Having machine learning in a company’s portfolio used to be an investor magnet. Now, the market is bullish on MLaaS, with a new breed of companies offering machine learning services (libraries/APIs/frameworks) to help other companies get their job done better and faster. 

According to PwC, AI’s potential global economic impact will be worth $15.7 trillion by 2030. And, as interests slowly shift towards MLOps, it is possible that these companies, which promise to scale and accelerate ML deployment, might grab a bigger piece of the pie. Last week, OctoML raised $28 million. The Seattle-based startup offers a machine learning acceleration platform built on top of the open-source Apache TVM compiler framework project. The $28 million Series B funding brings the company’s total funding to $47 million. 

For OctoML’s CEO, Luis Ceze, there is still a significant gap between building a model and making it production-ready. Between rapidly evolving ML models, wrote Ceze in a blog post, ML frameworks and a Cambrian explosion of hardware backends makes ML deployment challenging. “It is not easy to make sure your model runs fast enough and to benchmark it across different deployment hardware. Even if your determined machine learning team has hurtled through this gauntlet, they still have to go through a whole different set of challenges to package and deploy at the edge,” explained Ceze.

A good performance in ML models requires long hours of manual optimizations. These long hours will then translate into hefty cloud bills. Added to this is the model packaging which varies with devices and platforms. According to Ceze, there are no modern CI/CD integrations to keep up with model changes.

“What good is an ML model if it isn’t fast? doesn’t scale? isn’t accurate enough? takes weeks to deploy? and costs too much?,” questioned Ceze as he made a case for OctoML. 

OctoML addressed these pain points with their open-source machine learning compiler framework Apache TV, which according to the team, has quickly become the go-to solution for developers and ML engineers to maximize ML model performance on any hardware backend. “With OctoML we are establishing the first Machine Learning Acceleration Platform that will automatically maximize model performance while enabling seamless deployment on any hardware, cloud provider, or edge devices,” said Ceze.

Be it MLOps or XOps, these services are designed to ease the developers of technical debt that these mega ML models accumulate with changing complexities. Apart from OctoML, there are a few other startups that have succeeded in convincing the investors. Let’s take a look at couple of them:

Verta 

Funding till date: $10 million

The team at Verta is building software for data science teams to address the problem of model management — how to track, version, and audit models used across products. Verta MLOps software supports model development, deployment, operations, monitoring, and collaboration enabling data scientists to manage models across their lifecycle. So far, the company has $10 million in funding and it promises to make robust, scalable, mature deployable models a reality.

“We’re obsessed with helping organizations get ML models into production because that’s the only way they can generate business value,” said the team at Algorithmia. Their enterprise MLOps platform manages all stages of the production ML lifecycle within existing operational processes, so you can put models into production quickly, securely, and cost-effectively. Unlike inefficient and expensive do-it-yourself MLOps management solutions that lock users into specific technology stacks, Algorithmia automates ML deployment, optimizes collaboration between operations and development, leverages existing SDLC and CI/CD systems, and provides advanced security and governance.

Today Algorithmia’s services are used by over 130,000 engineers and data scientists, including the United Nations, government intelligence agencies, and Fortune 500 companies.

“It’s [MLOps] going to be an essential component to enterprises industrializing their AI efforts in the future,” said Diego M. Oppenheimer, Algorithmia’s CEO in a recent interview with GitHub.

Databand.ai

Funding: $14.5 million

Databand brings in the similar flavor into the ML ecosystem. The team Databand is trying to solve the problems that arise due to increasing data workloads. The company founded by Josh Benamram, Victor Shafran and Evgeny Shulmanhelps helps data engineering teams catch data pipeline issues and trace the impact of those problems across end-to-end data flows. Databand’s platform includes an application for visualizing pipeline metadata, and an open source library for integrating with your Python, Java, Scala, or SQL data processes. Data pipeline monitoring is a key aspect of machine learning deployment. We can clearly see how targeting even a niche aspect of the whole ML deployment can land big investors.

Modern day software companies are in the process of or have already embraced machine learning as a key tool. Now they are at a crucial juncture where they can either leverage the MLOps services offered by these startups or build everything on their own. But, there are not many reasons why an organization looking to transition to ML will take the pain of MLOps. As companies look to leverage ML minus the deployment headache, niche players like OctoML will  continue to pop up. Even the latest Gartner survey lists scalability and acceleration of machine learning deployment as two driving forces that will continue to trend this year. According to Gartner, XOps— a variant of MLOps that deals with efficiencies in data, machine learning, model, platform will try to implement best DevOps practices and ensure reliability, reusability and repeatability. 

The post Machine Learning Deployment Is The Biggest Tech Trend In 2021 appeared first on Artificial Intelligence.

We all know the famous quote — “The future belongs to the curious,” and Siddharth Maheshwari, the director of Amity Future Academy, believes it holds true for the field of data science as well. 

Amity Future Academy offers numerous courses in the field of data science, machine learning, artificial intelligence and business analytics in collaboration with global universities like eCornell, Wharton Online, UC Berkeley Online, to name a few. The university has faculties with more than 20 years of industry and mentoring experience who provide one-on-one mentoring to students. Further, Amity teaches in-demand tools and techniques like Tableau, big data and Power BI in addition to machine learning algorithms.

For this week’s interview, Analytics India Magazine got in touch with Siddharth to understand his views on the emerging data science market and how data science aspirants can embark on their journey.

An alumnus of IIM Bangalore, Siddharth is currently working as the director at Amity Future Academy, heading the emerging technology training portfolio in data, analytics, AI, cybersecurity, blockchain, product development and digital marketing. He is also the author of The Billion Minds – India’s Quest to Train a Billion Workforce. BM India has also enlisted Siddharth on the Edtech 40 under 40 list. 

AIM: What are your views on the data science education market in India?

Siddharth Maheshwari: We know that India is considered an agro-based economy, but the key contributor to the GDP has always been the service sector. And the service sector’s driving force in India is education. There are around 3.66 crores of graduates every year, as per the statistical review, and the numbers increase considerably every year. So, there is a vast gap in demand and supply if we look at the graduates and the industry’s demand. 

With rising complexity and competition, the problems need to be optimised with modern-day solutions and higher-level statistical algorithms. Analytical courses in data science, machine learning, and business analytics can provide us with solutions to solve such problems with highly advanced algorithms, real-time outputs and considerable insights. This, in turn, bridges the gap between graduates and the industry, thus giving a huge market for education institutes in the field of data science. 

In the last decade, there has been a substantial rise in student enrolment and work requirements in the field of data science in India compared to the global economy. Amity Future Academy has exclusive batches devoted to students abroad, and a lot of students are enrolling and improving their present skills.

AIM: What are the most significant challenges a university faces in instrumenting a data science and analytics course?

Siddharth Maheshwari: The most critical challenge for a university to instrument a data science and analytics course is to create a skilled and experienced faculty pool that can train the professionals and students on the latest tools and technologies along with the wisdom of industry expertise. 

In our data science and analytics courses, we make sure that we onboard only the faculties passionate and experienced in the analytics industry. We believe in the famous quote: “teaching is not a profession; it’s a passion.” And therefore, without passion for your subject and a desire for your students to learn, we will fail as a teacher and failure is never an option.

AIM: Do you think the COVID pandemic has pushed more people into data science courses?

Siddharth Maheshwari: Yes, I believe the COVID pandemic has massively surged the demand for data science courses. Due to the regulations placed to fight against the COVID pandemic, we have experienced a tectonic shift in every employee’s working habits as we moved from our offices to work from home culture. With traditional data architecture and models, it becomes difficult for organisations to take care of data and make decisions effectively. Hence, enterprises have realised the necessity for solutions to access and manage an outsized volume of knowledge. Thus, it created more opportunities for data scientists who specialised in data-driven objectives to realise data insights and pushed people to seek more data science courses.

AIM: How can governments and corporations encourage more students to select data science subjects?

Siddharth Maheshwari: To encourage more students, the government and corporations can create targeted outreach efforts, exposing students to STEM occupations before they take their first step of choosing math or science courses. It will assist them in learning important information about STEM majors and the way they translate to careers.

Alongside, they should provide all requisites and help colleges to create clear pathways for data science students to easily transition from high school, junior colleges to undergraduate/bachelor’s degree, which can result in a better response to the field.

AIM: What are the major obstacles plaguing data and analytics education in India?

Siddharth Maheshwari: The biggest barrier to data and analytics education in India is the lack of awareness of the programs. There is no knowledge of such programs’ presence and effectiveness to a substantial chunk of our population. The course fee is yet another consideration, as these courses are a little heavy on a middle-class individual’s pockets. So, for those who are not only genuinely interested but also willing to pave their path, we have come up with a few scholarships that eliminate this obstacle. 

In addition to these, other factors include the lack of faculty expertise.

AIM: What advice would you like to give to aspiring data scientists?

Siddharth Maheshwari: The aspiring data scientists should look for the problems they might be surrounded by and how they can use their knowledge to solve those problems. Hence, problems build curiosity and make people know more about the field, which serve as a driving force to start their learning journey.

To begin with data science, one must brush up their knowledge of linear algebra, the basics of statistics and probability. I also want to add that do not get demotivated by the programming languages and the AI-driven smart tools that candidates will have to learn to be a data scientist or for similar roles.

AIM: What is your projection of the growth of AI and big data analytics education in the future?

Siddharth Maheshwari: Looking at the heights of the technological innovations that we humans have achieved in the past few decades for improving the quality of human life by implementing the advancing technologies in day to day life — from driverless cars to space travel and now by exploring Mars, it is clear that the AI and machine learning industry has an excellent future.

Talking about big data, the industry is expected to grow twice in the near future, creating many lucrative opportunities for the people interested in the field.

AIM: Who should seek a career in data science? What does the future look like for someone who takes it up now?

Siddharth Maheshwari: As data science is not restricted to any particular domain, the one interested in dealing with the data and already working in data analytics, business analytics, or in database management systems and services should seek a career in data science.

According to the estimates, there is a shortage of data science professionals worldwide. In the coming years, with the growing amount of data on a daily basis coming from different fields and the data produced from IoT, there will be millions of job openings in the field.

In Amity, we provide a wide variety of toolkits and hands-on sessions to make the students job-ready and equip them with the knowledge to tackle any challenges arising in the projects they work for.

AIM: With many online courses and MOOCs available for data science enthusiasts, how does a professional degree make a difference in their career in the long term?

Siddharth Maheshwari: From anyone starting from scratch, MOOCs can offer a general idea of starting the journey and what to expect from the industry. However, a professional degree ensures that the candidate has gone through rigorous supervised training under mentors and industry experts’ close guidance. 

In most cases, a MOOC certification might not add that value to the career profile compared to a professional degree, as the monitoring and evaluation are seldom taken care of in MOOCs. Also, placement training is uninsured in MOOCs compared to a degree from a certified and well-collaborated university. A professional degree helps build credibility and ensures that the industry standards were met in the long run.

On the other hand, we moderate student’s progress from time to time and help them take over the challenges in the course. In order to ensure that students are aware of the industry issues, we hold weekly webinars and guest lectures with industry experts, where they can connect with the subject experts one by one. In addition to that, we have a dedicated placement team to help with interview preparation and support them with placements.

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Twitter and Facebook use algorithms to ease the burden of human moderators. But, how good are these algorithms?

“Freedom of expression is not absolute and it is subject to reasonable restrictions.”

MeitY, citing Article 19 (2)

Earlier this month, the Government of India reprimanded Twitter for allowing fake, unverified, anonymous and automated bot accounts to be operated on its platform. The Secretary of MeitY raised doubts about the platform’s commitment to transparency and healthy conversation on this platform. The way Twitter and Facebook handled the events leading upto the elections in the US and the aftermath, has served as a wake up call to the governments around the world. Heads of states like France and Germany openly opposed the “problematic” nature of these platforms. As the debate around privacy and freedom of speech grows stronger, individuals and nations are looking out for better, more transparent alternatives.

The differential treatment in practising censorship on these platforms has been exposed quite often. Recently, Twitter and the Indian Government had a brief stand-off during the farmers protests. In a strong worded statement, the government rebuked Twitter for showing differential treatment to what happened at the Red Fort in comparison to its action during the recent Capitol Hill protests. Twitter caved in. The platform banned few accounts under the guidance of MeitY. 

MeitY in a recent meeting with Twitter executives said that the social media company is free to formulate its own rules and guidelines like any other business entity does, but the  laws which are enacted by the Parliament of India “must be followed” irrespective of Twitter’s own rules and guidelines.

A private entity with power to silence the local elements in a foreign land is problematic in my ways. This is the reason why nations across the world root for their own spin-offs of these popular platforms. Indian app makers have responded whenever there is a radical push for digitisation: The demonetisation propelled Paytm, and the ban of Chinese apps birthed half a dozen startups. Koo is the latest entrant to the club: The Twitter alternative was launched 10 months ago and is the winner of the Aatmanirbhar App Challenge organised by the Indian government. The app raked in nearly a million users in the second week of this month. Parler is a good study in this regard. Launched in 2018, Parler was supposed to be the free speech alternative to Twitter. But the company’s lacklustre content moderation allowed radicals from all walks of life to disrupt conversations on the platform. Google and Apple disowned the app. AWS kicked them out of their servers and the app disappeared overnight.

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