjust that for the likelihood of execution.

So, you’d get a ranking not only of who was listening more, but also of who was likely to accomplish more. For instance, both Sanders and Warren have similar aggressive policy proposals, but Warren is better integrated into the party and thus more likely to get done what she promises. That’s an important distinction that likely is lost on most.

Once again, this would provide a strong counterpoint to voting based on physical appearance and likability to voting based on merit and ability to accomplish the tasks you, as a voter, want done. This would be useful for more than debates. It could be a website that voters could check periodically. It could go beyond the election, showing a running tab of how well the then-president was living up to promises made.

Wrapping Up

It continues to amaze me that while we are the world technology leader, we don’t use technology better to ensure a well-run government. The last guy we elected was a reality-TV star. He doesn’t like to read and arguably has created far more problems than he has fixed. That means the process is broken, and if we don’t fix the process our happy future is anything but certain.

Technology, particularly artificial intelligence, is incredibly good at helping people make difficult decisions at scale. We need to apply it to the political process, so we are better able to pick candidates who don’t just tell us what we want to hear but make actual progress. Without technology, that appears to be a bridge too far.