Acceldata: Emerging Leader in Data Observability

We are delighted to announce our recent investment into Acceldata, a highly differentiated data observability solution for the modern enterprise.

You’ve heard the aphorism “data is the new oil”.  It highlights the importance of data in today’s digital economy where enterprises are investing billions in capturing data across every touchpoint of customer interaction to enhance customer experience and improve business operations. To be useful and relied on, the data must be of high quality and integrity, and data teams need to be able observe and diagnose data pipelines to identify and remediate poor quality data.  Compounding the problem of the exponential growth of data and multiple repositories, is the move by most enterprises into hybrid data environments, where part of their workloads are on-prem and part in the cloud.  This has resulted in errors, complexity in data pipelines and bloated data costs for organizations.

Acceldata has developed a Data Observability Cloud platform which provides CIO’s and engineers visibility into the compute layer, data pipelines and data infrastructure. This multi-dimensional visibility enables customers to correlate signals from all layers to provide more meaningful insights for data products. The solution improves data system reliability, optimizes engineering productivity, and expedites cloud migration and data validation.  It additionally allows organizations to better monitor and manage their ever escalating data costs. To date, these capabilities have been unattainable by single-dimensional / monitoring solutions and home-grown alternatives.

Data Observability is an important issue for CIO’s building data driven products. It is a top 5 spend priority and constitutes about 5% of total enterprise data spend which we estimate will increase to 15% in the next few years constituting a massive addressable market for the company.

Founders Rohit Choudhary and Ashwin Rajeeva have built an impressive team with expertise in the data observability category. They understand the pain points of data teams intricately, having gone through that journey themselves at Hortonworks in their previous roles. Their domain depth and sharp focus on customer pain points has enabled them to build a highly differentiated offering and to become trusted partners for the data ops needs of their customers.

The unanimously positive feedback on team, platform need and time to value that we got from their customers, most of whom are large enterprises with complex data stacks, gives us confidence that Acceldata is well positioned to be the Data Observability leader in the coming decade.

We are very excited to have a front row seat in this journey!

Recession-Proofing your Software Revenue - Time to Value is Critical

Delivering proof of value (the ROI your client is making by licensing your software) has been a core focus for software CEOs. What is often overlooked is how fast the client can start to experience this value. The software industry has a wide range of ‘time to value’ (TTV): from complex integrations like SAP that can take years, to product-led growth software where clients jump right in to leveraging the solutions immediately. Are you happy with your company’s TTV? What product changes do you need to make to improve TTV in the next 6 months?

A quick time-to-value is crucial for a strong customer success strategy. Companies that can deliver value quickly:

  • Enable quicker sales processes
  • Improve customer success and experience
  • Experience better customer retention
  • Drive greater scalability

Time-to-value is the amount of time it takes for customers to start seeing value from your product – how quickly your product can deliver on its promises. Customers have limited resources to allocate to tools and initiatives so the faster you can show value, the better the customer experience.  And in recessionary times, proof of impact is critically important.

While it may sound simple, TTV is widely overlooked in SaaS as business leaders fail to recognize the impact it has on product adoption and revenue growth. The primary culprit of slow TTV is long onboarding processes. This can happen if products require custom integrations, buy-in from multiple stakeholders, significant changes to customer workflows, or extensive training. These are not always top of mind when developing products because products are built to be sold – we claim success when we win the customer. Through this perspective, a long onboarding process is just an inevitable requirement to enable ROI delivery. Instead, success should be when the customer wins. Actualizing the value of your product quickly should be a key priority for your company because it is a key priority for your customer.

Prior to joining March Capital, I led MarketShare, an analytics software company I co-founded. We were right in the middle of the aforementioned spectrum – getting the data out of the clients’ various repositories and into our analytics engine was always the weak link in time to value. Some companies are simply better organized with their data than others.  We invested heavily in innovative solutions to streamline that process so time to value was closer to 3 months than the original 9 months it would often take. Also, we created a ‘light’ software offering called MarketShare Benchmark that would provide immediate and actionable insights using lookalike variables, derived algorithmically through user-generated answers to a series of questions regarding their goals, business parameters, competitive landscape and the like. Those results, and the time to value, were nearly immediate and this became a fast-growing product line for the company.

At March Capital, we see this customer-centric TTV approach as a leading indicator of revenue growth and scalability because it reduces sales friction and improves customer retention time and time again.

Offering quick TTV is incredibly impactful in sales processes. Customers are inundated with a slew of tools claiming to solve problems for their business. The quicker your product can solve their problems, the easier their decision becomes. This is especially true as budgets tighten. We increasingly look for companies that can show immediate or near-immediate value. Being able to communicate this makes it much easier to win new deals.  Indeed, one of my board companies has begun offering a guaranteed business case – if they don’t hit the target revenue impact in a defined period of time after signing contracts, the client doesn’t pay.  The need to have a fast TTV for this company is obvious and it is working. And this has led to a 30 day deployment- substantially faster than before.

Once you do win the customer, the more quickly you can make them a happy evangelizer, the more successful you will be at keeping (and growing) them. Slow TTV lowers conversion rates, creates frustration, and ultimately drives churn.

Sales efficiency and retention benefits of quick TTV help drive higher valuations

Immuta, a leading data access control platform and March portfolio company, does an exceptional job of fast TTV. By streamlining workflows and implementing automation in the onboarding process, Immuta can show time to value quicker than alternative solutions. This has been a key ingredient to their success. Across our portfolio, companies are working on reducing sales friction and streamlining onboarding processes where possible to support expansion and customer success strategies amidst tightening IT budgets.

How long does it take for customers to become promoters? What processes take place between sale and value generation? Which of those steps can be eliminated by better product features? How many clicks does it take for the customer to be impressed? Are you investing in accelerating TTV?  Your competitors most likely are.  These important questions should be top of mind to develop great products that can create value for customers quickly and fuel scalable success. 

Parallel Domain: Why Synthetic Data is Critical to the Future of ML

A driver assistance system stops a car when a child unexpectedly runs into the road. A drone drops a package on the doorstep of a customer. A robot picks the shirt you ordered online off a warehouse shelf and packs it for shipping.

How does the car recognize the child? Why does the drone drop the package on your porch and not in your swimming pool? How does the robot select the exact shirt you ordered?

These feats are all made possible by computer vision machine learning (ML) models and the fuel for these models is data.

Machine learning models ingest tremendous amounts of data, which enables them to replicate human performance. Models need sufficient high-quality data to unlock their potential.

At March, we are excited to announce our investment in Parallel Domain, a company that is providing ML developers with an on-demand data generation platform to train and test computer vision models 180x faster than today’s methods.

Obtaining real world data needed to train computer vision models is a significant obstacle to their successful deployment. Generating the necessary volume of high-quality training data is capital and time intensive – enterprise ML projects allocate up to 50% of budget just for data acquisition.

Once the data is collected, the task of sorting and labeling is time-consuming, costly, and error prone. Real data is also often unbalanced, and biased – there are too few examples of the objects and situations the company needs in the data – leading to poor ML performance. This performance can have, as in the example of a child unexpectedly in the road, tremendously high and sometimes even life or death stakes.

Parallel Domain solves this data problem by generating synthetic sensor data (camera, lidar, and radar) at scale and without human intervention in an elastic data cloud, to supplement or even replace real data.

Gartner predicts that by 2024, 60% of the data used for the development of AI and analytics projects will be synthetically generated. This makes the potential synthetic data market worth an estimated $57B by 2027.

Parallel Domain is well positioned to be a market leader in synthetic data for computer vision due to its proprietary procedural generation technology, ever-expanding asset catalog, strong value-proposition to customers, and experienced team.

The company is led by CEO Kevin McNamara. Kevin is supported by an incredibly talented leadership team that has pioneered synthetic data, simulation, and computer graphics at Apple, Tesla, Pixar, EA, NVIDIA, and other world-class organizations.

March Capital is impressed with the company’s strong customer value proposition and traction with leading players in the mobility space, such as Toyota Research Institute, Google, and Continental. We are thrilled to lead the company’s $30 million Series B financing and look forward to working alongside the Parallel Domain team to accelerate the company’s growth and success.

To learn more about Parallel Domain, visit

Accelerating Revenue with Customer Data and Automation: Emerging Technology Stacks

Amidst growing data volumes and an impending cookie-less world, Customer Data Platforms (CDPs) have emerged as the most comprehensive data solution for the end-to-end customer journey and a mission critical tool of the enterprise technology stack. However, when trying to build a customer data strategy, IT teams are inundated by a slew of tools and acronyms. What is a CDP? What are the different types of CDPs? What about my Data Management Platform (DMP) and Customer Relationship Management (CRM) tools? Where do cloud data warehouses like Snowflake and Databricks fit into all of this? To better monetize your customers through data and analytics, it is important to understand what CDPs actually are and the role these tools should play in your technology stack.

CRMs, DMPs, and the birth of CDP

To understand CDPs and the broader customer data tools landscape today, it is helpful to know its history. The first ancestors to CDP appeared when customer Rolodexes were digitized in the late 1970s as businesses began putting customer data into spreadsheets. These evolved into basic customer databases by the 1980s and 1990s, and by the turn of the millennium, moved into the cloud with Salesforce.

Simultaneously, the Internet brought about online advertising, which generated massive amounts of data for marketers to manage. This led to the aforementioned DMPs, a data warehouse designed to store and analyze third-party (cookie-based and anonymized) data. DMPs allow marketing teams to build highly granular customer segments. Companies like Snowflake and Databricks have since emerged as highly performant, flexible, and scalable evolutions of data warehousing.

In 2013, the CDP was first defined and developed with the purpose of synthesizing first-party customer data and combining it with the volumes of data generated by customers’ online activity. The CDP provided enterprises a holistic source of truth on their customers and thus an unmatched ability to optimize customer experience and targeted communications. DMPs, on the other hand, warehoused anonymized third-party customer data gathered from external sources. While CDPs also hosted third-party data, DMPs were better equipped to gather, analyze, and action data on potential customers who had yet to engage with the company. Where CDPs specialized on the existing customer, DMPs focused on new customers.

CDP 2.0 and the displacement of the DMP

Developments in machine learning technology and metadata management gave way to “CDP 2.0.” Concurrently, the deprecation of third-party cookies by Google and Apple have diminished the value of DMPs. While these tools were previously used in conjunction, going forward, we anticipate DMPs will be subsumed by CDPs. 

To summarize: CDP 1.0 allowed enterprises to know who their customers are; CDP 2.0 shows us how customers behave. Many companies call themselves CDPs but are ultimately providing the original use case of CDPs without the valuable analytical customer journey offering.

Beyond just aggregating and managing a customer profile, true CDPs today can scale to collect and analyze detailed, event-level behavioral data – both known and anonymous – and thereby map the customer journey that informs how, when, and where to target customers for optimal engagement. 

The tremendous value of event-level behavioral information lies in its ability to answer questions like: what are the most recent emails a customer opened, the last five SKUs they bought online, the last three calls they had with the call center? Advancements in data management and computing capabilities have given CDPs the user journeying superpower to create robust predictive persona strategies. 

Concurrently, the “cookie apocalypse” continues to evolve, as Google announced their decision to remove cookies from its Chrome browser by 2024. Third-party cookies are the lifeblood of the DMP.

Enterprises will now have to make a decision – struggle to manage rising acquisition costs, or focus on driving customer lifetime value. The answer is clear. Understanding the customer, how they engage with the brand, and the journey they take from acquisition to retention will define success going forward. As marketing resources are increasingly deployed to drive lifetime value of customers (LTV), the CDP has emerged as a mission critical business tool for the enterprise at the expense of the DMP. 

CDP of the future 

The future of CDPs is Composable Architecture. Composable Architecture, as opposed to bundled CDPs, allows the enterprise to decide where their customer data lives. This has incredible benefits for data control, flexibility, and cost. 

The norm historically has been Bundled CDP architecture, requiring teams to purchase not only the CDP’s analytical and visualization layers, but also the data storage layer. This requires data duplication, thus increasing cost and complexity and creating friction with IT teams who usually drive for a single data store for better efficiency and governance. 

For instance, one of the companies I’m on the board of, ActionIQ, has made impressive headways here with its leading HybridCompute technology. HybridCompute allows clients to run ActionIQ on their existing data warehouses, data lakes, or data lakehouses, without creating a parallel data platform. This has proven to be a strong selling point for CIOs as it drives performance, efficiency, and data security.

As the ecosystem of customer data management begins to mature, what originally sounded like alphabet soup is becoming more clear. CDP 2.0s, including Composable CDPs like ActionIQ, are emerging as the solution that can finally capture the full end-to-end customer journey in unparalleled detail – and drive higher sales, higher NPS scores and higher LTV.

The Future of How We Live, Work, and Pay

Over the past 2 years COVID has radically transformed nearly every aspect of our lives. However, even before we entered a global pandemic, we were seeing rapid change in housing, the workplace, and finance driven by a more pervasive, though less potent, catalyst, technology. In The Future of How We Live, Work, and Pay panel, I discussed this impact with three entrepreneurs whose companies are at the forefront of this transformation, Nora Apsel, Founder and CEO of Morty, Maria Colacurcio, CEO of, and Ema Rouf, Co-Founder of Pave. Our conversation touched on the lessons of the past two years, with an eye to applying them successfully over the next two years.

The constraints of the past two years have driven digitalization forward in a powerful way. Consumers have become increasingly comfortable with working, shopping and investing online. The digital footprint created by this behavior, coupled with the willingness of more than 10 million Americans to share their data with providers they trust, is creating a paradigm shift in how goods and services are delivered.

Companies are acquiring more data than ever before, and the most successful ones are utilizing this data to offer a high level of personalization to consumers. According to Apsel, “Morty is using this data to discover the best mortgage rate and package for each of its customers, simplifying the home buying process”. Similarly, Pave is helping fintech companies make use of vast amounts of financial data to build personalized financial experiences. This data is also bringing to light critical societal challenges, including inequities in the workforce that is addressing through its workplace equity platform.

Today, we are at a similar inflection point as in 2020, one characterized by uncertainty in the financial markets, geopolitical unrest, and societal adjustment to a post-COVID world. This pivotal moment presents challenges but also opportunities, as highlighted by all three panelists. According to the panelists, key themes for companies across sectors over the next few years will be personalization, transparency, and creating long-term value for both customers and employees.

Rouf views personalization as an ongoing theme for the next several years. “People want instantaneous processes. Things have to be digital, accessible through the phone and personalized. Consumer segments are becoming more and more niche but the next generation of Fintech companies will effectively utilize data to provide service in a way that traditional FSI’s have failed to do.”

Colacurcio emphasizes the growing importance of transparency across sectors, asserting that, “rather than employing a ‘last in, first out’ approach, successful companies will conduct data-driven analysis of employee value and performance.” Apsel and Rouf agree that increased transparency will have an outsized impact in the future of the housing and financial services industries respectively. Rouf goes so far as to predict that “the future of credit models will be open source, with communities of data scientists and developers creating transparency and democratizing [credit] evaluation.”

A final prediction made by all three panelists is growth in the importance of creating value for employees. Colacurcio embraces her own rhetoric at, utilizing a structured and data-driven pay process. “Pay is just a proxy for opportunity,” according to Colacurcio and with new legislation requiring companies with more than 100 employees to post salary ranges on job requirements, she views pay equity as the first step toward greater workplace equity.

Pave is a remote-native company and Apsel offers Morty’s employees flexibility with a hybrid work environment. Both agree that, while distributed workforces are challenging, it is increasingly possible to manage given technological advances and the benefits to employees are worth the adjustment.

As the world emerges from isolation, a new technology-driven normal is emerging in multiple facets of life. To be successful over the next few years, companies will need to embrace the opportunities and face the challenges catalyzed by technology in how we live, work, and pay.

Watch the full session from The Montgomery Summit 2022 here.


Future of Healthcare x AI

Future of Healthcare x AI Panel

 How do we make sense of the development and delivery of healthcare today? Due to advancements in the modern age, we are living much longer than previous generations. As the number of patients grow, healthcare faces huge problems with too few doctors to treat them. Artificial Intelligence, or AI, can help resolve some of these difficulties by offering advances that range from more efficient diagnoses to safer treatments.

We are also seeing potential cures and therapeutics for our biggest healthcare challenges getting closer to cures thanks to development of AI. AI is being used to rapidly and effectively explore different protein combinations and likely side effects our outcomes. We are also seeing technological solutions that can actually change the ‘software’ of our genetics rather than simply cut and edit them.

Moderated by Wes Nichols, Partner at March Capital, this special panel will examine how we are seeing AI transform the healthcare industry today and beyond.

Punit Singh Soni is the Founder and CEO of Suki, an AI-powered, voice-enabled digital assistant for doctors that enables its users to be more productive while solving some of the biggest problems in healthcare. Hear from Soni how Suki helps physicians cut their paperwork burden by 70% while driving double-digit increases in revenue by getting the billing codes right.

Geoffrey von Maltzahn, General Partner at Flagship Pioneering and CEO of Tessera Therapeutics, is an inventor, entrepreneur, and the co-founder of multiple groundbreaking companies in healthcare. He has deep expertise in genome engineering, the microbiome, bioengineering, and nanotechnology. Join us in this panel to learn about his focus on inventing technologies and starting companies to address global challenges in medicine and environmental sustainability, including the breakthrough technology of Tessera as well as his other company, Generate Biomedicines.

With over 25 years of experience at Amgen Research, Executive Director, Dr. Peter Grandsard is responsible for the chemical, biophysical, and physical characterization of research-stage therapeutic candidates, synthetics and biologics alike. Grandsard has managed Amgen functions in bio-assay development and screening; small molecule process analytical sciences; and lab automation tech. Given his role, it is no surprise that he follows evolution in instrumentation and information technologies very closely, with more and more focus on machine learning for in-silico prediction of attributes. Hear from Grandsard on how AI and machine learning offer the potential to reduce the time and expense of clinical trials, how exactly AI is transforming clinical trials, and what this will mean for pharma and biotech startups in the US and internationally.

Learning More About The Montgomery Summit 2022

We will be hosting The Montgomery Summit presented by March Capital on Tuesday, May 24th and Wednesday, May 25th at the Fairmont Miramar Hotel & Bungalows in Santa Monica, CA. 

Last March, we hosted our first-ever virtual Summit with over 4,000 attendees and 250 speakers (including 100+ innovative private companies) tuning in from around the world virtually. We are excited to be back in person for our 19th annual Summit and to take advantage of the near-perfect Santa Monica weather.

For almost two decades, The Montgomery Summit has convened leading entrepreneurs, technology executives, investors, and thought leaders to share insights, to encourage innovation, and to challenge the status quo. Over the past two years, the continued rapid digital transformation of the global economy has created unprecedented disruption, investment opportunities, and wealth— now it is a good time to convene and evaluate where we are and what the future holds.

At any time, Montgomery Summit attendees can enjoy one of seven parallel sessions during the day and a half of programming—featuring stellar keynote speakers, panel sessions on creativity, leadership, and innovation, and dynamic private company presentations—that will captivate you for two days.

Keynotes and Notable Speakers

The Montgomery Summit consistently features global leaders and influencers as keynote speakers. This year, Summit guests will hear from luminaries who continue to drive significant and impactful innovation in technology, finance, and leadership:

  • George Kurtz, President/CEO & Co-Founder, CrowdStrike
  • René Lacerte, Founder & CEO,
  • Dr. Ronald, D. Sugar, Board Director, Apple, Uber, Chevron, and Amgen
  • Roxanne Austin, President, Austin Investment Advisors
  • John Chambers, Founder & CEO, JC2 Ventures
  • Spencer Rascoff, Co-founder & Chairman: Pacaso, Recon Food, dot.LA, Supernova
  • Michael Milken, Chairman, Milken Institute
  • Blythe Masters, Founding Partner, Motive Partners
  • Girish Mathrubootham, CEO & Founder, Freshworks

Panels with Industry Leaders

The Summit will also offer a series of panel discussions featuring executives from Nasdaq, Amgen, Wells Fargo, Honeywell, SiriusXM, NVIDIA, and Netflix. We have reinvigorated the layout of the 2022 Summit so that most of our programming will be hosted outdoors.

Session topics include:

  • Tech Priorities for 2022
  • AI, Supercomputing, & Quantum: Critical Technologies Reshaping National Priorities
  • The Changing Environment for Liquidity
  • Future of Healthcare x AI

Sessions featuring Leading Private Companies

The 2022 Summit will feature Innovation Forums presented by March Capital that will focus on 3 to 4 leading companies from a particular sector moderated by my Partners at March Capital and other industry experts. We started these sector-focused panel discussions for our virtual Summit in 2021 and are excited to continue this new element of programming to our in-person Summit this year.

Discussion topics include:

  • Payments — The Gateway to Data Visibility featuring Gr4vy, Fidel, and Pagos
  • Ecommerce Infrastructure & Automation featuring Alloy Automation, Rally, Tradeswell, and Violet
  • The Modern Data Stack featuring Acceldata, Immuta, SingleStore, and ThoughtSpot
  • Building a Cyber Platform featuring JumpCloud and Coalition

The Summit will also highlight several transformative market-leading companies in a fireside chat format with an expert moderator. These spotlight companies include Firebolt, Human Interest, Qumulo, SparkCognition, TaxBit, ThoughtSpot, Uniphore, and Unqork. The Summit will also feature 25-minute presentations from emerging and growth stage private companies. View the complete list of presenting companies on our website.

World-Class Hospitality and Networking

We are excited to host an unforgettable private sunset reception for our registered guests on Tuesday, May 24th at the iconic Getty Villa in Malibu. The Getty Villa will be featuring the exhibition Persia: Ancient Iran and the Classical Worldthe first major U.S. exhibition to highlight the relationship between the Classical World and Ancient Iran. Guests will be transported to the Villa from the Fairmont Miramar in Santa Monica.

From our reception at the Getty Villa to the In-N-Out Burger truck to numerous small and larger events around the Summit—our hospitality is renowned. We will conclude the Summit with a reception at The Bungalow featuring a DJ set from Forester where our guests can enjoy the Santa Monica breeze and views of the Pacific Ocean.

I hope to see you in Santa Monica next week! To register for the 2022 Summit, please click the following link: . If you have any questions or would like to request an invitation, please contact


Today, we are thrilled to share that Brad Weirick is joining us as March Capital’s newest partner and first general counsel.

Brad has over three decades of experience advising investment firms and companies on public and private M&A transactions and securities offerings following a distinguished career as a partner at Gibson, Dunn & Crutcher in Los Angeles. His wealth of technology and VC industry expertise is unmatched – as is his deep knowledge of M&A, securities offerings and corporate governance.

I have worked closely with Brad for over two decades, and I know that having him as a part of the March team will not only be a great value to us but also to our investors and entrepreneurs. He has always been a wise counsel and confidante to me, to my partners, and to our CEOs. As we continue to build a world-class technology investment platform and to back the fastest-growing companies in the innovation economy across all stages of their growth journey, Brad will be an incredible and invaluable strategic and legal resource.

The technology sector continues to rapidly evolve, creating a robust pipeline of opportunities for us and making our commitment to supporting our portfolio companies through value-added resources and guidance all the more critical to their growth. We look forward to enhancing our capabilities with respect to corporate governance, compliance, deal structuring and deal execution, ensuring that we are best-in-class on all fronts, while helping our portfolio companies achieve their full potential with Brad on our team.

We are excited for Brad to join us as we continue to partner with exceptional entrepreneurs leading the future and as we help scale their companies globally.

Welcome to March, Brad!

Building a Platform Around Cyber

How do executives make sense of 1,000+ cybersecurity companies? We know that every organization has a limited number of resources in which to implement and monitor its cybersecurity posture. Recently, platforms are emerging that simplify cybersecurity, potentially shifting the market away from a collection of “best-of-breed” point solutions. The question in the minds of investors and entrepreneurs alike – what does it take build one?

Moderated by Jed Leidheiser, Partner at March Capital, this special panel will examine the core principles and tough decisions that are necessary to build a cybersecurity platform, from experts who’ve grown companies into mission-critical platforms, and who aren’t done innovating yet.

Co-founder & CEO of Coalition, Joshua Motta, started out at Microsoft as the company’s youngest hire. He saw cybersecurity as a risk management problem, yet the data and solutions weren’t available to make risk-weighted decisions.  In founding Coalition, his aim was to allow businesses to quantify their cybersecurity risk, and then provide tools to monitor and insure against that risk. Today, Coalition has over 140,000 customers worldwide.

Gur Talpaz leads Corporate Development at CrowdStrike, after being an industry analyst and Managing Director at Stifel. CrowdStrike is now a market leader in cybersecurity, with a platform composed of 22 modules, that processes over 1 trillion events per day.  Hear how CrowdStrike built its own platform and is now giving back to the ecosystem, such as with its third-party marketplace and its Falcon Fund.

JumpCloud founding team member and Chief Strategy Officer, Greg Keller, grew the company by turning “product users” into “product champions.” JumpCloud is the open directory platform for secure access. In a market that many thought would be dominated by Microsoft, JumpCloud has experienced rapid growth by not forcing users into a particular ecosystem.

Together, these speakers will shed light on how to build a true cybersecurity platform, en route to becoming large businesses in their own right.

The Next-Generation of Machine Learning

This past year, the first robots capable of reproduction were created, ransomware attacks on critical utilities across California and Florida were thwarted, and a test to predict Alzheimer’s Disease prior to symptom onset was discovered.

What do these three feats have in common?

They were all made possible by machine learning models. Machine learning (ML) and artificial intelligence (AI) are at the heart of today’s major technological and scientific breakthroughs.

But AI and ML are not solely used for major discovery – they have become a staple of data-driven technology and ubiquitous in our daily lives. ML models show us which products we’ll enjoy from our favorite designer, protect our bank accounts from fraud, and determine which protein combinations will best treat diseases.

These models are also critical to the inner workings of modern companies. In fact, McKinsey reports that more than 56% of enterprises have adopted AI for one or more important use-cases, with the top three being service operation optimization, AI-based enhancement of products, and customer service automation.

Though the majority of companies understand the importance of AI and ML adoption, they often lack the right infrastructure, tools, and talent to get the most out of their models.

First and foremost, data is the fuel on which ML models feed. To derive value from a model, the data on which it is trained and run must be clean and properly formatted. Companies such as Scale, Labelbox, and Vody ensure quality model inputs through proper data labeling, management, and training.

Once a company has completed data prep, it must successfully deploy the model and support it in production. There are several companies in the newly minted “MLOps” space that facilitate faster model deployment, more efficient model scale, and reliable and unbiased model insights.

Bringing models into production is one of the most difficult steps of the ML life cycle – only 10-20% of models succeed. This step can tie up data science talent for months in an attempt to refine model performance. Machine learning acceleration platforms, such as OctoML and Deci use ML themselves to optimize model performance for hardware targets and reduce deployment time.

Once models are deployed, they must be run efficiently and scale effectively. AI optimization and orchestration company Run:ai virtualizes hardware resources to optimally allocate them across ML initiatives, saving customers time and money.

Even when delivered at speed, insights and recommendations are worthless if companies are unable to trust their accuracy. Zillow and Instacart experienced firsthand the pain and financial impact caused by machine learning model drift – when a model’s predictive power degrades due to changes in the environment. The former lost more than $500 million when its house buying model was not adjusted to reflect changing market conditions, while the latter faced significant struggles during the onset of the pandemic when its inventory prediction models dropped from 93% to 61% accuracy.

Beyond the problem of drift, models can also be inherently flawed due to biases in the data used to train them. Goldman Sachs came under scrutiny after models used to determine credit limits for its Apple Card offering were found to be gender biased.

To protect against model drift and bias, companies such as Fiddler, Arthur , and WhyLabs offer solutions for model monitoring and explainability. These solutions offer insight into the “black box” of machine learning by allowing a company to track model performance over time, understand the drivers of model recommendations, and address anomalies.

Join us to learn more about the Next Generation of Machine Learning at this years’ The Montgomery Summit 2022 presented by March Capital on May 24th and 25th, where we will hear from several of the companies mentioned above that are enabling enterprises to uplevel their ML efforts. To register for the 2022 Summit, please click the following link: If you have any questions, please contact