21.4 C
Wednesday, September 22, 2021

2021 Data/AI Salary Survey

In June 2021, we requested the recipients of our Data & AI Newsletter to answer a survey about compensation. The outcomes gave us perception into what our subscribers are paid, the place they’re positioned, what industries they work for, what their issues are, and what kinds of profession growth alternatives they’re pursuing.

While it’s sadly untimely to say that the survey passed off on the finish of the COVID-19 pandemic (although we are able to all hope), it passed off at a time when restrictions have been loosening: we have been beginning to exit in public, have events, and in some instances even attend in-person conferences. The outcomes then present a spot to start out fascinated by what impact the pandemic had on employment. There was lots of uncertainty about stability, notably at smaller corporations: Would the corporate’s enterprise mannequin proceed to be efficient? Would your job nonetheless be there in a 12 months? At the identical time, workers have been reluctant to search for new jobs, particularly if they’d require relocating—at the very least based on the rumor mill. Were these issues mirrored in new patterns for employment?

Learn quicker. Dig deeper. See farther.

Executive Summary

  • The common wage for information and AI professionals who responded to the survey was $146,000.
  • The common change in compensation during the last three years was $9,252. This corresponds to an annual improve of two.25%. However, 8% of the correspondents reported decreased compensation, and 18% reported no change.
  • We don’t see proof of a “great resignation.” 22% of respondents stated they supposed to vary jobs, roughly what we might have anticipated. Respondents appeared involved about job safety, most likely due to the pandemic’s impact on the financial system.
  • Average compensation was highest in California ($176,000), adopted by Eastern Seaboard states like New York and Massachusetts.
  • Compensation for ladies was considerably decrease than for males (84%). Salaries have been decrease no matter training or job title. Women have been extra doubtless than males to have superior levels, notably PhDs.
  • Many respondents acquired certifications. Cloud certifications, particularly in AWS and Microsoft Azure, have been most strongly related to wage will increase.
  • Most respondents participated in coaching of some kind. Learning new expertise and bettering previous ones have been the commonest causes for coaching, although hireability and job safety have been additionally components. Company-provided coaching alternatives have been most strongly related to pay will increase.


The survey was publicized by way of O’Reilly’s Data & AI Newsletter and was restricted to respondents within the United States and the United Kingdom. There have been 3,136 legitimate responses, 2,778 from the US and 284 from the UK. This report focuses on the respondents from the US, with solely restricted consideration paid to these from the UK. A small variety of respondents (74) recognized as residents of the US or UK, however their IP addresses indicated that they have been positioned elsewhere. We didn’t use the information from these respondents; in observe, discarding this information had no impact on the outcomes.

Of the two,778 US respondents, 2,225 (81%) recognized as males, and 383 (14%) recognized as ladies (as recognized by their most popular pronouns). 113 (4%) recognized as “other,” and 14 (0.5%) used “they.”

The outcomes are biased by the survey’s recipients (subscribers to O’Reilly’s Data & AI Newsletter). Our viewers is especially robust within the software program (20% of respondents), laptop {hardware} (4%), and laptop safety (2%) industries—over 25% of the overall. Our viewers can also be robust within the states the place these industries are concentrated: 42% of the US respondents lived in California (20%), New York (9%), Massachusetts (6%), and Texas (7%), although these states solely make up 27% of the US inhabitants.

Compensation Basics

The common annual wage for workers who labored in information or AI was $146,000. Most salaries have been between $100,000 and $150,000 yearly (34%); the following commonest wage tier was from $150,000 to $200,000 (26%). Compensation depended strongly on location, with common salaries highest in California ($176,000).

The common wage change over the previous three years was $9,252, which is 2.25% per 12 months (assuming a closing wage equal to the common). A small variety of respondents (8%) reported wage decreases, and 18% reported no change. Economic uncertainty attributable to the pandemic could also be liable for the declines in compensation. 19% reported will increase of $5,000 to $10,000 over that interval; 14% reported will increase of over $25,000. A examine by the IEEE means that the common wage for technical workers elevated 3.6% per 12 months, increased than our respondents indicated.

39% of respondents reported promotions prior to now three years, and 37% reported altering employers throughout that interval. 22% reported that they have been contemplating altering jobs as a result of their salaries hadn’t elevated through the previous 12 months. Is this an indication of what some have known as a “great resignation”? Common knowledge has it that technical workers change jobs each three to 4 years. LinkedIn and Indeed each advocate staying for at the very least three years, although they observe that youthful workers change jobs extra typically. LinkedIn elsewhere states that the annual turnover charge for expertise workers is 13.2%—which means that workers keep at their jobs for roughly seven and a half years. If that’s right, the 37% that modified jobs over three years appears about proper, and the 22% who stated they “intend to leave their job due to a lack of compensation increase” doesn’t appear overly excessive. Keep in thoughts that intent to vary and precise change are usually not the identical—and that there are numerous causes to vary jobs except for wage, together with flexibility round working hours and dealing from house.

64% of the respondents took half in coaching or obtained certifications prior to now 12 months, and 31% reported spending over 100 hours in coaching packages, starting from formal graduate levels to studying weblog posts. As we’ll see later, cloud certifications (particularly in AWS and Microsoft Azure) have been the preferred and appeared to have the biggest impact on salaries.

The causes respondents gave for collaborating in coaching have been surprisingly constant. The overwhelming majority reported that they needed to be taught new expertise (91%) or enhance present expertise (84%). Data and AI professionals are clearly involved in studying—and that studying is self-motivated, not imposed by administration. Relatively few (22%) stated that coaching was required by their job, and even fewer participated in coaching as a result of they have been involved about dropping their job (9%).

However, there have been different motives at work. 56% of our respondents stated that they needed to extend their “job security,” which is at odds with the low quantity who have been involved about dropping their job. And 73% reported that they engaged in coaching or obtained certifications to extend their “hireability,” which can counsel extra concern about job stability than our respondents would admit. The pandemic was a menace to many companies, and workers have been justifiably involved that their job may vanish after a foul pandemic-influenced quarter. A want for elevated hireability can also point out that we’ll see extra folks seeking to change jobs within the close to future.

Finally, 61% of the respondents stated that they participated in coaching or earned certifications as a result of they needed a wage improve or a promotion (“increase in job title/responsibilities”). It isn’t stunning that workers see coaching as a path to promotion—particularly as corporations that need to rent in fields like information science, machine studying, and AI take care of a scarcity of certified workers. Given the issue of hiring experience from exterior, we count on an growing variety of corporations to develop their very own ML and AI expertise internally utilizing coaching packages.

Salaries by Gender

To no person’s shock, our survey confirmed that information science and AI professionals are principally male. The variety of respondents tells the story by itself: solely 14% recognized as ladies, which is decrease than we’d have guessed, although it’s roughly per our convention attendance (again after we had reside conferences) and roughly equal to different technical fields. A small quantity (5%) reported their most popular pronoun as “they” or Other, however this pattern was too small to attract any vital comparisons about compensation.

Women’s salaries have been sharply decrease than males’s salaries, averaging $126,000 yearly, or 84% of the common wage for males ($150,000). That differential held no matter training, as Figure 1 exhibits: the common wage for a girl with a doctorate or grasp’s diploma was 82% of the wage for a person with an equal diploma. The distinction wasn’t fairly as excessive for folks with bachelor’s levels or who have been nonetheless college students, nevertheless it was nonetheless vital: ladies with bachelor’s levels or who have been college students earned 86% or 87% of the common wage for males. The distinction in salaries was best between individuals who have been self-taught: in that case, ladies’s salaries have been 72% of males’s. An affiliate’s diploma was the one diploma for which ladies’s salaries have been increased than males’s.

Figure 1: Women’s and males’s salaries by diploma

Despite the wage differential, a better share of ladies had superior levels than males: 16% of ladies had a doctorate, versus 13% of males. And 47% of ladies had a grasp’s diploma, versus 46% of males. (If these percentages appear excessive, take into account that many professionals in information science and AI are escapees from academia.)

Women’s salaries additionally lagged males’s salaries after we in contrast ladies and men with related job titles (see Figure 2). At the chief stage, the common wage for ladies was $163,000 versus $205,000 for males (a 20% distinction). At the director stage, the distinction was a lot smaller—$180,000 for ladies versus $184,000 for males—and ladies’s salaries have been really increased than these on the govt stage. It’s straightforward to hypothesize about this distinction, however we’re at a loss to clarify it. For managers, ladies’s salaries have been $143,000 versus $154,000 for males (a 7% distinction).

Career development can also be a problem: 18% of the ladies who participated within the survey have been executives or administrators, in contrast with 23% of the boys.

Figure 2. Women’s and males’s salaries by job title

Before shifting on from our consideration of the impact of gender on wage, let’s take a short take a look at how salaries modified over the previous three years. As Figure 3 exhibits, the proportion of women and men respondents who noticed no change was nearly similar (18%). But extra ladies than males noticed their salaries lower (10% versus 7%). Correspondingly, extra males noticed their salaries improve. Women have been additionally extra more likely to have a smaller improve: 24% of ladies had a rise of below $5,000 versus 17% of males. At the excessive finish of the wage spectrum, the distinction between women and men was smaller, although nonetheless not zero: 19% of males noticed their salaries improve by over $20,000, however solely 18% of ladies did. So essentially the most vital variations have been within the midrange. One anomaly stands proud: a barely increased share of ladies than males obtained wage will increase within the $15,000 to $20,000 vary (8% versus 6%).

Figure 3. Change in wage for ladies and men over three years

Salaries by Programming Language

When we checked out the preferred programming languages for information and AI practitioners, we didn’t see any surprises: Python was dominant (61%), adopted by SQL (54%), JavaScript (32%), HTML (29%), Bash (29%), Java (24%), and R (20%). C++, C#, and C have been additional again within the record (12%, 12%, and 11%, respectively).

Discussing the connection between programming languages and wage is hard as a result of respondents have been allowed to verify a number of languages, and most did. But after we seemed on the languages related to the very best salaries, we received a considerably completely different record. The most generally used and fashionable languages, like Python ($150,000), SQL ($144,000), Java ($155,000), and JavaScript ($146,000), have been solidly in the course of the wage vary. The outliers have been Rust, which had the very best common wage (over $180,000), Go ($179,000), and Scala ($178,000). Other much less widespread languages related to excessive salaries have been Erlang, Julia, Swift, and F#. Web languages (HTML, PHP, and CSS) have been on the backside (throughout $135,000). See Figure 4 for the total record.

Figure 4. Salary vs. programming language

How will we clarify this? It’s troublesome to say that information and AI builders who use Rust command a better wage, since most respondents checked a number of languages. But we imagine that this information exhibits one thing vital. The provide of expertise for newer languages like Rust and Go is comparatively small. While there will not be an enormous demand for information scientists who use these languages (but), there’s clearly some demand—and with skilled Go and Rust programmers briefly provide, they command a better wage. Perhaps it’s even easier: whatever the language somebody will use at work, employers interpret information of Rust and Go as an indication of competence and willingness to be taught, which will increase candidates’ worth. An identical argument could be made for Scala, which is the native language for the broadly used Spark platform. Languages like Python and SQL are desk stakes: an applicant who can’t use them may simply be penalized, however competence doesn’t confer any particular distinction.

One shock is that 10% of the respondents stated that they didn’t use any programming languages. We’re unsure what meaning. It’s potential they labored fully in Excel, which needs to be thought of a programming language however typically isn’t. It’s additionally potential that they have been managers or executives who not did any programming.

Salaries by Tool and Platform

We additionally requested respondents what instruments they used for statistics and machine studying and what platforms they used for information analytics and information administration. We noticed among the similar patterns that we noticed with programming languages. And the identical warning applies: respondents have been allowed to pick a number of solutions to our questions concerning the instruments and platforms that they use. (However, a number of solutions weren’t as frequent as for programming languages.) In addition, should you’re accustomed to instruments and platforms for machine studying and statistics, you recognize that the boundary between them is fuzzy. Is Spark a software or a platform? We thought of it a platform, although two Spark libraries are within the record of instruments. What about Kafka? A platform, clearly, however a platform for constructing information pipelines that’s qualitatively completely different from a platform like Ray, Spark, or Hadoop.

Just as with programming languages, we discovered that essentially the most broadly used instruments and platforms have been related to midrange salaries; older instruments, even when they’re nonetheless broadly used, have been related to decrease salaries; and among the instruments and platforms with the fewest customers corresponded to the very best salaries. (See Figure 5 for the total record.)

The commonest responses to the query about instruments for machine studying or statistics have been “I don’t use any tools” (40%) or Excel (31%). Ignoring the query of how one does machine studying or statistics with out instruments, we’ll solely be aware that those that didn’t use instruments had a mean wage of $143,000, and Excel customers had a mean wage of $138,000—each under common. Stata ($120,000) was additionally on the backside of the record; it’s an older package deal with comparatively few customers and is clearly falling out of favor.

The fashionable machine studying packages PyTorch (19% of customers, $166,000 common wage), TensorFlow (20%, $164,000), and scikit-learn (27%, $157,000) occupied the center floor. Those salaries have been above the common for all respondents, which was pulled down by the big numbers who didn’t use instruments or solely used Excel. The highest salaries have been related to H2O (3%, $183,000), KNIME (2%, $180,000), Spark NLP (5%, $179,000), and Spark MLlib (8%, $175,000). It’s laborious to belief conclusions based mostly on 2% or 3% of the respondents, however it seems that salaries are increased for individuals who work with instruments which have lots of “buzz” however aren’t but broadly used. Employers pay a premium for specialised experience.

Figure 5. Average wage by instruments for statistics or machine studying

We see nearly precisely the identical factor after we take a look at information frameworks (Figure 6). Again, the commonest response was from individuals who didn’t use a framework; that group additionally obtained the bottom salaries (30% of customers, $133,000 common wage).

In 2021, Hadoop typically looks as if legacy software program, however 15% of the respondents have been engaged on the Hadoop platform, with a mean wage of $166,000. That was above the common wage for all customers and on the low finish of the midrange for salaries sorted by platform.

The highest salaries have been related to Clicktale (now ContentSquare), a cloud-based analytics system for researching buyer expertise: solely 0.2% of respondents use it, however they’ve a mean wage of $225,000. Other frameworks related to excessive salaries have been Tecton (the industrial model of Michelangelo, at $218,000), Ray ($191,000), and Amundsen ($189,000). These frameworks had comparatively few customers—essentially the most broadly used on this group was Amundsen with 0.8% of respondents (and once more, we warning in opposition to studying an excessive amount of into outcomes based mostly on so few respondents). All of those platforms are comparatively new, often mentioned within the tech press and social media, and look like rising healthily. Kafka, Spark, Google BigQuery, and Dask have been within the center, with lots of customers (15%, 19%, 8%, and 5%) and above-average salaries ($179,000, $172,000, $170,000, and $170,000). Again, the preferred platforms occupied the center of the vary; expertise with much less often used and rising platforms commanded a premium.

Figure 6. Average wage by information framework or platform

Salaries by Industry

The best variety of respondents labored within the software program trade (20% of the overall), adopted by consulting (11%) and healthcare, banking, and training (every at 8%). Relatively few respondents listed themselves as consultants (additionally 2%), although consultancy tends to be cyclic, relying on present pondering on outsourcing, tax regulation, and different components. The common revenue for consultants was $150,000, which is barely barely increased than the common for all respondents ($146,000). That could point out that we’re at present in some form of an equilibrium between consultants and in-house expertise.

While information evaluation has develop into important to each form of enterprise and AI is discovering many purposes exterior of computing, salaries have been highest within the laptop trade itself, as Figure 7 makes clear. For our functions, the “computer industry” was divided into 4 segments: laptop {hardware}, cloud providers and internet hosting, safety, and software program. Average salaries in these industries ranged from $171,000 (for laptop {hardware}) to $164,000 (for software program). Salaries for the promoting trade (together with social media) have been surprisingly low, solely $150,000.

Figure 7. Average wage by trade

Education and nonprofit organizations (together with commerce associations) have been on the backside finish of the dimensions, with compensation simply above $100,000 ($106,000 and $103,000, respectively). Salaries for technical employees in authorities have been barely increased ($124,000).

Salaries by State

When taking a look at information and AI practitioners geographically, there weren’t any huge surprises. The states with essentially the most respondents have been California, New York, Texas, and Massachusetts. California accounted for 19% of the overall, with over double the variety of respondents from New York (8%). To perceive how these 4 states dominate, do not forget that they make up 42% of our respondents however solely 27% of the United States’ inhabitants.

Salaries in California have been the very best, averaging $176,000. The Eastern Seaboard did properly, with a mean wage of $157,000 in Massachusetts (second highest). New York, Delaware, New Jersey, Maryland, and Washington, DC, all reported common salaries within the neighborhood of $150,000 (as did North Dakota, with 5 respondents). The common wage reported for Texas was $148,000, which is barely above the nationwide common however nonetheless appears on the low facet for a state with a big expertise trade.

Salaries within the Pacific Northwest weren’t as excessive as we anticipated. Washington simply barely made it into the highest 10 by way of the variety of respondents, and common salaries in Washington and Oregon have been $138,000 and $133,000, respectively. (See Figure 8 for the total record.)

The highest-paying jobs, with salaries over $300,000, have been concentrated in California (5% of the state’s respondents) and Massachusetts (4%). There have been just a few attention-grabbing outliers: North Dakota and Nevada each had only a few respondents, however every had one respondent making over $300,000. In Nevada, we’re guessing that’s somebody who works for the on line casino trade—in any case, the origins of likelihood and statistics are tied to playing. Most states had no respondents with compensation over $300,000.

Fiigure 8. Average wage by state

The lowest salaries have been, for essentially the most half, from states with the fewest respondents. We’re reluctant to say greater than that. These states sometimes had below 10 respondents, which signifies that averaging salaries is extraordinarily noisy. For instance, Alaska solely had two respondents and a mean wage of $75,000; Mississippi and Louisiana every solely had 5 respondents, and Rhode Island solely had three. In any of those states, one or two extra respondents on the govt stage would have an enormous impact on the states common. Furthermore, the averages in these states are so low that every one (or nearly all) respondents should be college students, interns, or in entry-level positions. So we don’t assume we are able to make any assertion stronger than “the high paying jobs are where you’d expect them to be.”

Job Change by Salary

Despite the variations between states, we discovered that the need to vary jobs based mostly on lack of compensation didn’t rely considerably on geography. There have been outliers at each extremes, however they have been all in states the place the variety of respondents was small and one or two folks seeking to change jobs would make a big distinction. It’s not terribly attention-grabbing to say that 24% of respondents from California intend to vary jobs (solely 2% above the nationwide common); in any case, you’d count on California to dominate. There could also be a small sign from states like New York, with 232 respondents, of whom 27% intend to vary jobs, or from a state like Virginia, with 137 respondents, of whom solely 19% have been pondering of adjusting. But once more, these numbers aren’t a lot completely different from the overall share of potential job changers.

If intent to vary jobs as a consequence of compensation isn’t depending on location, then what does it rely on? Salary. It’s by no means stunning that respondents with the bottom salaries (below $50,000/12 months) are extremely motivated to vary jobs (29%); this group consists largely of scholars, interns, and others who’re beginning their careers. The group that confirmed the second highest want to vary jobs, nonetheless, had the very best salaries: over $400,000/12 months (27%). It’s an attention-grabbing pairing: these with the very best and lowest salaries have been most intent on getting a wage improve.

26% of these with annual salaries between $50,000 and $100,000 indicated that they intend to vary jobs due to compensation. For the rest of the respondents (these with salaries between $100,000 and $400,000), the proportion who intend to vary jobs was 22% or decrease.

Salaries by Certification

Over a 3rd of the respondents (37%) replied that they hadn’t obtained any certifications prior to now 12 months. The subsequent largest group replied “other” (14%), which means that they’d obtained certifications prior to now 12 months however not one of many certifications we listed. We allowed them to write down in their very own responses, and so they shared 352 distinctive solutions, starting from vendor-specific certifications (e.g., DataRobotic) to college levels (e.g., University of Texas) to well-established certifications in any variety of fields (e.g., Certified Information Systems Security Professional a.ok.a. CISSP). While there have been definitely instances the place respondents used completely different phrases to explain the identical factor, the quantity of distinctive write-in responses displays the nice variety of certifications obtainable.

Cloud certifications have been by far the preferred. The prime certification was for AWS (3.9% obtained AWS Certified Solutions Architect-Associate), adopted by Microsoft Azure (3.8% had AZ-900: Microsoft Azure Fundamentals), then two extra AWS certifications and CompTIA’s Security+ certification (1% every). Keep in thoughts that 1% solely represents 27 respondents, and all the opposite certifications had even fewer respondents.

As Figure 9 exhibits, the very best salaries have been related to AWS certifications, the Microsoft AZ-104 (Azure Administrator Associate) certification, and the CISSP safety certification. The common wage for folks itemizing these certifications was increased than the common wage for US respondents as an entire. And the common wage for respondents who wrote in a certification was barely above the common for many who didn’t earn any certifications ($149,000 versus $143,000).

Figure 9. Average wage by certification earned

Certifications have been additionally related to wage will increase (Figure 10). Again AWS and Microsoft Azure dominate, with Microsoft’s AZ-104 main the best way, adopted by three AWS certifications. And on the entire, respondents with certifications seem to have obtained bigger wage will increase than those that didn’t earn any technical certifications.

Figure 10. Average wage change by certification

Google Cloud is an apparent omission from this story. While Google is the third-most-important cloud supplier, solely 26 respondents (roughly 1%) claimed any Google certification, all below the “Other” class.

Among our respondents, safety certifications have been comparatively unusual and didn’t look like related to considerably increased salaries or wage will increase. Cisco’s CCNP was related to increased wage will increase; respondents who earned the CompTIA Security+ or CISSP certifications obtained smaller will increase. Does this replicate that administration undervalues safety coaching? If this speculation is right, undervaluing safety is clearly a big mistake, given the continuing significance of safety and the potential for new assaults in opposition to AI and different data-driven techniques.

Cloud certifications clearly had the best impact on wage will increase. With only a few exceptions, any certification was higher than no certification: respondents who wrote in a certification below “Other” averaged a $9,600 wage improve over the previous couple of years, versus $8,900 for respondents who didn’t acquire a certification and $9,300 for all respondents no matter certification.


Participating in coaching resulted in wage will increase—however solely for many who spent greater than 100 hours in a coaching program. As Figure 11 exhibits, these respondents had a mean wage improve of $11,000. This was additionally the biggest group of respondents (19%). Respondents who solely reported enterprise 1–19 hours of coaching (8%) noticed decrease wage will increase, with a mean of $7,100. It’s attention-grabbing that those that participated in 1–19 hours of coaching noticed smaller will increase than those that didn’t take part in coaching in any respect. It doesn’t make sense to invest about this distinction, however the information does make one factor clear: should you interact in coaching, be critical about it.

Figure 11. Average wage change vs. hours of coaching

We additionally requested what kinds of coaching respondents engaged in: whether or not it was firm supplied (for which there have been three options), a certification program, a convention, or another form of coaching (detailed in Figure 12). Respondents who took benefit of company-provided alternatives had the very best common salaries ($156,000, $150,000, and $149,000). Those who obtained certifications have been subsequent ($148,000). The outcomes are related if we take a look at wage will increase over the previous three years: Those who participated in numerous types of company-offered coaching obtained will increase between $11,000 and $10,000. Salary will increase for respondents who obtained a certification have been in the identical vary ($11,000).

Figure 12. Average wage change vs. kind of coaching

The Last Word

Data and AI professionals—a rubric below which we embody information scientists, information engineers, and specialists in AI and ML—are well-paid, reporting a mean wage slightly below $150,000. However, there have been sharp state-by-state variations: salaries have been considerably increased in California, although the Northeast (with some exceptions) did properly.

There have been additionally vital variations between salaries for women and men. Men’s salaries have been increased no matter job title, no matter coaching and no matter educational levels—despite the fact that ladies have been extra more likely to have a sophisticated educational diploma (PhD or grasp’s diploma) than have been males.

We don’t see proof of a “great resignation.” Job turnover by way of the pandemic was roughly what we’d count on (maybe barely under regular). Respondents did look like involved about job safety, although they didn’t need to admit it explicitly. But except for the least- and most-highly compensated respondents, the intent to vary jobs due to wage was surprisingly constant and nothing to be alarmed at.

Training was essential, partly as a result of it was related to hireability and job safety however extra as a result of respondents have been genuinely involved in studying new expertise and bettering present ones. Cloud coaching, notably in AWS and Microsoft Azure, was essentially the most strongly related to increased wage will increase.

But maybe we must always depart the final phrase to our respondents. The closing query in our survey requested what areas of expertise would have the most important impact on wage and promotions within the coming 12 months. It wasn’t a shock that many of the respondents stated machine studying (63%)—lately, ML is the most well liked matter within the information world. It was extra of a shock that “programming languages” was famous by simply 34% of respondents. (Only “Other” obtained fewer responses—see Figure 13 for full particulars.) Our respondents clearly aren’t impressed by programming languages, despite the fact that the information means that employers are prepared to pay a premium for Rust, Go, and Scala.

There’s one other sign value taking note of if we glance past the extremes. Data instruments, cloud and containers, and automation have been almost tied (46, 47, and 44%). The cloud and containers class consists of instruments like Docker and Kubernetes, cloud suppliers like AWS and Microsoft Azure, and disciplines like MLOps. The instruments class consists of instruments for constructing and sustaining information pipelines, like Kafka. “Automation” can imply lots of issues however on this context most likely means automated coaching and deployment.

Figure 13. What applied sciences could have the most important impact on compensation within the coming 12 months?

We’ve argued for a while that operations—efficiently deploying and managing purposes in manufacturing—is the most important concern going through ML practitioners within the coming years. If you need to keep on prime of what’s occurring in information, and if you wish to maximize your job safety, hireability, and wage, don’t simply learn to construct AI fashions; learn to deploy purposes that reside within the cloud.

In the basic film The Graduate, one character famously says, “There’s a great future in plastics. Think about it.” In 2021, and with out being anyplace close to as repulsive, we’d say, “There’s a great future in the cloud. Think about it.”

#Note-Author Name – Mike Loukides

Related Articles


Please enter your comment!
Please enter your name here

Stay Connected

- Advertisement -spot_img

Latest Articles