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3 Essential Steps to Getting Paid to Speak Right Away

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June 12, 2020 6 min read

Opinions expressed by Entrepreneur contributors are their own.

When I first started out as a speaker and coach back in 2001, I thought getting paid to speak was something completely out of reach for a “regular” guy like myself. I thought you had to be a famous football coach or a five-time New York Times best-selling author.

But as I’ve learned over the years, getting paid speaking engagements is not as hard as you might think. Why? Because outside of professional sports, where they draft college players into the pro ranks, professional speaking is one of the few industries where “the powers that be” have a vested interest in bringing in new folks.

In other words, if I’m a meeting planner, there’s no way I can bring in the same speakers to this year’s annual conference as I did last year. To the participants, unless I bring in new names and faces on an annual basis, it looks like I’m not doing my job.

So if I’m a meeting planner with an all-day event coming up — say, two keynotes (morning and lunch) and eight breakouts — then I’ve got a total of 10 slots to fill. But as we already said, I can’t use the same 10 folks I had last year, or from the year before. Meaning that for my 2020 conference, I need to find Competent Paid Speakers #21 through #30 from my list. And that’s the challenge that every meeting planner faces for every conference, every year.

Let’s say there is a total of 100 events annually you could potentially speak at based on your expertise. And since this isn’t year one of the conference, the meeting planners for those events already hired the first 1,000 speakers in 2018 (remember: two keynotes plus eight breakouts = 10 speaking slots, times the 100 total events) and the next 1,000 in 2019, because they can’t hire the same folks twice. Meaning that if you’re the 2,001st decent speaker in your field that meeting planners are aware of, and you can get on their radar, then you too have a shot at getting paid to speak for their next upcoming event.

This is an over-simplified example, but I think you get the idea: You can actually get paid to speak without being some super-famous person. So what can you do right now to start getting paid speaking engagements? I’m glad you asked.

Related: The Most Obvious (But Still Unusual) Way to Engage More Qualified Prospects

Step 1: Create a compelling title for your talk

I can’t emphasize this enough, because for most speakers this is where they get tripped up. Think of the title for your talk as the speaking equivalent to the cover of a book. And while we all know that you don’t want to judge a book by its cover, I think it’s fair to say that a good-looking cover and an interesting title go a long way towards you picking up any book from the shelf.

The same is true for meeting planners. They look at hundreds of potential speakers a month, so you want to have a title that jumps off the page. Personally, I’m a “how to” man myself. “How to Overachieve Without Over-Committing” and “How to Talk So Others Will Listen” are a couple of titles I use a lot.  Turns out that when you use “how to” for solving a relevant problem people have, meeting planners are interested.

Step 2: Create a client-attracting description of your talk

After creating a compelling title, the next thing to work on is the actual description of your talk, and this doesn’t have to be hard either. As long as you can describe your topic in the context of a problem that your talk will solve for audience members, then you’ll be fine. Here’s an example from my talk on “How Overachieve Without Over-Committing”.

Do ever feel like you’ve got so much going on that you’re not sure where to start, let alone whether or not you’ll get everything done? Well say no more, as popular speaker and best-selling author Brian Hilliard shares some tips and tricks for getting stuff done … without killing yourself in the process!

See what I did? I wrote the description in the context of a problem that I believe a lot of people face, and then I presented my talk as a potential solution. Pretty straightforward, right?

Step 3: Start branding yourself as a paid speaker

Before you hire a fancy PR Firm to start “branding” yourself as a paid speaker, let’s just step back a bit. All I’m talking about is having a page on your website that successfully positions you as a paid speaker.  Here’s what that page can look like:

  • A professional headshot;
  • A description of your talk(s);
  • Your bio;
  • A list of some clients and/or speaking appearances you’ve previously had (optional); and
  • Some video of you actually speaking to an audience.

And that’s it. Our only objective here is to show people that you do this for a living, and that if they hire you, they can rest assured that you’re a competent professional who not only knows what they’re talking about, but can deliver that information in an entertaining manner.

Related: This Simple Change Can Make More Prospects Say ‘Yes’ to Your Offers

The bottom line is that getting paid to speak doesn’t have to be hard. It can be, but like most things in life, it doesn’t have to be. If you really do want to get out there and start getting paid speaking gigs, you really only need to do a few things to get moving, because make no mistake about it: Someone right now with similar expertise is getting paid to speak. And my question is: Why can’t that person be you?

Source: http://feedproxy.google.com/~r/entrepreneur/latest/~3/JwRygkMz9CE/350319

AI

HPE acquires Determined AI to bolster its high-performance compute business

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Elevate your enterprise data technology and strategy at Transform 2021.


Hewlett Packard Enterprise (HPE) today announced it has acquired Determined AI, a San Francisco, California-based startup developing an open source platform for building machine learning models. The two companies say the deal will combine Determined AI’s software with HPE’s high-performance computing (HPC) offerings. Terms of the deal weren’t publicly disclosed.

Building and training machine learning models are among the most demanding stages in AI development because researchers must face challenges in HPC. These include setting up and managing parallel workloads and configuring infrastructure that spans compute, storage, fabric, and accelerators. Researchers also need to know how to program, schedule, and train their models to maximize utilization of the infrastructure they’ve set up.

Determined AI, which was cofounded in 2017 by Ameet Talwalkar, Evan Sparks, and Neil Conway, counts among its key contributors Ph.D. students and faculty from the University of California, Berkeley and Carnegie Mellon. The company’s platform helps set up, fine-tune, manage, and share workstations and clusters that run on-premises or in the cloud, as well as speeding up model training via capabilities like accelerator scheduling, advanced hyperparameter optimization, and neural architecture search. (Hyperparameters are variables whose values are used to control the learning process, while neural architecture search is a technique for automating the design of particular AI models.)

Determined AI claims it accelerated AI-guided drug discovery for one customer from three days to three hours. “The Determined AI team is excited to join HPE, who shares our vision to realize the potential of AI,” Sparks said. “Over the last several years, building AI applications has become extremely compute-, data-, and communication-intensive. By combining with HPE’s industry-leading HPC and AI solutions, we can accelerate our mission to build cutting-edge AI applications and significantly expand our customer reach.”

Growth in HPC

With the Determined AI purchase, HPE is chasing after an HPC market that’s becoming red hot. According to IDC, the accelerated AI server segment is expected to grow by 38% each year to reach $18 billion by 2024. And Intersect360 Research notes that the demand for HPC will increase by more than 40% to reach almost $55 billion in revenue by 2024.

In May, HPE expanded its GreenLake hybrid cloud services platform with a new data services cloud console, a suite of infrastructure management services, and HPE Alletra, cloud-native infrastructure for edge-to-cloud data workflows. In December 2020, HPE took the wraps off HPE GreenLake cloud services, a pay-per-use service providing access to fully managed, prebundled services based on HPC systems.

“As we enter the Age of Insight, our customers recognize the need to add machine learning to deliver better and faster answers from their data,” HPE SVP Justin Hotard said in a press release. “AI-powered technologies will play an increasingly critical role in turning data into readily available, actionable information to fuel this new era. Determined AI’s unique open source platform allows machine learning engineers to build models faster and deliver business value sooner without having to worry about the underlying infrastructure. I am pleased to welcome the world-class Determined AI team, who share our vision to make AI more accessible for our customers and users, into the HPE family.”

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Source: https://venturebeat.com/2021/06/21/hpe-acquires-determined-ai-to-bolster-its-high-performance-compute-business/

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AI

Transform 2021: Where does your enterprise stand on the AI adoption curve?

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Elevate your enterprise data technology and strategy at Transform 2021.


VentureBeat’s annual AI survey returns alongside Transform 2021 this July 12-16, 2021. 

Transform, the world’s leading event on applied AI for enterprise business & technology decision makers, will host thousands of executives across industries to share their AI and data technology stories. 

VentureBeat is bringing back its AI survey to sample leaders who are innovating and integrating AI and other large data projects into their workflows and product development, as well as those who are interested in learning how to get started. It takes just a few minutes, and in return, you’ll get an exclusive look at the full results. You’ll also be invited to join Transform as a VIP passholder and hear from Matt Marshall, CEO at VentureBeat, on July 16th for top learnings and trends gleaned from results. 

Attendees from across the globe will join Transform online to hear from top industry experts on strategy and technology in the main application areas of AI/ML automation technology, data, analytics, intelligent automation, conversational AI, intelligent AI assistants, AI at the edge, IoT, & computer vision. Executives from enterprises in healthcare, finance, retail, manufacturing, security, and technology, are invited to join vertical tracks at Transform 2021. Take the AI survey or register today to join VentureBeat for 5 days of AI and data.  

To learn more about where your company stands on the AI adoption curve, and how the market is evolving in the data-driven era, take the VB AI Survey now, and join VentureBeat at the AI and data event of the year. Responses must be completed by July 9 at 5 P.M. Pacific.

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Source: https://venturebeat.com/2021/06/17/transform-2021-where-does-your-enterprise-stand-on-the-ai-adoption-curve/

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AI

Grafana Labs acquires load-testing startup K6

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Elevate your enterprise data technology and strategy at Transform 2021.


Observability platform Grafana Labs today announced it has acquired K6, a Stockholm-headquartered startup that’s building an open source load testing tool for engineering teams. Grafana says it and K6 will work together on an integrated offering as part of Grafana’s tech stack, providing a way to monitor and connect logs, metrics, and traces to diagnose app performance issues.

“When we first spoke with the team at K6, we were immediately impressed by the incredible similarities between them and Grafana, including our passion for open source and how they modernized load testing,” Grafana cofounder and CEO Raj Dutt said in a press release. “In the past, load testing required costly infrastructure investments to run, resulting in only the most well-funded efforts reaping the benefits. These days, compute is available on demand and microservices are ephemeral, so it is far less costly to run test simulations. Because K6 is open source and also has a cloud offering, developers can realize the benefits much more rapidly.”

In 2000, K6’s founding team was working on a massively multiplayer online role-playing game (MMORPG), with the goal of supporting hundreds of players simultaneously. The need for early load testing arose, and the team opted to open a consultancy to support organizations including the European Space Agency. In 2008, the team pivoted to bring its load testing tool to market as a website benchmarking product. And in 2016, K6 started working on a new open source load testing tool for automating performance tests.

K6

Above: K6’s load testing platform.

Image Credit: K6

“Grafana is the unquestioned leader in the observability space, and [they] have done an incredible job building an open and composable observability stack for their users — both on the open source and enterprise fronts,” K6 CEO Robin Gustafasson said. “Joining the Grafana family will accelerate our ability to give modern engineering teams better ways to observe and build reliable applications.”

Platform expansion

The K6 acquisition comes a week after Grafana detailed a series of product updates, including the general availability of Grafana 8.0 and Grafana Tempo 1.0, as well as new machine learning capabilities. The latest release of Grafana includes enhanced visualizations, a unified alerting system, and the debut of Grafana Tempo distributed tracing.

More than 740,000 people are actively using the Grafana platform to visualize data from smart home devices, business apps, and more, according to Grafana cofounder Torkel Ödegaard. And Grafana has over 1,000 paying customers, including Bloomberg, JP Morgan Chase, eBay, PayPal, and Sony.

A comprehensive software monitoring solution, along with a number of other technical practices, has the potential to positively contribute to continuous delivery. According to VMWare Tanzu, 92% of businesses are using observability tools to enable more effective business decision-making.  Given the large number of metrics collected about the behavior of distributed app environments, real-time business insights can emerge with the use of observability tools, creating value for stakeholders.

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Source: https://venturebeat.com/2021/06/17/grafana-labs-acquires-load-testing-startup-k6/

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Graph database platform Neo4j raises $325M to inform decision-making

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Graph platform Neo4j today announced that it raised $325 million at an over $2 billion valuation in a series F round led by Eurazeo, with additional investment from GV. The capital, which brings the company’s total raised to date to over $500 million, will be put toward expanding Neo4j’s platform, workforce, and customer base, the company says.

Markets and Markets anticipates the graph database market will reach $2.4 billion by 2023 from $821.8 million in 2018. And analysts at Gartner expect that enterprise graph processing and graph databases will grow 100% annually through 2022, facilitating decision-making in 30% of organizations by 2023. Graph databases and graph-oriented databases leverage graph structures for semantic queries, with nodes, edges, and properties that store and represent data. They’re a type of non-relational technology that depicts the relationships connecting various entities — like two people in a social network, for instance — and that can analyze interconnected data.

Neo4j offers an open source NoSQL graph database written in Java and Scala with a declarative query language called Cypher. It supports a number of applications, including identity and access management, knowledge graph augmentation, and network and database infrastructure monitoring, as well as risk reporting compliance and social media graphs.

Neo4j’s founders encountered performance problems with relational database management systems, which inspired their decision to build the first Neo4j prototype. Emil Eifrem, the founder and CEO of the company, sketched what today is known as the property graph model on an airplane napkin during a flight to Mumbai in 2000. A property graph is a type of graph where relationships are not only connections but carry a name and some properties.

“Neo4j has been downloaded more than 120 million times by over 200 million developers, more than 50,000 of which are trained. Our main competition is legacy SQL systems that are bogged down by low-performance queries,” Eifrem told VentureBeat via email. “We see competition as a good thing, as smaller companies tend to stake out market niches that might go unidentified by the larger leaders. Competition fuels innovation, as it motivates every vendor to be better, and that’s good news for customers. ”

On the backend

Neo4j features constant time traversals that can scale up to billions of nodes, a flexible property graph schema that adapts over time, and drivers for popular programming languages like JavaScript, .NET, Go, and Python. It’s compliant with ACID (atomicity, consistency, isolation, and durability) requirements, meaning it guarantees database transactions even in the event of power failures and errors. And on the AI front, it supports high-performance graph queries on large datasets.

Neo4j

Above: An example of a graph database created with the Neo4j platform.

Image Credit: Neo4j

Development on Neo4j began in 2003, and it’s been publicly available since 2007 in two editions: a free Community edition and an Enterprise edition. The Enterprise edition adds hot backups, parallel graph algorithms, LDAP and active directory integration, multi-clustering, larger graphs, and more.

“Graph technologies are a purpose-built method for adding and leveraging context from data and are increasingly integrated with machine learning and AI solutions in order to add contextual information … Graphs also serve as a source of truth for AI-related data and components for greater reliability. This is especially important for AI bias. Providing these context and connections to AI systems to have more situationally appropriate outcomes mirrors the decisions in the same way humans do,” Eifrem said. “Graphs can also greatly increase the accuracy of machine learning models with the data you already have. Graphs increase the dimensionality of your data by adding relationships which we know are highly predictive of behavior.”

Graph database growth

Gartner predicts that graph processing and graph databases “will grow at 100% annually over the next few years to accelerate data preparation and enable more complex and adaptive data [analytics].” In a Neo Technology survey conducted by Evans Data Corporation, 49% of companies said that they anticipate taking on real-time recommendations through graph databases in the next two years. Fifty-eight percent said that they’re already using graph databases at scale.

Data analytics is the science of analyzing raw data to extract meaningful insights. A range of organizations can use data to boost their marketing strategies, increase their bottom line, personalize their content, and better understand their customers. Businesses that use big data increase their profits by an average of 8%, according to a survey conducted by BARC.

Startups like TigerGraph, MongoDB, Cambridge Semantics, DataStax, and others compete with Neo4j in a graph database market expected to be worth $2.4 billion by 2023, in addition to incumbents like Microsoft and Oracle. Even Amazon threw its hat in the graph database ring in November 2017 with the launch of Neptune, a fully managed graph database powered by its Amazon Web Services division.

But Neo4j — which has over 500 employees — has achieved a few pretty impressive milestones, including more than 3 million downloads as of November 2018 and over 300 enterprise subscription users. The company counts among its current and previous customers Lyft, Walmart, eBay, Adobe, Orange, Monsanto, IBM, Microsoft, Cisco, Medium, Airbnb, NASA, and the U.S. Army.

Neo4j customer Meredith Corporation says it scaled its Neo4j graph to analyze 30 billion nodes of digital traffic and has tested capacity to accommodate 100 billion in the future. Recently, Neo4j itself demonstrated real-time query performance against a graph with over 200 billion nodes and more than a trillion relationships running on over a thousand machines.

Last year, Neo4j introduced Neo4j for Graph Data Science, which the company claims is the first data science environment built to harness the predictive power of relationships for scenarios like fraud detection, customer and patient journey tracking, and drug discovery. It arrived alongside Neo4j Aura Professional on Google Cloud Platform, a fully integrated graph database service on the Google Cloud Marketplace designed for small and medium-size businesses. Neo4j also recently debuted the Neo4j BI Connector, which presents live graph datasets for analysis within popular business intelligence technologies including Tableau and Looker. And the company rolled out the Neo4j Connector for Apache Spark, an integration tool to move data bi-directionally between the Neo4j Graph Platform and Apache Spark.

In addition to Eurazeo and GV, Creandum also participated in San Mateo, California-based Neo4j’s latest fundraising round, as did Greenbridge Partners, DTCO, Lightrock, and One Peak Partners. Neo4j previously closed a $40 million venture round led by One Peak.

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  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
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Source: https://venturebeat.com/2021/06/17/graph-database-platform-neo4j-raises-320m-to-inform-decision-making/

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